What Contributes Most to High Health Care Costs? Health Care Spending in High Resource Patients

Association Between Sleep Apnea Treatment and Health Care Resource Use in Patients With Atrial Fibrillation

BACKGROUND

Obstructive sleep apnea (OSA) contributes to the generation, recurrence, and perpetuation of atrial fibrillation, and it is associated with worse outcomes. Little is known about the economic impact of OSA therapy in atrial fibrillation. This retrospective cohort study assessed the impact of positive airway pressure (PAP) therapy adherence on health care resource use and costs in patients with OSA and atrial fibrillation.

METHODS AND RESULTS

Insurance claims data for ≥1 year before sleep testing and 2 years after device setup were linked with objective PAP therapy use data. PAP adherence was defined from an extension of the US Medicare 90-day definition. Inverse probability of treatment weighting was used to create covariate-balanced PAP adherence groups to mitigate confounding. Of 5867 patients (32% women; mean age, 62.7 years), 41% were adherent, 38% were intermediate, and 21% were nonadherent. Mean±SD number of all-cause emergency department visits (0.61±1.21 versus 0.77±1.55 [P=0.023] versus 0.95±1.90 [P<0.001]), all-cause hospitalizations (0.19±0.69 versus 0.24±0.72 [P=0.002] versus 0.34±1.16 [P<0.001]), and cardiac-related hospitalizations (0.06±0.26 versus 0.09±0.41 [P=0.023] versus 0.10±0.44 [P=0.004]) were significantly lower in adherent versus intermediate and nonadherent patients, as were all-cause inpatient costs ($2200±$8054 versus $3274±$12 065 [P=0.002] versus $4483±$16 499 [P<0.001]). All-cause emergency department costs were significantly lower in adherent and intermediate versus nonadherent patients ($499±$1229 and $563±$1292 versus $691±$1652 [P<0.001 and P=0.002], respectively).

CONCLUSIONS

These data suggest clinical and economic benefits of PAP therapy in patients with concomitant OSA and atrial fibrillation. This supports the value of diagnosing and managing OSA and highlights the need for strategies to enhance PAP adherence in this population.

Healthcare resource utilisation and costs in patients with treated obstructive sleep apnea

Obstructive sleep apnea (OSA) is a highly prevalent yet underdiagnosed disease that creates a large economic burden on the United States healthcare system. In this retrospective study, we tested the hypothesis that adherence to positive airway pressure (PAP) therapy, the 'gold standard' treatment for OSA, is associated with reduced healthcare resource utilisation and costs. We linked de-identified payer-sourced medical claims and objective PAP usage data for patients newly diagnosed with OSA. Inverse probability of treatment weighting was used to create balanced groups of patients who were either adherent, intermediately adherent, or non-adherent to PAP therapy. From a sample of 179,542 patients (average age 52.5 years, 61% male), 37% were adherent, 40% intermediate, and 23% non-adherent. During the first year, PAP adherence was significantly associated with fewer emergency room visits (mean [SD] adherent: 0.39 [1.20] versus intermediate: 0.47 [1.30], p < 0.001; versus non-adherent: 0.54 [1.44], p < 0.001), all-cause hospitalisations (mean [SD] adherent: 0.09 [0.43] versus intermediate: 0.12 [0.51], p < 0.001; versus non-adherent: 0.13 [0.55], p < 0.001), and lower total costs (mean [SD] adherent $5874 [8045] versus intermediate $6523 [9759], p < 0.001; versus non-adherent $6355 [10,517], p < 0.001). Results were similar in the second year of PAP use. These results provide additional evidence from a large, diverse sample to support the diagnosis and treatment of OSA and encourage long-term adherence to PAP therapy.

Specialty-type and state-level variation in paroxetine use among older adult patients

Introduction

Paroxetine is an older "selective" serotonin reuptake inhibitor (SSRI) that is notable for its lack of selectivity, resulting in a cholinergic adverse-effect profile, especially among older adults (65+).

Methods

Paroxetine prescription rates and costs per state were ascertained from the Medicare Specialty Utilization and Payment Data. States' annual prescription rate, corrected per thousand Part D enrollees, outside 95% confidence interval were considered significantly different from the average.

