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.

High-cost users: drivers of inpatient healthcare expenditure concentration in urban China.

BACKGROUND: Total healthcare expenditures are concentrated among a small number of patients. To date, studies on the concentration of health care expenditures in developing countries are limited, mainly focusing on concentration measures and the demographic, clinical and socioeconomic characteristics of high-cost users (HCU). The drivers of the skewed overall distribution of health care expenditures are opaque. Using inpatient administrative claims data, this study provides new evidence on the concentration of healthcare expenditures in China; the demographic and clinical characteristics of high-cost users; and the drivers of the overall distribution of healthcare expenditures. METHODS: Utilizing administrative claims data for hospitalization in a prefecture-level city in China, we investigated the concentration of healthcare expenditure. We used recentered influence function (RIF) regression to examine the drivers of healthcare expenditure concentration, decomposing and estimating the effects of demographic and disease characteristics on the overall distribution of health care expenditures. RESULTS: Using a sample of 87,841 adults, we found extreme skewness in the distribution of inpatient medical expenditures in China, with approximately 49% of annual medical expenditures generated by the top 10% of inpatient groups. HCUs tend to be elderly and male, with high-frequency hospitalizations and long lengths of stay. In addition, healthcare expenditure concentration was related to diseases of the circulatory system, malignant neoplasms, diseases of the musculoskeletal system and connective tissue, diseases of the digestive system, injury and poisoning, and diseases of the respiratory system. Malignant and major diseases reinforced the concentration of healthcare spending, and a 10% increase in the prevalence of malignancy would result in a predicted Gini coefficient increase of 7.2%, heart disease of 0.92% and cerebrovascular disease of 1.5%. The above significant positive effects were not observed for hypertension and diabetes mellitus. CONCLUSIONS: Our study provides new insights into the concentration of inpatient medical expenditures in China, including the precise picture of HCU expenditure concentration, the drivers of HCU expenditure concentration and the magnitude of their impact. With the aging of China's population and the profound shift in the disease spectrum, policymakers need to strengthen the early detection and intervention management of specific chronic diseases and high-risk populations, especially the early diagnosis and treatment of key cancers.

Association between high cost user status and end-of-life care in hospitalized patients: A national cohort study of patients who die in hospital.

BACKGROUND: Studies comparing end-of-life care between patients who are high cost users of the healthcare system compared to those who are not are lacking. AIM: The objective of this study was to describe and measure the association between high cost user status and several health services outcomes for all adults in Canada who died in acute care, compared to non-high cost users and those without prior healthcare use. SETTINGS AND PARTICIPANTS: We used administrative data for all adults who died in hospital in Canada between 2011 and 2015 to measure the odds of admission to the intensive care unit (ICU), receipt of invasive interventions, major surgery, and receipt of palliative care during the hospitalization in which the patient died. High cost users were defined as those in the top 10% of acute healthcare costs in the year prior to a person's hospitalization in which they died. RESULTS: Among 252,648 people who died in hospital, 25,264 were high cost users (10%), 112,506 were non-high cost users (44.5%) and 114,878 had no prior acute care use (45.5%). After adjustment for age and sex, high cost user status was associated with a 14% increased odds of receiving an invasive intervention, a 15% increased odds of having major surgery, and an 8% lower odds of receiving palliative care compared to non-high cost users, but opposite when compared to patients without prior healthcare use. CONCLUSIONS: Many patients receive aggressive elements of end-of-life care during the hospitalization in which they die and a substantial number do not receive palliative care. Understanding how this care differs between those who were previously high- and non-high cost users may provide an opportunity to improve end of life care for whom better care planning and provision ought to be an equal priority.

Characteristics and resource utilization of high-cost users in the intensive care unit: a population-based cohort study.

