Dataset Information

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

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.

SUBMITTER: He X

PROVIDER: S-EPMC9210624 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

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

He Xiaolin X Li Danjin D Wang Wenyi W Liang Hong H Liang Yan Y

International journal for equity in health 20220620 1

<h4>Objectives</h4>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.<h4>Methods</h4>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-c ...[more]

PMID: 35725607

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Project description:BackgroundOlder adult overdose death rates have increased significantly in recent years. However, research for prevention of drug overdose death specific to older adults is limited. Our objective is to identify profiles based on missed intervention points (touchpoints) to inform prevention of future older adult unintentional overdose deaths.MethodsWe used latent class analysis methods to identify profiles of decedents aged 55 + years in the Illinois Statewide Unintentional Drug Overdose Reporting System. This system collects data on 92.6% of all unintentional overdose deaths in Illinois and includes data from death certificates, coroner/medical examiner, toxicology, and autopsy reports. Data include decedent demographics, circumstances leading up to and surrounding the fatal overdose and details regarding the overdose. Variables in the latent class analysis model included sex, race, alcohol test result, social isolation, recent emergency department (ED) visit, chronic pain, and pain treatment.ResultsWe identified three distinct decent profiles. Class 1 (13% of decedents) included female decedents who were in pain treatment, had physical health problems, and had greater likelihood of a recent ED visit before their death. Class 2 (35% of decedents) decedents were most likely to be socially connected (live with others, employed, had social or family relationships) but less likely to have recent healthcare visits. Class 3 (52% of decedents) decedents had higher social isolation (lived alone, unemployed, unpartnered), were mostly male, had fewer known physical health conditions, and more alcohol positivity at time of death. White decedents are clustered in class 1 while Black decedents are predominant in classes 2 and 3.ConclusionsThese profiles link to potential touchpoint opportunities for substance use disorder screening harm reduction and treatment. Class 1 members were most likely to be reachable in healthcare settings. However, most decedents were members of Classes 2 and 3 with less engagement in the healthcare system, suggesting a need for screening and intervention in different contexts. For Class 2, intervention touchpoints might include education and screening in work or social settings such as senior centers given the higher degree of social connectivity. For Class 3, the most isolated group, touchpoints may occur in the context of harm reduction outreach and social service delivery.

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Dataset Information

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

Objectives

Methods

Results

Conclusions

Publications

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

Similar Datasets

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