| Literature DB >> 32667653 |
Lucas A Berenbrok1, Nico Gabriel1, Kim C Coley1, Inmaculada Hernandez1.
Abstract
Importance: The shift toward value-based care has placed emphasis on preventive care and chronic disease management services delivered by multidisciplinary health care teams. Community pharmacists are particularly well positioned to deliver these services due to their accessibility. Objective: To compare the number of patient visits to community pharmacies and the number of encounters with primary care physicians among Medicare beneficiaries who actively access health care services. Design, Setting, and Participants: This cross-sectional study analyzed a 5% random sample of 2016 Medicare beneficiaries from January 1, 2016, to December 31, 2016 (N = 2 794 078). Data were analyzed from October 23, 2019, to December 20, 2019. Medicare Part D beneficiaries who were continuously enrolled and had at least 1 pharmacy claim and 1 encounter with a primary care physician were included in the final analysis (n = 681 456). Those excluded from the study were patients who were not continuously enrolled in Part D until death, those with Part B skilled nursing claims, and those with Part D mail-order pharmacy claims. Exposures: We conducted analyses for the overall sample and for subgroups defined by demographics, region of residence, and clinical characteristics. Main Outcomes and Measures: Outcomes included the number of visits to community pharmacies and encounters with primary care physicians. Unique visits to the community pharmacy were defined using a 13-day window between individual prescription drug claims. Kruskal-Wallis tests were used to compare the medians for the 2 outcomes.Entities:
Mesh:
Year: 2020 PMID: 32667653 PMCID: PMC7364370 DOI: 10.1001/jamanetworkopen.2020.9132
Source DB: PubMed Journal: JAMA Netw Open ISSN: 2574-3805
Figure 1. Flow Diagram
Characteristics of Active Medicare Beneficiaries Included in Analysis
| Variable | No. (%) |
|---|---|
| Age, y | |
| <65 | 120 428 (17.7) |
| 65-74 | 271 546 (39.9) |
| ≥75 | 289 482 (42.5) |
| Sex | |
| Male | 262 771 (38.6) |
| Female | 418 685 (61.4) |
| Race/ethnicity | |
| White | 560 416 (82.2) |
| Black | 65 469 (9.6) |
| Hispanic | 16 567 (2.4) |
| Other | 39 004 (5.7) |
| Region of residence | |
| By degree of urbanization | |
| Metropolitan area | 529 414 (77.7) |
| Nonmetropolitan area | |
| Urban | 134 692 (19.8) |
| Rural | 16 537 (2.4) |
| By access to health care | |
| Medically underserved area | 160 591 (23.6) |
| Nonmedically underserved area | 520 865 (76.4) |
| By region of residence | |
| Northeast | 128 997 (18.9) |
| Midwest | 153 577 (22.5) |
| South | 282 809 (41.5) |
| West | 114 765 (16.8) |
We used Rural-Urban Continuum Codes from the US Department of Agriculture Economic Research Service to categorize metropolitan areas (codes 1-3), nonmetropolitan urban areas (codes 4-7), and nonmetropolitan rural areas (codes 8-9).[9]
Does not sum to group totals due to missing data.