Results

There was a steady decrease in paroxetine prescriptions (-34.52%) and spending (-16.69%) from 2015-2020 but a consistent, five-fold state-level difference. From 2015-2020, Kentucky (194.9, 195.3, 182.7, 165.1, 143.3, 132.5) showed significantly higher prescriptions rates relative to the national average, and Hawaii (42.1, 37.9, 34.3, 31.7, 27.7, 26.6) showed significantly lower prescription rates. North Dakota was often a frequent elevated prescriber of paroxetine (2016: 170.7, 2018: 143.3), relative to the average. Neuropsychiatry and geriatric medicine frequently prescribed the largest amount of paroxetine prescriptions, relative to the number of providers in that specialty, from 2015-2020.

Discussion

Despite the American Geriatrics Society prohibition against paroxetine use in the older adults and many effective treatment alternatives, paroxetine was still commonly used in this population, especially in Kentucky and North Dakota and by neuropsychiatry and geriatric medicine. These findings provide information on the specialty types and states where education and policy reform would likely have the greatest impact on improving adherence to the paroxetine prescription recommendations.

High-Cost Users of Prescription Drugs: National Health Insurance Data from South Korea

IMPORTANCE

In OECD countries, pharmaceutical spending reached around 800 billion USD in 2013, accounting for about 20% of total spending in the retail sector. Pharmaceutical expenditures are steadily increasing in South Korea, necessitating strategies to promote efficiency.

OBJECTIVE

This study investigated factors associated with high-cost users (HCUs), who account for the majority of outpatient prescriptions in the total South Korean population. The top 20 frequently prescribed therapeutic subgroups were also investigated.

DESIGN

This is an observational study performed using health insurance claims data in 2019.

PARTICIPANTS

In total, 44,744,632 people (including 6,806,339 aged 65 years or older) who were prescribed outpatient medications were included.

MAIN MEASURES

HCUs were defined as those for whom prescription drug costs were in the top 5%. Multivariate logistic regression analysis was performed using factors including age, insurance type, number of prescription drugs, outpatient visit days, prescription treatment days, and chronic diseases.

RESULTS

HCUs accounted for 3.6 million (5% of the total population) and 1.4 million (21.1% of those 65 years or older). Furthermore, 4.1% of HCUs in the total population had few comorbidities. Male sex, older age, insurance (Medical Aid), comorbidities, chronic diseases, number of prescription drugs, outpatient visit days, and prescription days were all associated with an increased probability of being an HCU. The highest spending was found for B01 (antithrombotic agents) with 0.4 billion USD, followed by C10 (lipid-modifying agents) and A10 (drugs used in diabetes). The proportion of spending for HCUs among the general population was highest in L01 (antineoplastic agents), at 98.2%, and L04 (immunosuppressants), at 87.8%, whereas among the elderly, the highest proportions were found for B01 (antithrombotic agents), at 44.5%, and N06 (antidepressants), at 44.3%.

CONCLUSION

Age and multiple chronic conditions were strongly associated with HCUs, and it seems necessary to reduce drug prescriptions in patients without complex comorbidities. Several measures should target those without multiple chronic conditions who are nonetheless HCUs.

Gap in Willingness and Access to Video Visit Use Among Older High-risk Veterans: Cross-sectional Study

Background

The recent shift to video care has exacerbated disparities in health care access, especially among high-need, high-risk (HNHR) adults. Developing data-driven approaches to improve access to care necessitates a deeper understanding of HNHR adults’ attitudes toward telemedicine and technology access.

Objective

This study aims to identify the willingness, access, and ability of HNHR veterans to use telemedicine for health care.

Methods

WWe designed a questionnaire conducted via mail or telephone or in person. Among HNHR veterans who were identified using predictive modeling with national Veterans Affairs data, we assessed willingness to use video visits for health care, access to necessary equipment, and comfort with using technology. We evaluated physical health, including frailty, physical function, performance of activities of daily living (ADL) and instrumental ADL (IADL); mental health; and social needs, including Area Deprivation Index, transportation, social support, and social isolation.

Results

The average age of the 602 HNHR veteran respondents was 70.6 (SD 9.2; range 39-100) years; 99.7% (600/602) of the respondents were male, 61% (367/602) were White, 36% (217/602) were African American, 17.3% (104/602) were Hispanic, 31.2% (188/602) held at least an associate degree, and 48.2% (290/602) were confident filling medical forms. Of the 602 respondents, 327 (54.3%) reported willingness for video visits, whereas 275 (45.7%) were unwilling. Willing veterans were younger (P<.001) and more likely to have an associate degree (P=.002), be health literate (P<.001), live in socioeconomically advantaged neighborhoods (P=.048), be independent in IADLs (P=.02), and be in better physical health (P=.04). A higher number of those willing were able to use the internet and email (P<.001). Of the willing veterans, 75.8% (248/327) had a video-capable device. Those with video-capable technology were younger (P=.004), had higher health literacy (P=.01), were less likely to be African American (P=.007), were more independent in ADLs (P=.005) and IADLs (P=.04), and were more adept at using the internet and email than those without the needed technology (P<.001). Age, confidence in filling forms, general health, and internet use were significantly associated with willingness to use video visits.

