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Disparities in receipt of recommended care among younger versus older medicare beneficiaries: a cohort study

  • Ling Na1,
  • Joel E. Streim2,
  • Liliana E. Pezzin3, 4,
  • Jibby E. Kurichi1,
  • Dawei Xie1,
  • Hillary R. Bogner1,
  • Pui L. Kwong1,
  • Steven M. Asch5 and
  • Sean Hennessy1, 6Email author
BMC Health Services ResearchBMC series – open, inclusive and trusted201717:241

https://doi.org/10.1186/s12913-017-2168-5

Received: 17 August 2016

Accepted: 16 March 2017

Published: 29 March 2017

Abstract

Background

Although health disparities have been documented between Medicare beneficiaries based on age (<65 years vs. older age groups), underuse of recommended medical care in younger beneficiaries has not been thoroughly investigated. In this study, we aim to identify and characterize vulnerabilities of the younger Medicare age group (aged <65 years) in relation to older age groups (aged 65–74 years and ≥75 years) and to explore age group as a determinant of use of recommended care among Medicare beneficiaries.

Methods

We conducted a cohort study of community-dwelling Medicare beneficiaries who participated in the Medicare Current Beneficiary Survey between 2001 and 2008 (N = 30,117). Age group characteristics were compared using cross-sectional data at baseline. During follow-up, we assessed the association between age and receipt of recommended care on 38 recommended care indicators, adjusting for sociodemographic and clinical characteristics. Follow-up periods differed by component indicator.

Results

At baseline, a higher proportion of younger beneficiaries experienced social disadvantage, disability and certain morbidities than older age groups. During follow-up, younger beneficiaries were significantly less likely to receive overall recommended care compared to those 65–74 years of age (adjusted odds ratio and 95% confidence interval: 0.75, 0.70–0.80). In addition, male gender, non-Hispanic black race, less than high school education, living alone, with children or with others, psychiatric disorders and higher activity limitation stages were all associated with underuse of recommended care.

Conclusions

Younger Medicare beneficiary status appears to be an independent risk factor for underuse of appropriate care. Support to ameliorate disparities in different social and health aspects may be warranted.

Keywords

MedicareYounger beneficiariesHealth disparityRecommended careQuality of care

Background

The Healthy People 2020 initiative seeks to eliminate health disparities and improve the health of all groups in the US [1]. A distinct group that suffers multiple health disparities, yet has not been investigated thoroughly, is Medicare beneficiaries under 65 years of age. Younger Medicare beneficiaries face major social disadvantages and a disproportionately high burden of disabilities and medical morbidities. Unlike those who are eligible for Medicare solely due to being 65 years of age and older, younger enrollees must have received Social Security disability benefits for 24 months or have either amyotrophic lateral sclerosis or end-stage renal disease [2]. Younger Medicare beneficiaries are more likely to be male, non-white, economically and educationally disadvantaged, to be in fair or poor health, and to have a higher prevalence of disabilities and mental health disorders [36]. In 2012, younger beneficiaries constituted 17% of the 50.8 million Medicare enrollees, but triggered 20% of total Medicare expenditures [7]. Despite these higher expenditures, they underutilized preventive health services including influenza vaccine, eye and dental exams, mammograms, and prostate exams [4].

Braveman’s health disparity framework lays the ground for our analysis of younger Medicare beneficiaries [8]. A health disparity is a population-specific, potentially avoidable difference in health or important influences on health that is systematically associated with socially disadvantaged groups [8], such as the impoverished, racial minorities and individuals with disabilities. An important way to eliminate health disparities is through equitable health care, defined as equally accessible care to all users, and greater provision of care to users who demonstrate greater need [810]. In Braveman’s framework, a health disparity should be assessed by comparing groups in a social hierarchy in relation to each other [11], because such comparisons help policy makers identify vulnerable social groups, target interventions and reallocate resources to achieve greater health equity. Factors associated with health disparities include minority race [12, 13], lower income and less education [14], and disability [1517]. Often these vulnerabilities, as well as rural location and reduced physician supply, are also associated with poor quality of care [1825]. Although it is expected that younger beneficiary status is associated with health disparity due to Medicare enrollment criteria, younger beneficiaries demonstrated largely unmet health care needs.

However, younger beneficiaries are often excluded from studies of Medicare beneficiaries. The few pioneering studies comparing younger versus older beneficiaries highlighted the importance of the topic, although they tend to have several limitations [46]: self-reported health service utilization is subject to recall bias; types of services are often limited to preventive care; and crude associations without risk adjustment are not particularly useful for policy planning. To better capture underuse of care in the younger population, we employed claims data, a variety of indicators and risk-adjusted models. Furthermore, three main characteristics of younger beneficiaries (greater comorbidity, disability and socioeconomic disadvantages) do not always affect quality of care in the same direction. Multimorbid patients tend to get higher quality of care [26], disability has mixed quality [24]; minority race and lower income, while also having mixed quality, tend to predict worse care [21, 27]. Comparing younger with older Medicare beneficiaries can shed light on the direction and magnitude of these relationships, and their synergies and dys-synergies as they co-occur in younger beneficiary population. The comparison is important as a policy evaluation issue: is the Medicare program failing its younger beneficiaries?

