A methodology for identifying high-need, high-cost patient personas for international comparisons

Living longer in declining health: Factors driving healthcare costs among older people

BACKGROUND

Developed countries are facing challenges in caring for people who are living longer but with a greater morbidity burden. Such people are likely to be regular users of healthcare.

OBJECTIVES

Our analytical aim is to identify factors that explain healthcare costs among: (1) people over 55 years old; (2) the top 5% and 1% high-cost users among this population; (3) those that transition into the top 5% and 1% from one year to the next; (4) those that appear in the top 5% and 1% over multiple years; and (5) those that remain in the top 5% and 1% over consecutive years.

METHODS

The data covered 2011 to 2017 and comprised 1,485,170 observations for a random sample of 224,249 people aged over 55 years in the Catalan region of Spain. We analysed each person's annual healthcare costs across all public healthcare settings related to their age, gender, socio-economic status (SES), whether or not and when they died, and morbidity status, through Adjusted Morbidity Groups.

RESULTS

After controlling for morbidity status, the oldest people did not have the highest costs and were less likely to be among the most costly patients. There was also only a modest impact on costs associated with SES and with dying. Healthcare costs were substantially higher for those with a neoplasm or four or more long term conditions (LTCs), costs rising with the complexity of their conditions. These morbidity indicators were also the most important factors associated with being and remaining in the top 5% or top 1% of costs.

CONCLUSION

Our results suggest that age and proximity to death are poor predictors of higher costs. Rather, healthcare costs are explained mainly by morbidity status, particularly whether someone has neoplasms or multiple LTCs. Morbidity measures should be included in future studies of healthcare costs.

Why the US spends more treating high-need high-cost patients: a comparative study of pricing and utilization of care in six high-income countries

One of the most pressing challenges facing most health care systems is rising costs. As the population ages and the demand for health care services grows, there is a growing need to understand the drivers of these costs across systems. This paper attempts to address this gap by examining utilization and spending of the course of a year for two specific high-need high-cost patient types: a frail older person with a hip fracture and an older person with congestive heart failure and diabetes. Data on utilization and expenditure is collected across five health care settings (hospital, post-acute rehabilitation, primary care, outpatient specialty and drugs), in six countries (Canada (Ontario), France, Germany, Spain (Aragon), Sweden and the United States (fee for service Medicare) and used to construct treatment episode Purchasing Power Parities (PPPs) that compare prices using baskets of goods from the different care settings. The treatment episode PPPs suggest other countries have more similar volumes of care to the US as compared to other standardization approaches, suggesting that US prices account for more of the differential in US health care expenditures. The US also differs with regards to the share of expenditures across care settings, with post-acute rehab and outpatient speciality expenditures accounting for a larger share of the total relative to comparators.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-022-01688-3.