Conclusions

Approximately half of the HNHR respondents were unwilling for video visits and a quarter of those willing lacked requisite technology. The gap between those willing and without requisite technology is greater among older, less health literate, African American veterans; those with worse physical health; and those living in more socioeconomically disadvantaged neighborhoods. Our study highlights that HNHR veterans have complex needs, which risk being exacerbated by the video care shift. Although technology holds vast potential to improve health care access, certain vulnerable populations are less likely to engage, or have access to, technology. Therefore, targeted interventions are needed to address this inequity, especially among HNHR older adults.

Discharge Prediction for Patients Undergoing Inpatient Surgery: Development and validation of the DEPENDENSE score

BACKGROUND

A substantial proportion of patients undergoing inpatient surgery each year is at risk for postoperative institutionalization and loss of independence. Reliable individualized preoperative prediction of adverse discharge can facilitate advanced care planning and shared decision making.

METHODS

Using hospital registry data from previously home-dwelling adults undergoing inpatient surgery, we retrospectively developed and externally validated a score predicting adverse discharge. Multivariable logistic regression analysis and bootstrapping were used to develop the score. Adverse discharge was defined as in-hospital mortality or discharge to a skilled nursing facility. The model was subsequently externally validated in a cohort of patients from an independent hospital.

RESULTS

In total, 106 164 patients in the development cohort and 92 962 patients in the validation cohort were included, of which 16 624 (15.7%) and 7717 (8.3%) patients experienced adverse discharge, respectively. The model was predictive of adverse discharge with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI 0.87-0.88) in the development cohort and an AUC of 0.86 (95% CI 0.86-0.87) in the validation cohort.

CONCLUSION

Using preoperatively available data, we developed and validated a prediction instrument for adverse discharge following inpatient surgery. Reliable prediction of this patient centered outcome can facilitate individualized operative planning to maximize value of care.

Supervised mixture of experts models for population health

We propose a machine learning driven approach to derive insights from observational healthcare data to improve public health outcomes. Our goal is to simultaneously identify patient subpopulations with differing health risks and to find those risk factors within each subpopulation. We develop two supervised mixture of experts models: a Supervised Gaussian Mixture model (SGMM) for general features and a Supervised Bernoulli Mixture model (SBMM) tailored to binary features. We demonstrate the two approaches on an analysis of high cost drivers of Medicaid expenditures for inpatient stays. We focus on the three diagnostic categories that accounted for the highest percentage of inpatient expenditures in New York State (NYS) in 2016. When compared with state-of-the-art learning methods (random forests, boosting, neural networks), our approaches provide comparable prediction performance while also extracting insightful subpopulation structure and risk factors. For problems with binary features the proposed SBMM provides as good or better performance than alternative methods while offering insightful explanations. Our results indicate the promise of such approaches for extracting population health insights from electronic health care records.

Prediction of health care expenditure increase: how does pharmacotherapy contribute?

BACKGROUND

Rising health care costs are a major public health issue. Thus, accurately predicting future costs and understanding which factors contribute to increases in health care expenditures are important. The objective of this project was to predict patients healthcare costs development in the subsequent year and to identify factors contributing to this prediction, with a particular focus on the role of pharmacotherapy.

METHODS

We used 2014-2015 Swiss health insurance claims data on 373'264 adult patients to classify individuals' changes in health care costs. We performed extensive feature generation and developed predictive models using logistic regression, boosted decision trees and neural networks. Based on the decision tree model, we performed a detailed feature importance analysis and subgroup analysis, with an emphasis on drug classes.

RESULTS

The boosted decision tree model achieved an overall accuracy of 67.6% and an area under the curve-score of 0.74; the neural network and logistic regression models performed 0.4 and 1.9% worse, respectively. Feature engineering played a key role in capturing temporal patterns in the data. The number of features was reduced from 747 to 36 with only a 0.5% loss in the accuracy. In addition to hospitalisation and outpatient physician visits, 6 drug classes and the mode of drug administration were among the most important features. Patient subgroups with a high probability of increase (up to 88%) and decrease (up to 92%) were identified.