We used data from the Health Resources & Services Administration to identify medically underserved areas.[10]
Number of Primary Care Physician Encounters and Pharmacy Visits per Person-Year for the Overall Sample and by Subgroups
| Variable | No. of encounters/visits per person-year, median (IQR) | ||||
|---|---|---|---|---|---|
| Primary care physician encounters | Pharmacy visits | ||||
| Defined using 13-d window | Defined using 10-d window | Defined using 13-d window | Defined using 10-d window | ||
| Overall | 7 (4-14) | 13 (9-17) | 14 (9-19) | <.001 | <.001 |
| Subgroup analyses demographics | |||||
| Age, y | |||||
| <65 | 7 (3-13) | 15 (10-18) | 16 (11-21) | <.001 | <.001 |
| 65-74 | 7 (3-13) | 12 (8-16) | 13 (8-18) | ||
| ≥75 | 8 (4-15) | 13 (9-17) | 14 (10-19) | ||
| Sex | |||||
| Male | 7 (3-13) | 13 (8-17) | 14 (9-19) | <.001 | <.001 |
| Female | 8 (4-14) | 13 (9-17) | 14 (9-19) | ||
| Race | |||||
| White | 7 (4-14) | 13 (9-17) | 14 (9-19) | <.001 | <.001 |
| Black | 7 (3-14) | 13 (9-17) | 15 (10-20) | ||
| Hispanic | 8 (4-15) | 13 (9-17) | 14 (9-19) | ||
| Other | 7 (4-14) | 12 (7-15) | 13 (8-17) | ||
| Region of residence | |||||
| By degree of urbanization | |||||
| Metropolitan area | 8 (4-14) | 13 (8-17) | 14 (9-19) | <.001 | <.001 |
| Nonmetropolitan area | |||||
| Urban | 6 (3-12) | 14 (10-17) | 15 (10-20) | <.001 | <.001 |
| Rural | 5 (2-11) | 14 (10-17) | 15 (10-20) | ||
| By access to health care | |||||
| Medically underserved area | 8 (4-14) | 13 (8-17) | 14 (9-19) | <.001 | <.001 |
| Nonmedically underserved area | 7 (3-13) | 14 (9-17) | 15 (10-20) | ||
| Clinical characteristics | |||||
| Acute myocardial infarction | 14 (7-26) | 15 (12-19) | 17 (12-21) | .60 | <.001 |
| Asthma | 12 (6-21) | 16 (12-19) | 18 (13-22) | <.001 | |
| Atrial fibrillation | 13 (7-23) | 16 (12-19) | 18 (13-22) | ||
| Chronic kidney disease | 11 (5-20) | 15 (12-19) | 17 (12-22) | ||
| COPD | 12 (6-21) | 16 (12-19) | 18 (13-22) | ||
| Depression | 10 (5-18) | 16 (12-19) | 18 (13-22) | ||
| Diabetes | 10 (5-17) | 15 (11-18) | 17 (12-21) | ||
| Heart failure | 10 (5-19) | 15 (11-18) | 17 (12-21) | ||
| Hyperlipidemia | 9 (5-16) | 14 (10-17) | 15 (11-20) | ||
| Hypertension | 9 (5-16) | 14 (10-18) | 15 (11-20) | ||
| Osteoporosis | 11 (6-18) | 14 (10-18) | 15 (10-20) | ||
| Rheumatoid arthritis/osteoarthritis | 10 (5-18) | 14 (11-18) | 16 (11-21) | ||
| Stroke or transient ischemic attack | 13 (7-23) | 15 (11-18) | 17 (12-21) | ||
Abbreviations: COPD, chronic obstructive pulmonary disease; IQR, interquartile range.
We used Rural-Urban Continuum Codes from the US Department of Agriculture Economic Research Service to categorize metropolitan areas (codes 1-3), nonmetropolitan urban areas (codes 4-7), and nonmetropolitan rural areas (codes 8-9).[9]
We used data from the Health Resources & Services Administration to identify medically underserved areas.[10]
We used the Centers for Medicare and Medicaid Services Chronic Condition Data Warehouse definitions of priority conditions.[11]
Figure 2. Difference in the Median Number of Encounters With Primary Care Physicians (PCPs) and Visits to Community Pharmacies
This figure represents the difference between the median number of visits to the community pharmacy and encounters with primary care physicians by state (A) and by county (B). Pharmacy visits were defined using a 13-day window between claims, as explained in the Methods section. Insufficient data denotes that there were less than 11 beneficiaries in each county, which is the minimum cell size requirement for reporting from the Centers for Medicare and Medicaid Services. Only 9 counties had primary care physician encounters that equaled or outnumbered pharmacy visits. These counties are in Florida, Georgia, Indiana, Kentucky, North Carolina, and Texas.