We sought to identify predictors of underuse of recommended care by applying Asch’s underuse indicator system to recent Medicare claims of health service utilization [21]. Asch’s underuse indicator system is a clinically valid, comprehensive and claim-based measurement tool, which examines highly prevalent conditions and preventive care. These indicators have been validated on both inpatient, outpatient and physician service claims data for Medicare beneficiaries 65 years of age and older [21, 24], but not younger beneficiaries. Therefore, we aimed to characterize vulnerabilities of the younger Medicare age group and then explore age group as a determinant of use of recommended care among Medicare enrollees. We assess the extent to which the earlier findings of disparities in sociodemographic and health characteristics hold in younger beneficiaries in our data. We further test our hypothesis that compared to older beneficiaries, younger beneficiaries are less likely to receive recommended care after accounting for sociodemographic characteristics, degree of comorbidity and activity limitation.

Methods

Study sample

We analyzed data from a nationally representative sample of the Medicare population, the Medicare Current Beneficiary Survey (MCBS) [28, 29]. The MCBS is a longitudinal panel survey that contains individual-level information of sociodemographics, health care encounters and health and physical functioning. Survey participants are typically interviewed three times per year for 4 years with health and functioning assessed in the fall of each year. The sample is replenished annually with newly enrolled beneficiaries replacing those who died or exited the survey. Survey data are linked to Medicare claims data that are available for 3 years after the initial survey. The MCBS uses multistage sampling design, with weights, strata and cluster information available. MCBS oversamples beneficiaries aged 85 years and older and those aged 65 years and younger. One study reported that the initial response rate of MCBS was 82.6%, similar to other national surveys [29]. The response rates were 82–83% across different age categories. The magnitude of potential bias due to non-response was reduced by non-response adjustment provided in the survey [29]. Our study included community-dwelling Medicare beneficiaries who enrolled in the MCBS between 2001 and 2008. The entry panels of 2001–2007 were followed for 3 years, and panel 2008 was followed for 2 years because claims data beyond 2010 were not available.

The study was approved by the University of Pennsylvania Institutional Review Board.

Receipt of recommended care

To assess receipt of recommended care, we adapted the indicator system measuring underuse of necessary care that was developed by Asch and colleagues [21] and later modified by Chan [24]. The original indicators span several domains of care: initial evaluation, diagnostic tests, therapeutic interventions, hospitalization follow-up, monitoring of routine care and avoidable outcomes. The indicator system was tested and validated on 1992–1993 Medicare claims and was applied to 1994–1996 claims data [21]. After excluding six avoidable outcome indicators (because we wished to focus on process measures) and three indicators with inadequate sample size, we adapted 38 indicators or process measures, of recommended care to our study. Three of these 38 indicators measured receipt of preventive care: a physician annual visit, a biennial visual impairment assessment, and a biennial mammography for women aged between 45 and 75 years. The remaining 35 indicators examined care for acute and chronic conditions, including acute myocardial infarction, anemia, angina, breast cancer, cerebrovascular accident (CVA), transient ischemic attack (TIA), cholelithiasis, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), depression, diabetes, gastrointestinal bleeding and hypertension.

Each indicator specified which beneficiaries were eligible (i.e., had an opportunity) for its assessment, the care that should be received, and a recommended time interval. Receipt of recommended care was coded as present if claims data indicated delivery of care within the recommended time frame, and absent otherwise. Receipt of care was assessed at the opportunity level; thus a beneficiary might have multiple opportunities for recommended care. Opportunities were not eligible for indicator assessment if they had incomplete follow-up time due to death or loss to follow-up, disenrollment in Part A and/or part B, or enrollment in a managed care program during the assessment period. For indicators with short assessment periods (2–4 weeks), subjects were excluded if there was a hospitalization or ER visit during the follow-up period.

Age groups

Our main interest was Medicare beneficiaries younger than age 65. Recognizing the potential heterogeneity of older beneficiaries in their health status and health care quality [5], we classified them as younger old (65–74 years) and older old (75 years and older).

Sociodemographic and clinical characteristics

Sociodemographics and clinical characteristics were assessed based on self- or proxy-report in the surveys. Sociodemographics included sex, race (non-Hispanic white, non-Hispanic black, Hispanic or other), education (less than high school education or high school diploma and above), dual enrollment in Medicare and Medicaid, living arrangement (alone, with spouse, with children, with others or in a retirement community), and residential location (metropolitan or non-metropolitan area). Health and clinical characteristics were self-reported and included number of comorbidities (hypertension, myocardial infarction, angina/chronic heart disease, other heart disease, stroke, diabetes mellitus, Parkinson’s disease, emphysema/asthma/chronic obstructive pulmonary disease, rheumatoid arthritis, non-rheumatoid arthritis, osteoporosis/soft bones and cancers other than skin), presence of a developmental, psychiatric or cognitive disorder (mental retardation, Alzheimer’s/dementia or mental/psychiatric disorders), vision impairment, and hearing impairment. In addition, we included an indicator of proxy versus self-response to the survey. We chose not to use specific conditions or comorbidity indices based on claims ICD-9 codes because the assessment periods of these indices partially overlap with indicator-level follow-up periods, instead of preceding follow-up periods.

