Evidence map›Paper›PMID 38701030›Full record

ArticleJournal of managed care & specialty pharmacy2024

Costs and health care resource utilization among Medicare beneficiaries diagnosed with chronic lymphocytic leukemia.

Tsung-Ying Lee, Abree Johnson, Catherine E Cooke, Jean A Yared, Amanda Summers, Keri Yang, Sizhu Liu, Boxiong Tang, Eberechukwu Onukwugha

Abstract read
In one paragraph

Article in Journal of managed care & specialty pharmacy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Tsung-Ying LeeDepartment of Practice, Sciences, and Health Outcomes Research, School of Pharmacy, University of Maryland, Baltimore.
Abree JohnsonDepartment of Practice, Sciences, and Health Outcomes Research, School of Pharmacy, University of Maryland, Baltimore.
Catherine E CookeDepartment of Practice, Sciences, and Health Outcomes Research, School of Pharmacy, University of Maryland, Baltimore.
Jean A YaredDepartment of Medicine, School of Medicine, University of Maryland, Baltimore.
Amanda SummersDepartment of Practice, Sciences, and Health Outcomes Research, School of Pharmacy, University of Maryland, Baltimore.
Keri YangBeiGene USA, Inc., San Mateo, CA.
Sizhu LiuBeiGene USA, Inc., San Mateo, CA.
Boxiong TangBeiGene USA, Inc., San Mateo, CA.
Eberechukwu OnukwughaDepartment of Practice, Sciences, and Health Outcomes Research, School of Pharmacy, University of Maryland, Baltimore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic lymphocytic leukemia (CLL) is the most common type of leukemia. However, published studies of CLL have either only focused on costs among individuals diagnosed with CLL without a non-CLL comparator group or focused on costs associated with specific CLL treatments. An examination of utilization and costs across different care settings provides a holistic view of utilization associated with CLL.

objectiveTo quantify the health care costs and resource utilization types attributable to CLL among Medicare beneficiaries and identify predictors associated with each of the economic outcomes among beneficiaries diagnosed with CLL.

methodsThis retrospective study used a random 20% sample of the Medicare Chronic Conditions Data Warehouse (CCW) database covering the 2017-2019 period. The study population consisted of individuals with and without CLL. The CLL cohort and non-CLL cohort were matched using a 1:5 hard match based on baseline categorical variables. We characterized economic outcomes over 360 days across cost categories and places of services. We estimated average marginal effects using multivariable generalized linear regression models of total costs and across type of services. Total cost was compared between CLL and non-CLL cohorts using the matched sample. We used generalized linear models appropriate for the count or binary outcome to identify factors associated with various categories of health care resource utilization, such as inpatient admissions, emergency department (ED) visits, and oncologist/hematologist visits.

resultsA total of 2,736 beneficiaries in the CLL cohort and 13,571 beneficiaries in the non-CLL matched cohort were identified. Compared with the non-CLL cohort, the annual cost for the CLL cohort was higher (CLL vs non-CLL, mean [SD]: $22,781 [$37,592] vs $13,901 [$24,725]), mainly driven by health care provider costs ($6,535 vs $3,915) and Part D prescription drug costs ($5,916 vs $2,556). The main categories of health care resource utilization were physician evaluation/management visits, oncologist/hematologist visits, and laboratory services. Compared with beneficiaries aged 65-74 years, beneficiaries aged 85 years or older had lower use and cost in maintenance services (ie, oncologist visits, hospital outpatient costs, and prescription drug cost) but higher use and cost in acute services (ie, ED). Compared with residency in a metropolitan area, living in a nonmetropolitan area was associated with fewer physician visits but higher ED visits and hospitalizations.

conclusionsThe cooccurrence of lower utilization of routine care services, along with higher utilization of acute care services among some individuals, has implications for patient burden and warrants further study.

Indexed as

Health Care CostsLeukemia, Lymphocytic, Chronic, B-CellMedicarePatient Acceptance of Health CareAgedAged, 80 and overFemaleHealth ResourcesHospitalizationHumansMaleRetrospective StudiesUnited States

Identifiers

PMID38701030
PMCPMC11068650

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.