BACKGROUND: Healthcare expenditure within the intensive care unit (ICU) is costly. A cost reduction strategy may be to target patients accounting for a disproportionate amount of healthcare spending, or high-cost users. This study aims to describe high-cost users in the ICU, including health outcomes and cost patterns. METHODS: We conducted a population-based retrospective cohort study of patients with ICU admissions in Ontario from 2011 to 2018. Patients with total healthcare costs in the year following ICU admission (including the admission itself) in the upper 10th percentile were defined as high-cost users. We compared characteristics and outcomes including length of stay, mortality, disposition, and costs between groups. RESULTS: Among 370,061 patients included, 37,006 were high-cost users. High-cost users were 64.2 years old, 58.3% male, and had more comorbidities (41.2% had ≥3) when likened to non-high cost users (66.1 years old, 57.2% male, 27.9% had ≥3 comorbidities). ICU length of stay was four times greater for high-cost users compared to non-high cost users (22.4 days, 95% confidence interval [CI] 22.0-22.7 days vs. 5.56 days, 95% CI 5.54-5.57 days). High-cost users had lower in-hospital mortality (10.0% vs.14.2%), but increased dispositioning outside of home (77.4% vs. 42.2%) compared to non-high-cost users. Total healthcare costs were five-fold higher for high-cost users ($238,231, 95% CI $237,020-$239,442) compared to non-high-cost users ($45,155, 95% CI $45,046-$45,264). High-cost users accounted for 37.0% of total healthcare costs. CONCLUSION: High-cost users have increased length of stay, lower in-hospital mortality, and higher total healthcare costs when compared to non-high-cost users. Further studies into cost patterns and predictors of high-cost users are necessary to identify methods of decreasing healthcare expenditure.

Defining and Predicting High Cost Utilization in Children's Outpatient Mental Health Services.

Little is known about high-cost service users in the context of youth outpatient mental health, despite the fact that they account for a large proportion of overall mental healthcare expenditures. A nuanced understanding of these users is critical to develop and implement tailored services, as well as to inform relevant policies. This study aims to characterize high-cost service users by examining demographic factors, diagnoses, and service type use. Administrative service use data were extracted from a large County Department of Behavioral Health Services database. Latent profile analyses suggest a four-profile solution primarily distinguished by youth age and diagnostic complexity. Study findings have implications for defining high-cost service users and key targets for efforts aiming to improve outcomes for these youth.

Unique Characteristics of High-Cost Users of Medical Care With Comorbid Mental Illness or Addiction in a Population-Based Cohort.

OBJECTIVE: To understand whether high-cost users of medical care with and without comorbid mental illness or addiction differ in terms of their sociodemographic and health characteristics. Unique characteristics would warrant different considerations for interventions and service design aimed at reducing unnecessary health care utilization and associated costs. METHODS: From the top 10% of Ontarians ranked by total medical care costs during fiscal year 2011/2012 (N = 314,936), prior 2-year mental illness or addiction diagnoses were determined from administrative data. Sociodemographics, medical illness characteristics, medical costs, and utilization were compared between those high-cost users of medical care with and without comorbid mental illness or addiction. Odds of being a frequent user of inpatient (≥3 admissions) and emergency (≥5 visits) services were compared between groups, adjusting for age, sex, socioeconomic status and medical illness characteristics. RESULTS: High-cost users of medical care with comorbid mental illness or addiction were younger, had a lower socioeconomic status, had greater historical medical morbidity, and had higher total medical care costs (mean excess of $2,031/user) than those without. They were more likely to be frequent users of inpatient (12.8% vs 10.2%; adjusted OR, 1.14; 95% CI: 1.12-1.17) and emergency (8.4% vs 4.8%; adjusted OR, 1.55; 95% CI: 1.50-1.59) services. Effect sizes were larger in major mood, psychotic, and substance use disorder subgroups. CONCLUSIONS: High-cost medical care users with mental illness or addiction have unique characteristics with respect to sociodemographics and service utilization patterns to consider in interventions and policies for this patient group.

Acute Care Use for Ambulatory Care-Sensitive Conditions in High-Cost Users of Medical Care with Mental Illness and Addictions.