Activity limitation stages

Activity limitation stages based on the International Classification of Functioning Disability and Health (ICF) [30] in separate activity of daily living (ADL) and instrumental activity of daily living (IADL) domains were derived from survey data for each respondent. ADL stages include the self-care functions of eating, toileting, dressing, bathing or showering, getting in/out of bed or chairs and walking. IADL stages incorporate the domestic life functions of telephoning, managing money, preparing meals, doing light housework, shopping for personal items and doing heavy housework. Five ADL stages (0–IV) and five IADL stages (0–IV) present a combination of severity and types of disability (Appendix). Stage III was designed as a non-fitting stage to characterize unusual limitation patterns. Methods for ascertaining stage are documented elsewhere [31, 32].

Statistical analysis

Chi-square tests were used to assess differences in baseline characteristics among the three age groups. Pairwise chi-square tests were applied to statistically significant between-group differences, with the younger and older old compared to the younger old. Receipt of recommended care was expressed as a percent by dividing the number of instances of recommended care received by the number of opportunities. We calculated the weighted percent of receipt of overall (collapsed across the 38 indicators) and indicator-specific recommended care for all age groups combined and for each age group separately. The association between age group and receipt of overall recommended care was assessed first in an unadjusted logistic regression model, and subsequently in multivariable logistic regression. Separate adjusted models were fit for ADL and IADL stages because collinearity precludes including both domains in a single model. Age group and covariates including sex, race and education were assessed at baseline, and other covariates that may vary over time were assessed in the survey cycle immediately preceding indicator the follow-up date. The model applied survey sampling weights and accounted for the complex sampling design and non-independence of multiple eligible indicators for the same individual. Analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC).

Results

Sample characteristics

The distribution of the baseline sample (N = 30,117) by age group was 16% were younger than age 65 years, 48% aged 65–74 years, and 36% aged 75 years and older. Table 1 lists baseline characteristics by age group. The most striking sociodemographic differences among the age groups were in race/ethnicity, living arrangement and dual enrollment. Compared to the older groups, younger beneficiaries were more likely to be non-Hispanic black (19% vs. 9% and 7%) and Hispanic (11% vs. 8% and 6%), to live with others (24% vs. 5% and 4%), and to be dually-enrolled in Medicaid (44% vs. 11% and 12%).
Table 1

Sociodemographic, functional and clinical characteristics of medicare beneficiaries (2001–2008) by age group

Variable

Total

N = 30,117

Age < 65

N (column weighted %)

5201 (16.3)

Age 65–74

N (column weighted %)

11,289 (47.5)

Age ≥ 75

N (column weighted %)

13,627 (36.2)

p-value

Gender

<.0001

 Male

13,649 (45.2)

2853 (52.6)

5360 (46.3)

5436 (40.3)

 

 Female

16,468 (54.8)

2348 (47.4)

5929 (53.7)

8191 (59.7)

 

Race/Ethnicity

<.0001

 Non-Hispanic White

23,893 (79.2)

3459 (67.3)

9017 (79.9)

11,417 (83.7)

 

 Non-Hispanic Black

2966 (9.7)

1007 (18.6)

1007 (8.7)

952 (7.0)

 

 Hispanic

2372 (7.9)

580 (11.2)

913 (8.0)

879 (6.3)

 

 Other

886 (3.2)

155 (2.9)

352 (3.4)

379 (2.9)

 

Living arrangement

<.0001

 Retirement community

1905 (5.6)

101 (2.2)

430 (3.6)

1374 (9.7)

 

 With spouse

14,124 (51.2)

1649 (39.1)

7004 (62.6)

5471 (41.6)

 

 With children

3158 (9.5)

658 (11.4)

802 (7.0)

1698 (12.1)

 

 With others

2762 (7.8)

1597 (23.6)

589 (5.2)

576 (4.2)

 

 Alone

8168 (25.9)

1196 (23.8)

2464 (21.6)

4508 (32.5)

 

Dual enrollment in Medicare and Medicaid

<.0001

 No

24,292 (83.5)

2409 (56.4)

9984 (89.4)

11,899 (87.8)

 

 Yes

5825 (16.5)

2792 (43.6)

1305 (10.6)

1728 (12.2)

 

Education

<.0001

 High school diploma or above

21,252 (72.8)

3527 (69.1)

8543 (77.4)

9182 (68.5)

 

 No high school diploma

8865 (27.2)

1674 (30.9)

2746 (22.6)

4445 (31.5)

 

Living in metropolitan area

<.0001

 No

7942 (25.0)

1598 (29.0)

3092 (25.1)

3252 (23.1)

 

 Yes

22,175 (75.0)

3603 (71.0)

8197 (74.9)

10,375 (76.9)

 

Proxy report

<.0001

 No

27,492 (92.8)

4277 (87.2)

10,757 (95.4)

12,458 (91.9)

 

 Yes

2625 (7.2)

924 (12.8)

532 (4.6)

1169 (8.1)

 

Vision impairment

<.0001

 No

27,674 (92.7)

4621 (87.9)

10,752 (95.8)

12,301 (90.8)

 

 Yes

2443 (7.3)

580 (12.1)

