Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jun 20;21(1):86.
doi: 10.1186/s12939-022-01688-3.

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

Xiaolin He  1 , Danjin Li  2 , Wenyi Wang  3 , Hong Liang  3 , Yan Liang  4
Affiliations

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

Xiaolin He et al. Int J Equity Health. .

Abstract

Objectives: To 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.

Methods: We 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.

Results: Five 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.

Conclusions: Profiling 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.

Keywords: Health care costs; Health service use; High-cost users; Older adults; Segmentation.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Total aggregate (Panel A) and average per patient (Panel B) spending, for latent classes, 2019
See this image and copyright information in PMC

References

    1. Kim YJ, Park H. Improving prediction of high-cost health care users with medical check-up data. Big Data. 2019;7:163–175. doi: 10.1089/big.2018.0096. - DOI - PubMed
    1. Anderson M, Revie CW, Stryhn H, Neudorf C, Rosehart Y, Li W, Osman M, Buckeridge DL, Rosella LC, Wodchis WP. Defining 'actionable' high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology. Int J Equity Health. 2019;18:171. doi: 10.1186/s12939-019-1074-3. - DOI - PMC - PubMed
    1. Ng SH, Rahman N, Ang IYH, Sridharan S, Ramachandran S, Wang DD, Tan CS, Toh SA, Tan XQ. Characterization of high healthcare utilizer groups using administrative data from an electronic medical record database. BMC Health Serv Res. 2019;19:452. doi: 10.1186/s12913-019-4239-2. - DOI - PMC - PubMed
    1. Wammes JJG, van der Wees PJ, Tanke MAC, Westert GP, Jeurissen PPT. Systematic review of high-cost patients' characteristics and healthcare utilisation. BMJ Open. 2018;8:e023113. doi: 10.1136/bmjopen-2018-023113. - DOI - PMC - PubMed
    1. Lee JY, Muratov S, Tarride JE, Holbrook AM. Managing high-cost healthcare users: the international search for effective evidence-supported strategies. J Am Geriatr Soc. 2018;66:1002–1008. doi: 10.1111/jgs.15257. - DOI - PubMed

Publication types