OBJECTIVE: The role of mental illness and addiction in acute care use for chronic medical conditions that are sensitive to ambulatory care management requires focussed attention. This study examines how mental illness or addiction affects risk for repeat hospitalization and/or emergency department use for ambulatory care-sensitive conditions (ACSCs) among high-cost users of medical care. METHOD: A retrospective, population-based cohort study using data from Ontario, Canada. Among the top 10% of medical care users ranked by cost, we determined rates of any and repeat care use (hospitalizations and emergency department [ED] visits) between April 1, 2011, and March 31, 2012, for 14 consensus established ACSCs and compared them between those with and without diagnosed mental illness or addiction during the 2 years prior. Risk ratios were adjusted (aRR) for age, sex, residence, and income quintile. RESULTS: Among 314,936 high-cost users, 35.9% had a mental illness or addiction. Compared to those without, individuals with mental illness or addiction were more likely to have an ED visit or hospitalization for any ACSC (22.8% vs. 19.6%; aRR, 1.21; 95% confidence interval [CI], 1.20-1.23). They were also more likely to have repeat ED visits or hospitalizations for the same ACSC (6.2% vs. 4.4% of those without; aRR, 1.48; 95% CI, 1.44-1.53). These associations were stronger in stratifications by mental illness diagnostic subgroup, particularly for those with a major mental illness. CONCLUSIONS: The presence of mental illness and addiction among high-cost users of medical services may represent an unmet need for quality ambulatory and primary care.

Regional variation in healthcare spending and mortality among senior high-cost healthcare users in Ontario, Canada: a retrospective matched cohort study.

BACKGROUND: Senior high cost health care users (HCU) are a priority for many governments. Little research has addressed regional variation of HCU incidence and outcomes, especially among incident HCU. This study describes the regional variation in healthcare costs and mortality across Ontario's health planning districts [Local Health Integration Networks (LHIN)] among senior incident HCU and non-HCU and explores the relationship between healthcare spending and mortality. METHODS: We conducted a retrospective population-based matched cohort study of incident senior HCU defined as Ontarians aged ≥66 years in the top 5% most costly healthcare users in fiscal year (FY) 2013. We matched HCU to non-HCU (1:3) based on age, sex and LHIN. Primary outcomes were LHIN-based variation in costs (total and 12 cost components) and mortality during FY2013 as measured by variance estimates derived from multi-level models. Outcomes were risk-adjusted for age, sex, ADGs, and low-income status. In a cost-mortality analysis by LHIN, risk-adjusted random effects for total costs and mortality were graphically presented together in a cost-mortality plane to identify low and high performers. RESULTS: We studied 175,847 incident HCU and 527,541 matched non-HCU. On average, 94 out of 1000 seniors per LHIN were HCU (CV = 4.6%). The mean total costs for HCU in FY2013 were 12 times higher that of non-HCU ($29,779 vs. $2472 respectively), whereas all-cause mortality was 13.6 times greater (103.9 vs. 7.5 per 1000 seniors). Regional variation in costs and mortality was lower in senior HCU compared with non-HCU. We identified greater variability in accessing the healthcare system, but, once the patient entered the system, variation in costs was low. The traditional drivers of costs and mortality that we adjusted for played little role in driving the observed variation in HCUs' outcomes. We identified LHINs that had high mortality rates despite elevated healthcare expenditures and those that achieved lower mortality at lower costs. Some LHINs achieved low mortality at excessively high costs. CONCLUSIONS: Risk-adjusted allocation of healthcare resources to seniors in Ontario is overall similar across health districts, more so for HCU than non-HCU. Identified important variation in the cost-mortality relationship across LHINs needs to be further explored.

Rates of Mental Illness and Addiction among High-Cost Users of Medical Services in Ontario.