537 (4.2)

1326 (9.2)

 

Hearing impairment

<.0001

 No

27,934 (93.5)

4890 (93.8)

10,745 (95.5)

12,299 (90.8)

 

 Yes

2183 (6.5)

311 (6.2)

544 (4.5)

1328 (9.2)

 

Cognitive, developmental and psychiatric disordersa

<.0001

 No

25,662 (87.3)

2766 (60.9)

10,442 (93.0)

12,454 (91.8)

 

 Yes

4455 (12.7)

2435 (39.1)

847 (7.0)

1173 (8.2)

 

Average number of comorbiditiesb

2.2 ± 0

2.3 ± 0

2.1 ± 0

2.4 ± 0

<.0001

Activity of Daily Living (ADL) Stages

<.0001

 0

19,874 (68.3)

2599 (45.4)

8846 (79.9)

8429 (63.5)

 

 I

5181 (16.5)

1156 (25.2)

1398 (11.8)

2627 (18.9)

 

 II

2622 (7.9)

656 (13.7)

568 (4.5)

1398 (9.6)

 

 III

2047 (6.2)

656 (13.5)

417 (3.3)

974 (6.7)

 

 IV

393 (1.1)

134 (2.3)

60 (0.5)

199 (1.3)

 

Instrumental Activity of Daily Living (IADL) stages

<.0001

 0

16,911 (59.4)

1339 (23.8)

8169 (74.0)

7403 (56.2)

 

 I

5332 (17.6)

1063 (24.9)

1670 (14.2)

2599 (18.8)

 

 II

2979 (9.5)

1046 (22.2)

675 (5.6)

1258 (8.9)

 

 III

4089 (11.4)

1500 (25.0)

665 (5.3)

1924 (13.2)

 

 IV

806 (2.1)

253 (4.0)

110 (0.8)

443 (2.9)

 

aCognitive, developmental, and psychiatric disorders include: mental retardation, Alzheimer’s/dementia and mental/psychiatric disorder

bNumber of comorbidities including: hypertension, myocardial infarction, angina/chronic heart disease, other heart disease, stroke, diabetes mellitus, Parkinson’s disease, emphysema/asthma/chronic obstructive pulmonary disease, rheumatoid arthritis, non-rheumatoid arthritis, osteoporosis/soft bones and other (non-skin) cancer

Younger beneficiaries carried a disproportionate burden of developmental, cognitive and psychiatric disorders (39% vs. 7% and 8%). They were significantly less likely to be functionally independent in ADLs (stage 0) compared to the other two older age groups (45% vs. 80% and 64%). Differences in IADL stages were even more striking: only 24% of younger beneficiaries were IADL independent (stage 0) compared to 74% of the younger old and 56% of the older old. They relied more heavily on proxy responses to survey questions and were more likely to be visually impaired.

Receipt of recommended care by age group across all indicators

In total 20,449 unique beneficiaries were eligible for at least one opportunity for recommended care, including 3756 younger, 7180 younger old and 9513 older old beneficiaries. These beneficiaries triggered 89,076 opportunities for care, with 14,015 for younger beneficiaries, 32,372 opportunities for the younger old, and 42,689 for the older old. As shown in Table 2, eligible younger beneficiaries received recommended care in 64% of the opportunities, in contrast to 73% for the younger old and 75% for the older old.
Table 2

Receipt of recommended care among medicare beneficiaries (2001–2008) by age group at the indicator level

 

Overall

Age < 65

Age 65–74

Age ≥75

Total number of opportunities for recommended care (unweighted denominator)

89,076

14,015

32,372

42,689

Total number of instances of recommended care received (unweighted numerator)

64,157

8702

23,582

31,873

Weighted percent of recommended care received

72.1%

63.9%

72.7%

74.8%

Receipt of recommended care by age group by indicator

Table 3 presents the weighted percent of receiving recommended care by age group for each indicator. The Centers for Medicare and Medicaid Services (CMS) prohibits publishing cell size below 11, yielding 30 eligible indicators for comparison, 14 of which had a statistically significant difference (p < .05) in receipt of recommended care by age group, shown in Fig. 1. Among these 14 indicators, pair-wise chi-square tests showed younger beneficiaries underused care on 10 indicators when compared to the younger old, and the older old group underutilized care on 5 indicators. Younger beneficiaries outperformed younger old for 1 indicator, while the older old did so for 4 indicators. Notably, younger beneficiaries were less likely than the younger old to have a follow-up visit within 4 weeks following hospital discharge for CVA, TIA and gastrointestinal (GI) bleed, to obtain a hematocrit within 4 weeks following an initial diagnosis of GI bleed, to receive routine care for diabetes (a glycosylated hemoglobin every 6 months, an annual eye exam and a doctor visit every 6 months), and preventive care in general (an annual physician visit, a biennial mammogram and a biennial assessment of visual impairment).
Table 3

Receipt of recommended care by indicator among medicare beneficiaries (2001–2008) by age group

Recommended care indicator

Overall

N = 30,117

Age < 65

5201 (16.3%)

Age 65–74

11,289 (47.5%)

Age ≥ 75

13,627 (36.2%)

P-value for difference among age groups

Raw numerator/denominator

Weighted percent (%)

