Evidence map›Paper›PMID 35725607›Full record

ArticleInternational journal for equity in health2022

Identifying patterns of clinical conditions among high-cost older adult health care users using claims data: a latent class approach.

Xiaolin He, Danjin Li, Wenyi Wang, Hong Liang, Yan Liang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal for equity in health, 2022. 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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xiaolin HeDepartment of Social Policy, Shanghai Administration Institute, Shanghai, China.
Danjin LiSchool of Nursing, Fudan University, Shanghai, China.
Wenyi WangSchool of Social Development and Public Policy, Fudan University, Shanghai, China.
Hong LiangSchool of Social Development and Public Policy, Fudan University, Shanghai, China.
Yan LiangSchool of Nursing, Fudan University, Shanghai, China. liangyan@fudan.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo identify patterns of clinical conditions among high-cost older adults health care users and explore the associations between characteristics of high-cost older adults and patterns of clinical conditions.

methodsWe analyzed data from the Shanghai Basic Social Medical Insurance Database, China. A total of 2927 older adults aged 60 years and over were included as the analysis sample. We used latent class analysis to identify patterns of clinical conditions among high-cost older adults health care users. Multinomial logistic regression models were also used to determine the associations between demographic characteristics, insurance types, and patterns of clinical conditions.

resultsFive clinically distinctive subgroups of high-cost older adults emerged. Classes included "cerebrovascular diseases" (10.6% of high-cost older adults), "malignant tumor" (9.1%), "arthrosis" (8.8%), "ischemic heart disease" (7.4%), and "other sporadic diseases" (64.1%). Age, sex, and type of medical insurance were predictors of high-cost older adult subgroups.

conclusionsProfiling patterns of clinical conditions among high-cost older adults is potentially useful as a first step to inform the development of tailored management and intervention strategies.

Indexed as

Delivery of Health CareAgedChinaHumansLatent Class AnalysisLogistic ModelsMiddle AgedHealth care costsHealth service useHigh-cost usersOlder adultsSegmentation

Identifiers

PMID35725607
PMCPMC9210624

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