OBJECTIVE: To quantify the burden of mental illness and addiction among high-costing users of medical services (HCUs) using population-level data from Ontario, and compare to a referent group of nonusers. METHOD: We conducted a population-level cohort study using health administrative data from fiscal year 2011-2012 for all Ontarians with valid health insurance as of April 1, 2011 (N = 10,909,351). Individuals were grouped based on medical costs for hospital, emergency, home, complex continuing, and rehabilitation care in 2011-2012: top 1%, top 2% to 5%, top 6% to 50%, bottom 50%, and a zero-cost nonuser group. The rate of diagnosed psychotic, major mood, and substance use disorders in each group was compared to the zero-cost referent group with adjusted odds ratios (AORs) for age, sex, and socioeconomic status. A sensitivity analysis included anxiety and other disorders. RESULTS: Mental illness and addiction rates increased across cost groups affecting 17.0% of the top 1% of users versus 5.7% of the zero-cost group (AOR, 3.70; 95% confidence interval [CI], 3.59 to 3.81). This finding was most pronounced for psychotic disorders (3.7% vs. 0.7%; AOR, 5.07; 95% CI, 4.77 to 5.38) and persisted for mood disorders (10.0% vs. 3.3%; AOR, 3.52; 95% CI, 3.39 to 3.66) and substance use disorders (7.0% vs. 2.3%; AOR, 3.82; 95% CI, 3.66 to 3.99). When anxiety and other disorders were included, the rate of mental illness was 39.3% in the top 1% compared to 21.3% (AOR, 2.39; 95% CI, 2.34 to 2.45). CONCLUSIONS: A high burden of mental illness and addiction among HCUs warrants its consideration in the design and delivery of services targeting HCUs.

Identifying factors associated with high use of acute care in Canada: a population-based retrospective study.

OBJECTIVES: To determine demographic, socioeconomic, and clinical factors associated with being high-cost users (HCUs) in adult patients (≥ 18 years) who received acute care in Canada. RESEARCH DESIGN: We conducted a retrospective study among adults who had at least one encounter with acute care facilities each year from 2011 to 2014 using national linked data sets. We defined HCUs as patients on the top 10% of the highest acute care cost users in the province, where the care was provided. Risk factors associated with being HCUs were identified using multilevel logistic regression. Provincial variations of identified risk factors were examined using logistic regression. Sensitivity analyses were also performed to investigate the influences of using different metrics and different thresholds to define high system users, missing data, and the inclusion of interaction terms on the study results. RESULTS: Between 2011 and 2014, a total of 3,891,410 patients with 6,017,430 hospitalizations were included. Patients who were male [odds ratio (OR), 1.60; 95% confidence interval (CI) 1.59-1.61], with low incomes [OR 1.42; 95% CI 1.41-1.43), with higher comorbidity score (OR 1.41; 95% CI 1.40-1.41] and older [OR 1.18; 95% CI 1.17-1.18] were more likely to be acute care HCUs. Significant interactions existed between comorbidity score and age/sex/income status. Across provinces, the associations between socioeconomic factors and being HCUs has the largest variation. When using various high system users (HSUs) definitions, the impacts of living in rural area and being visible minority on the odds of being HSUs differ. CONCLUSIONS: A few demographic, socioeconomic, and clinical factors was associated with high acute care expenditures. The associations between included risk factors and being acute care HCUs vary across provinces and different definitions of high system users (HSUs).

Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.

OBJECTIVES: To optimise planning of public health services, the impact of high-cost users needs to be considered. However, most of the existing statistical models for costs do not include many clinical and social variables from administrative data that are associated with elevated health care resource use, and are increasingly available. This study aimed to use machine learning approaches and big data to predict high-cost users among people with cardiovascular disease (CVD). METHODS: We used nationally representative linked datasets in New Zealand to predict CVD prevalent cases with the most expensive cost belonging to the top quintiles by cost. We compared the performance of four popular machine learning models (L1-regularised logistic regression, classification trees, k-nearest neighbourhood (KNN) and random forest) with the traditional regression models. RESULTS: The machine learning models had far better accuracy in predicting high health-cost users compared with the logistic models. The harmony score F1 (combining sensitivity and positive predictive value) of the machine learning models ranged from 30.6% to 41.2% (compared with 8.6-9.1% for the logistic models). Previous health costs, income, age, chronic health conditions, deprivation, and receiving a social security benefit were among the most important predictors of the CVD high-cost users. CONCLUSIONS: This study provides additional evidence that machine learning can be used as a tool together with big data in health economics for identification of new risk factors and prediction of high-cost users with CVD. As such, machine learning may potentially assist with health services planning and preventive measures to improve population health while potentially saving healthcare costs.