Weighted percent (%)

Weighted percent (%)

Weighted percent (%)

Acute Myocardial Infarction (AMI)

 Visit within 4 weeks following discharge of patients hospitalized for acute myocardial infarction

231/298

79

84

79

78

0.748

 Cholesterol test every 6 months for patients hospitalized for myocardial infarction who have hypercholesterolemia

224/365

64

71

69

58

0.188

Anemia

 Gastrointestinal workup for patients with iron deficiency anemia no later than 3 months after iron deficiency

355/1112

33

34

37

30

0.273

 Hematocrit/hemoglobin between 1 and 6 months following initial diagnosis of anemia

1723/2576

68

67

67

69

0.633

Angina

 Visit within 4 weeks following discharge of patients hospitalized for unstable angina

193/234

83

76

83

86

0.407

 Visit every 6 months for patients with chronic stable angina

1826/1940

94

92

93

96

0.135

 Follow-up visit or hospitalization within 4 weeks of initial diagnosis of unstable angina

196/236

84

77

82

89

0.150

 Lipid profile within 6 months after initial diagnosis of angina

59/767

9

X

14

4

0.0003

Breast Cancer

 Interval from biopsy and definitive therapy less than 3 months for patients with breast cancer and eventual mastectomy

60/79

73

X

70

81

0.273

 Mammogram within 3 months preceding an initial diagnosis of breast cancer

110/182

61

X

60

63

0.917

 Chest x-ray within 3 months preceding or following initial diagnosis of breast cancer

96/182

51

43

54

51

0.631

 Visit within 12 months for breast cancer patients who have undergone mastectomy without cytotoxic chemotherapy

71/71

100

X

100

100

N/A

 Mammography every year for patients with a history of breast cancer

416/629

69

70

78

61

0.0004

Cerebrovascular Accident (CVA)

 Carotid imaging within 2 weeks of initial diagnosis for patients hospitalized for carotid artery stroke

235/312

75

95

68

75

<.0001

 Interval between carotid imaging and carotid endarterectomy less than 2 months for cerebrovascular accident patients with eventual carotid endarterectomy

112/134

84

X

87

83

0.501

 Visit within 4 weeks following discharge of patients for cerebrovascular accident

379/571

67

57

75

64

0.011

Transient Ischemic Attack (TIA)

 Electrocardiogram within 2 days of initial diagnosis of transient ischemic attack

92/621

15

X

16

14

0.748

 Interval between carotid imaging and carotid endarterectomy less than 2 months for TIA patients with eventual carotid endarterectomy

45/54

85

X

91

82

0.012

 Visit within 4 weeks following discharge of patients hospitalized for transient ischemic attack

184/237

79

61

95

74

<.0001

 Visit every year for patients with diagnosis of transient ischemic attack

1540/1596

97

96

97

96

0.740

Cholelithiasis

 Cholecystectomy within 1 month preceding or 3 months following diagnosis of cholelithiasis and one or more of the following: cholecystitis, cholangitis, gallstone pancreatitis

282/699

41

43

48

34

0.030

Chronic Obstructive Pulmonary Disease (COPD)

 Visit every 6 months for patients with chronic obstructive pulmonary disease

4732/5197

91

90

91

92

0.236

Congestive Heart Failure (CHF)

 Chest x-ray within 3 months of initial diagnosis of congestive heart failure

1097/1580

69

72

64

71

0.067

 Electrocardiogram within 3 months of initial diagnosis of congestive heart failure

1023/1578

66

67

66

66

0.953

 Visit within 4 weeks following discharge of patients hospitalized for congestive heart failure

490/663

74

71

82

70

0.032

 Visit every 6 months for patients with congestive heart failure

4142/4527

92

91

93

91

0.201

Depression

 Visit within 2 weeks following discharge of patients hospitalized for depression

95/173

53

49

55

57

0.593

Diabetes Mellitus (DM)

 Glycosylated hemoglobin every 6 months for patients with diabetes

3499/6756

54

52

58

50

<.0001

 Eye exam every year for patients with diabetes

3160/6491

49

34

50

54

<.0001

 Visit within 4 weeks following discharge of patients hospitalized for diabetes

295/430

68

71

63

70

0.466

 Visit every 6 months for patients with diabetes

6185/6756

92

89

92

92

0.036

Gastrointestinal Bleeding

 Visit within 4 weeks following discharge of patients hospitalized for gastrointestinal bleeding

273/373

73

51

74

78

0.001

 Hematocrit within 4 weeks following discharge of patients hospitalized for gastrointestinal bleeding

201/373

54

36

57

58

0.025

 Follow-up visit within 4 weeks of initial diagnosis of gastrointestinal bleeding

491/676

74

74

77

69

0.195

Hypertension

 Visit within 4 weeks following discharge of patients hospitalized with malignant or otherwise severe hypertension

49/74

63

X

62

76

0.0002

Preventive Care

 Visit every year

17,905/19,535

92

87

91

94

<.0001

 Assessment of visual impairment every 2 years

9363/16,759

56

34

57

64

<.0001

 Mammography every 2 years for females aged between 45 and 75 (inclusive) years

2728/4240

65

58

67

61

<.0001

Note. According to the Centers for Medicare and Medicaid Services, cell size below 11, marked with an X, is not permitted for publication

Fig. 1

Disparities in receipt of recommended care among younger versus older age groups (<65, 65–74, ≥75)

Compared to the younger old, the older old beneficiaries were less likely to receive follow-up care for CHF, TIA and CVA after hospital discharge. However, the older old were more likely to attend an annual doctor visit, to have a biennial eye exam, and to receive eye exam for diabetes.