CONCLUSIONS

Pharmacotherapy provides important information for predicting cost increases in the total population. Moreover, its relative importance increases in combination with other features, including health care utilisation.

Chronic Diseases in High-Cost Users of Hospital, Primary Care, and Prescription Medication in the Capital Region of Denmark

BACKGROUND

A small proportion of patients account for the majority of health care costs. This group is often referred to as high-cost users (HCU). A frequently described characteristic of HCU is chronic disease. Yet, there is a gap in understanding the economic burden of chronic diseases associated with HCU to different types of health care services.

OBJECTIVE

To analyze which frequent chronic diseases have the strongest association with HCU overall, and HCU in hospital, primary care, and prescription medication.

DESIGN

This is a register-based observational study on Danish health service costs for various diseases in different medical settings.

PARTICIPANTS

A total of 1,350,677 individuals aged ≥ 18 years living in the Capital Region of Denmark by 1 January 2012 were included.

MAIN MEASURES

Chronic diseases, costs, and sociodemographic data were extracted from the nationwide registers, including data from hospitals, primary care, and medicine consumption. These information were merged on an individual level.

KEY RESULTS

Cancer, mental disorders except depression, and heart diseases have the strongest association with HCU overall. Mental disorders except depression were in the three diseases most prevalent in HCU in all the three health care services.

CONCLUSIONS

Our results show that the chronic diseases that have the strongest association with HCU differ between different types of health care services. Our findings may be helpful in informing future policies about health care organization and may guide to different prevention, treatment, and rehabilitation strategies that could lessen the burden in the hospital.

Incremental healthcare utilisation and costs among new senior high-cost users in Ontario, Canada: a retrospective matched cohort study

OBJECTIVES

To describe healthcare use and spending before and on becoming a new (incident) senior high-cost user (HCU) compared with senior non-HCUs; to estimate the incremental costs, overall and by service category, attributable to HCU status; and to quantify its monetary impact on the provincial healthcare budget in Ontario, Canada.

DESIGN

We conducted a retrospective, population-based comparative cohort study using administrative healthcare records. Incremental healthcare utilisation and costs were determined using the method of recycled predictions allowing adjustment for preincident and incident year values, and covariates. Estimated budget impact was computed as the product of the mean annual total incremental cost and the number of senior HCUs.

PARTICIPANTS

Incident senior HCUs were defined as Ontarians aged ≥66 years who were in the top 5% of healthcare cost users during fiscal year 2013 (FY2013) but not during FY2012. The incident HCU cohort was matched with senior non-HCUs in a ratio of 1 HCU:3 non-HCU.

RESULTS

Senior HCUs (n=175 847) reached the annual HCU threshold of CAD$10 192 through different combinations of incurred costs. Although HCUs had higher healthcare utilisation and costs at baseline, HCU status was associated with a substantial spike in both, with prolonged hospitalisations playing a major role. Twelve per cent of HCUs reached the HCU expenditure threshold without hospitalisation. Compared with non-HCUs (n=5 27 541), HCUs incurred an additional CAD$25 527 per patient in total healthcare costs; collectively CAD$4.5 billion or 9% of the 2013 Ontario healthcare budget. Inpatient care had the highest incremental costs: CAD$13 427, 53% of the total incremental spending.

CONCLUSIONS

Costs attributable to incident senior HCU status accounted for almost 1/10 of the provincial healthcare budget. Prolonged hospitalisations made a major contribution to the total incremental costs. A subgroup of patients that became HCU without hospitalisation requires further investigation.

Characterization of high healthcare utilizer groups using administrative data from an electronic medical record database

Background

High utilizers (HUs) are a small group of patients who impose a disproportionately high burden on the healthcare system due to their elevated resource use. Identification of persistent HUs is pertinent as interventions have not been effective due to regression to the mean in majority of patients. This study will use cost and utilization metrics to segment a hospital-based patient population into HU groups.

Methods

The index visit for each adult patient to an Academic Medical Centre in Singapore during 2006 to 2012 was identified. Cost, length of stay (LOS) and number of specialist outpatient clinic (SOC) visits within 1 year following the index visit were extracted and aggregated. Patients were HUs if they exceeded the 90th percentile of any metric, and Non-HU otherwise. Seven different HU groups and a Non-HU group were constructed. The groups were described in terms of cost and utilization patterns, socio-demographic information, multi-morbidity scores and medical history. Logistic regression compared the groups’ persistence as a HU in any group into the subsequent year, adjusting for socio-demographic information and diagnosis history.