Improving Prediction of High-Cost Health Care Users with Medical Check-Up Data.

Studies found that a small portion of the population spent the majority of health care resources, and they highlighted the importance of predicting high-cost users in the health care management and policy. Most prior research on high-cost user prediction models are based on diagnosis data with additional cost and health care utilization data to improve prediction accuracy. To further improve the prediction of high-cost users, researchers have been testing various new data sources such as self-reported health status data. In this study, we use three categories of medical check-up data, laboratory tests, self-reported medical history, and self-reported health behavior data to build high-cost user prediction models, and to assess the medical check-up features as predictors of high-cost users. Using three data-mining models, logistic regression, random forest, and neural network models, we show that under the diagnosis-based approach, medical check-up data marginally improve diagnosis-based prediction models. Under the cost-based approach, we find that medical check-up data improve cost-based prediction models marginally and medical check-up data can be a viable alternate data source to diagnosis data in predicting high-cost users.

The effect of socio-demographic factors on mental health and addiction high-cost use: a retrospective, population-based study in Saskatchewan.

OBJECTIVE: A small proportion of the population accounts for the majority of healthcare costs. Mental health and addiction (MHA) patients are consistently high-cost. We aimed to delineate factors amenable to public health action that may reduce high-cost use among a cohort of MHA clients in Saskatoon, Saskatchewan. METHODS: We conducted a population-based retrospective cohort study. Administrative health data from fiscal years (FY) 2009-2015, linked at the individual level, were analyzed (n = 129,932). The outcome of interest was ≥ 90th percentile of costs for each year under study ('persistent high-cost use'). Descriptive analyses were followed by logistic regression modelling; the latter excluded long-term care residents. RESULTS: The average healthcare cost among study cohort members in FY 2009 was ~ $2300; for high-cost users it was ~ $19,000. Individuals with unstable housing and hospitalization(s) had increased risk of persistent high-cost use; both of these effects were more pronounced as comorbidities increased. Patients with schizophrenia, particularly those under 50 years old, had increased probability of persistent high-cost use. The probability of persistent high-cost use decreased with good connection to a primary care provider; this effect was more pronounced as the number of mental health conditions increased. CONCLUSION: Despite constituting only 5% of the study cohort, persistent high-cost MHA clients (n = 6455) accounted for ~ 35% of total costs. Efforts to reduce high-cost use should focus on reduction of multimorbidity, connection to a primary care provider (particularly for those with more than one MHA), young patients with schizophrenia, and adequately addressing housing stability.

High-Cost Users of Prescription Drugs: A Population-Based Analysis from British Columbia, Canada.

OBJECTIVE: To examine variation in pharmaceutical spending and patient characteristics across prescription drug user groups. DATA SOURCES: British Columbia's population-based linked administrative health and sociodemographic databases (N = 3,460,763). STUDY DESIGN: We classified individuals into empirically derived prescription drug user groups based on pharmaceutical spending patterns outside hospitals from 2007 to 2011. We examined variation in patient characteristics, mortality, and health services usage and applied hierarchical clustering to determine patterns of concurrent drug use identifying high-cost patients. PRINCIPAL FINDINGS: Approximately 1 in 20 British Columbians had persistently high prescription costs for 5 consecutive years, accounting for 42 percent of 2011 province-wide pharmaceutical spending. Less than 1 percent of the population experienced discrete episodes of high prescription costs; an additional 2.8 percent transitioned to or from high-cost episodes of unknown duration. Persistent high-cost users were more likely to concurrently use multiple chronic medications; episodic and transitory users spent more on specialized medicines, including outpatient cancer drugs. Cluster analyses revealed heterogeneity in concurrent medicine use within high-cost groups. CONCLUSIONS: Whether low, moderate, or high, costs of prescription drugs for most individuals are persistent over time. Policies controlling high-cost use should focus on reducing polypharmacy and encouraging price competition in drug classes used by ordinary and high-cost users alike.