Factors associated with receipt of recommended care

Table 4 displays the association between age group and receipt of recommended care in a bivariate logistic regression model and multivariable logistic regression models that included ADL and IADL stages separately. In the unadjusted model, the odds of receiving overall recommended care was 34% lower among younger beneficiaries, but 11% higher among older old beneficiaries, each compared to the younger old.
Table 4

Logistic Regression Models Predicting Receipt of Recommended Care among Medicare Beneficiaries (2001–2008)

Variables

Model 1

Model 2 with ADL stages

Model 2 with IADL stages

 

OR (95% CI)

p-value

OR (95% CI)

p-value

OR (95% CI)

p-value

Age (ref: 65–74)

 

<.0001

 

<.0001

 

<.0001

 <65

0.66 (0.62–0.71)

<.0001

0.75 (0.70–0.80)

<.0001

0.75 (0.69–0.80)

<.0001

 ≥75

1.11 (1.07–1.16)

<.0001

1.15 (1.10–1.20)

<.0001

1.15 (1.10–1.20)

<.0001

Gender (ref: female)

 Male

  

0.86 (0.82–0.90)

<.0001

0.86 (0.82–0.90)

<.0001

Race/Ethnicity (ref: Non-Hispanic White)

   

0.005

 

0.005

 Hispanic

  

0.95 (0.87–1.05)

0.337

0.95 (0.86–1.04)

0.277

 Non-Hispanic Black

  

0.88 (0.82–0.95)

0.0004

0.88 (0.82–0.95)

0.0005

 Other

  

0.96 (0.86–1.08)

0.527

0.96 (0.85–1.08)

0.464

Education (ref: high school diploma)

 No high school diploma

  

0.85 (0.81–0.89)

<.0001

0.85 (0.81–0.89)

<.0001

Living Arrangement (ref: live with spouse)

   

<.0001

 

<.0001

 Alone

  

0.88 (0.83–0.92)

<.0001

0.87 (0.83–0.92)

<.0001

 Retirement community

  

0.95 (0.87–1.03)

0.199

0.95 (0.87–1.03)

0.188

 With children

  

0.77 (0.72–0.83)

<.0001

0.77 (0.72–0.83)

<.0001

 With others

  

0.82 (0.75–0.89)

<.0001

0.83 (0.76–0.90)

<.0001

Residential Location (ref: Non-Metropolitan location)

 Metropolitan location

  

1.14 (1.09–1.18)

<.0001

1.13 (1.08–1.18)

<.0001

Dual Enrollment in Medicare and Medicaid (ref: Medicare only)

 Dual enrollment

  

1.06 (1.00–1.13)

0.056

1.06 (1.00–1.13)

0.072

Proxy Response (ref: no)

 Proxy

  

0.87 (0.81–0.93)

<.0001

0.90 (0.84–0.96)

0.003

Conditions (ref: no)

 Vision impairment

  

1.01 (0.94–1.09)

0.731

1.02 (0.94–1.10)

0.694

 Hearing impairment

  

0.95 (0.88–1.02)

0.162

0.96 (0.89–1.03)

0.242

 Cognitive, developmental, and psychiatric disorders*

  

0.89 (0.83–0.94)

<.0001

0.90 (0.84–0.96)

<.0001

Sum of comorbidities**

  

1.12 (1.11–1.14)

<.0001

1.12 (1.10–1.13)

0.001

Stage (ref: Stage 0)

   

<.0001

 

<.0001

 Stage I

  

0.92 (0.88–0.97)

0.003

0.99 (0.94–1.05)

0.763

 Stage II

  

0.87 (0.81–0.93)

<.0001

0.89 (0.84–0.96)

0.001

 Stage III

  

0.80 (0.74–0.87)

<.0001

0.87 (0.81–0.93)

<.0001

 Stage IV

  

0.64 (0.54–0.76)

<.0001

0.69 (0.61–0.78)

<.0001

Note: Ref=reference category. For a variable that has more than two categories, a total p value of the variable is reported

Model 1 is adjusted only for age group; model 2’s are further adjusted for sociodemographics, health and clinical characteristics and ADL stages and IADL stages separately

* Cognitive, developmental, and psychiatric disorders include: mental retardation, Alzheimer's/dementia, and mental/psychiatric disorder

** Sum of comorbidities include: hypertension, myocardial infarction, angina/chronic heart disease, other heart disease, stroke, diabetes mellitus, Parkinson's disease, emphysema/asthma/chronic obstructive pulmonary disease, rheumatoid arthritis, non-rheumatoid arthritis, osteoporosis/soft bones, and other (non-skin) cancer

Model estimates for separate stage systems were similar (Table 4), after excluding less than 2% of missing cases. In the multivariable model adjusted for ADL stages, the association (OR) between younger age and receipt of recommended care was attenuated to 0.75. Male gender, black race, less than high school education, living alone, with children or with others (each compared to living with spouse), proxy response and having developmental, cognitive or psychiatric disorders were all independently associated with underuse of recommended care. Living in a metropolitan area and a greater number of comorbidities were associated with appropriate care. Both ADL and IADL stages showed ordered associations with receipt of recommended care. Compared to no ADL limitations (stage 0), the likelihood of receiving recommended care declined with higher ADL stages, with ORs (95% CIs) across stages I–IV at 0.92 (0.88–0.97), 0.87 (0.81–0.93), 0.80 (0.74–0.87) and 0.64 (0.54–0.76), respectively. A similar pattern held for IADL stages.

Discussion

Research on the appropriate use of health services by younger Medicare beneficiaries remains quite limited [3]. In this nationally representative study of community dwelling Medicare beneficiaries, we found that those younger than 65 compared to those 65–74 years of age had a higher proportion of characteristics conventionally associated with social disadvantage. Such characteristics include being non-Hispanic Black, living with disabilities, lower educational achievement and non-metropolitan residency. Even after adjusting for these factors and further adjusting for dual enrollment in Medicare and Medicaid, cognitive, developmental or psychiatric disorders and vision impairment, we found substantially reduced use of recommended care by younger Medicare beneficiaries. In contrast, the older old group was slightly more likely than the younger old to receive recommended care.

Our results are consistent with previous reports on younger beneficiaries with respect to the proportion of those who were non-Hispanic black, who were eligible for Medicare and Medicaid [5, 33] and who self-reported to have cognitive, developmental or psychiatric disorders [6]. Younger beneficiaries demonstrated a higher prevalence of self- or proxy- reported dependencies in ADLs and IADLs in our study than previously reported [34]. The results suggest that activity limitations of younger Medicare beneficiaries have not improved over time, supporting need for interventions.

Although it has been reported that younger Medicare beneficiaries significantly underuse preventive care compared to older beneficiaries [4], our study was able to quantify the extent of such deficits. We found the most striking deficiencies across the three prevention indices, routine care for diabetes and post-discharge follow-up for CVA and TIA. Inadequate care, particularly for chronic conditions, suggests that the current service delivery model that centers on acute illness [35] does not meet current needs for prevention and chronic conditions. The reorientation of Medicare to the management of chronic illness and the amelioration of activity limitation could improve the care and reduce costs for chronically ill beneficiaries [36]. Appropriate use of preventive services, medication management and behavioral interventions have been proposed as promising strategies for reducing severity of chronic conditions and their complications [3].

Younger beneficiary status was an independent predictor of underuse in the adjusted model, possibly due to the operation of unknown factors influencing underuse in this population, such as infrequent contact with the health system, especially outpatient services. A post-hoc analysis revealed that among beneficiaries with a cognitive, developmental or psychiatric disorder, the three age groups made similar numbers of outpatient visits (Median = 2.2, 2.1 and 1.9 respectively); in contrast, among beneficiaries without those disorders, younger beneficiaries visited a doctor more often than the younger old and older old beneficiaries (Median = 2.1 vs. 1.0 and 1.5). The excess office visits made by younger beneficiaries were likely due to Medicare eligible conditions other than cognitive, developmental or psychiatric disorders. These findings suggest that a greater number of office visits does not necessarily translate into adequate care for younger beneficiaries. One explanation for the paradox is that specialists may not make recommendations for preventive care outside their area of specialty. We speculate that improved care coordination among mental health, primary care and specialty care providers may contribute to a better understanding of patients’ comprehensive care needs and making critical recommendations.

In contrast, older old beneficiaries had a slightly better chance to get recommended care than the younger old, all else equal. This is consistent with our post-hoc finding that on average older old beneficiaries visited their doctors more often than the younger old. We found greater comorbidity associated with greater likelihood of receiving appropriate care, similar to published reports [26, 37]. Increased use of recommended care for both groups is likely due to their frequent office visits leading to a greater chance to fulfill care requirements.

As expected, non-Hispanic black race, less than high-school education, non-Metropolitan residence and disability independently predicted underuse of care. Although reversed racial disparity has been reported [27], likely due to selection bias of the samples [38], different sets of quality of care indicators studied, and use of claims versus self-reported data, underuse of medical care among racial minorities is more accentuated in literature [21, 39]. Improving surveillance data systems, creating a culturally-competent medical workforce and recruiting minority health professionals have emerged as strategies to address racial/ethnic differences in health and health care [40, 41]. Lower socioeconomic position [42] and rural settings [43, 44] diminish the chance to obtaining cancer prevention services. Removal of access barriers to care, especially financial barriers, was endorsed as central to create equity in health outcomes across different socioeconomic groups [45]. Availability of services, knowledge or physician recommendations of needed care and transportation are often reported factors underlying the geographic disparities in care and are points to address in interventions [43, 44] Greater use of home care in rural areas was also reported [46]. Future research may investigate population-level utilization of a wide range of health services. Disability is a known risk factor for underuse of certain care among Medicare beneficiaries excluding younger beneficiaries [24, 25]. This is also reflected in our study, which found a monotonic increase in care disparities with higher activity limitation stages (greater severity). Physical barriers, lack of professional assistance and social support, as well as experiences of distress likely influence service underuse [47, 48]. Resource reallocation targeting disabled individuals may aid their access to care and increase use of recommended care. Furthermore, since functional decline after hospitalization is fairly common [49], establishing care continuity in communities after hospital discharge can be critical for disabled persons.

We studied three Medicare age groups who likely occupy different positions in a social hierarchy and differ in their health status and utilization of health services. Such comparison is useful in identifying a disadvantaged population and its care needs, which subsequently informs resource reallocation to achieve greater equity. The study has several limitations. This study does not answer the question why younger beneficiaries underuse recommended care. The mechanism can be explained by access barriers to care, care not recommended by providers, or care recommended by providers but was not sought by the patient. For instance, some beneficiaries did not seek or comply with recommended care because of their limited health literacy or knowledge about their care plans [50, 51]. It has also been reported that providers tend to downplay the importance of healthy behaviors and disease prevention in the lives of their disabled patients [47]. Due to data limitation, we were not able to incorporate these potential causes for failure of care compliance in our analysis. We recommend in-depth observational studies that explore patient-doctor encounters to determine the causes of underuse and what types of appropriate preventions should be in place. Asch’s indicator system reflects care needs of highly prevalent conditions among the elderly population. These indicators may not reflect all care needs of younger beneficiaries, especially those experiencing cognitive, developmental or psychiatric disorders. Indicators that address the care for prevalent diseases in younger beneficiaries are highly desirable. Stratified analysis of receipt of recommended care among beneficiaries with versus without psychiatric disorders may also be considered, since persons who have been admitted for mental disorders tend to have poorer quality of care and higher mortality in somatic diseases, compared to persons who only have somatic diseases [52]. We acknowledge the likelihood of residual confounding in socioeconomic, comorbidity and to a lesser extent disability, measures. It is possible that even after controlling for all these variables, the reason of underuse among younger beneficiaries is that they are still sicker and more disadvantaged, rather than an independent effect of younger beneficiary status. Although there may be geographic variations in receipt of recommended care, MCBS is not powered to investigate state-level estimates. MCBS claims data (2002–2010) used in this study are not the most recent; however, the structure of the Medicare program eligibility for those under 65 has not changed, and the historical data matches the period when Asch’s indicators were developed. Due to incomplete claims data from beneficiaries enrolled in a managed care program, our results only apply to the fee-for-service Medicare population. Even though we combined eight beneficiary cohorts to compensate for small sample sizes associated with certain indicators, some indicators could not be addressed in the younger beneficiaries since cell sizes were still too small to report.

Conclusions

Our study has identified social and medical vulnerabilities of younger Medicare beneficiaries, and their lack of overall and specific type of care. Our results based on improved indicator metrics corroborated previous findings of potential influences on health service underutilization. CMS (Quality Strategy 2016) envisions care as valued-based: person-centered, cost-efficient and health-promoting [53]. It sets effective communication and coordination of care, prevention and treatment of chronic diseases, and partnership with communities to promote healthy living as among its goals, and eliminating racial and ethnic disparities and strengthening infrastructure and data systems as part of its foundational principles. Our findings provide evidence for the need of interventions that may bridge the health equity gap in the Medicare population.

Declarations

Acknowledgments

The authors thank Margaret G. Stineman, MD, for conceptualizing the study, obtaining funding and providing input in the manuscript.

Funding

The research for this manuscript was supported by grants from the National Institutes of Health (R01AG040105).

Availability of data and materials

The data that support the findings of this study are available from Centers for Medicare and Medicaid Services (CMS)’s Medicare Current Beneficiary Survey. CMS has granted our research team access to the survey data. However, restrictions apply to the availability of the full data, which were used under license for the NIH-grant funded project, and so are not publicly available.

Authors’ contributions

LN contributed to the conceptualization and design of the study, data analysis and interpretation, and writing of the manuscript; JS, LP, JK, DX, HB contributed to the conceptualization and design of the study, data interpretation and critical review of the manuscript; FK contributed to conceptualization and design of the study, data analysis and interpretation and review of the manuscript; SA critically reviewed the manuscript; SH contributed to the conceptualization and design of the study, data interpretation, critical review and revision of the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study was approved by the University of Pennsylvania Institutional Review Board, with approval number 817595. Consent to participate in this study was not applicable.

Disclosures

The research for this manuscript was supported by grants from the National Institutes of Health (R01HD074756). There are no personal conflicts of interest of any of the authors in the past 3 years, and no authors reported disclosures beyond the funding source. The opinions and conclusions of the authors are not necessarily those of the sponsoring agency. We certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated. This material has not been previously presented at a meeting.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania
(2)
Geriatric Psychiatry Section of the Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
(3)
Center for Patient Care and Outcomes Research (PCOR), Medical College of Wisconsin
(4)
Department of Medicine, Medical College of Wisconsin
(5)
Division of General Medical Disciplines, Stanford University School of Medicine
(6)
Center for Pharmacoepidemiology Research and Training, University of Pennsylvania

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Copyright

© The Author(s). 2017

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