Results

A total of 388,162 patients above the age of 21 were included in the study. Cost-LOS-SOC HUs had the highest multi-morbidity and persistence into the second year. Common conditions among Cost-LOS and Cost-LOS-SOC HUs were cardiovascular disease, acute cerebrovascular disease and pneumonia, while most LOS and LOS-SOC HUs were diagnosed with at least one mental health condition. Regression analyses revealed that HUs across all groups were more likely to persist compared to Non-HUs, with stronger relationships seen in groups with high SOC utilization. Similar trends remained after further adjustment.

Conclusion

HUs of healthcare services are a diverse group and can be further segmented into different subgroups based on cost and utilization patterns. Segmentation by these metrics revealed differences in socio-demographic characteristics, disease profile and persistence. Most HUs did not persist in their high utilization, and high SOC users should be prioritized for further longitudinal analyses. Segmentation will enable policy makers to better identify the diverse needs of patients, detect gaps in current care and focus their efforts in delivering care relevant and tailored to each segment.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4239-2) contains supplementary material, which is available to authorized users.

Systematic review of high-cost patients' characteristics and healthcare utilisation

OBJECTIVES

To investigate the characteristics and healthcare utilisation of high-cost patients and to compare high-cost patients across payers and countries.

DESIGN

Systematic review.

DATA SOURCES

PubMed and Embase databases were searched until 30 October 2017.

ELIGIBILITY CRITERIA AND OUTCOMES

Our final search was built on three themes: 'high-cost', 'patients', and 'cost' and 'cost analysis'. We included articles that reported characteristics and utilisation of the top-X% (eg, top-5% and top-10%) patients of costs of a given population. Analyses were limited to studies that covered a broad range of services, across the continuum of care. Andersen's behavioural model was used to categorise characteristics and determinants into predisposing, enabling and need characteristics.

RESULTS

The studies pointed to a high prevalence of multiple (chronic) conditions to explain high-cost patients' utilisation. Besides, we found a high prevalence of mental illness across all studies and a prevalence higher than 30% in US Medicaid and total population studies. Furthermore, we found that high costs were associated with increasing age but that still more than halve of high-cost patients were younger than 65 years. High costs were associated with higher incomes in the USA but with lower incomes elsewhere. Preventable spending was estimated at maximally 10% of spending. The top-10%, top-5% and top-1% high-cost patients accounted for respectively 68%, 55% and 24% of costs within a given year. Spending persistency varied between 24% and 48%. Finally, we found that no more than 30% of high-cost patients are in their last year of life.

CONCLUSIONS

High-cost patients make up the sickest and most complex populations, and their high utilisation is primarily explained by high levels of chronic and mental illness. High-cost patients are diverse populations and vary across payer types and countries. Tailored interventions are needed to meet the needs of high-cost patients and to avoid waste of scarce resources.

Strengthening value-based medication management in a free clinic for the uninsured: Quality interventions aimed at reducing costs and enhancing adherence

Skyrocketing costs of prescription medications in the USA pose a significant threat to the financial viability of safety net clinics that opt to supply medications at low to no out-of-pocket costs to patients. At the East Harlem Health Outreach Partnership clinic of the Icahn School of Medicine at Mount Sinai, a physician-directed student-run comprehensive primary care clinic for uninsured adults of East Harlem, expenditures on pharmaceuticals represent nearly two-thirds of annual costs. The practice of minimising costs while maintaining quality, referred to as high-value care, represents a critical cost-saving opportunity for safety net clinics as well as for more economical healthcare in general. In this paper, we discuss a series of quality improvement initiatives aimed at reducing pharmacy-related expenditures through two distinct yet related mechanisms: (A) promoting value-conscious prescribing by providers and (B) improving patient adherence to medication regimens. Interventions aimed at promoting value-conscious prescribing behaviour included blacklisting a costly medication on our clinic’s formulary and adding a decision tree in our mobile clinician reference application to promote value-conscious prescribing. Interventions targeted to improving patient adherence involved an automated text messaging system with English and Spanish refill reminders to encourage timely pick-up of medication refills. As a result of these processes, the free clinic experienced a 7.3%, or $3768, reduction in annual pharmacy costs. Additionally, medication adherence in patients with diabetes on oral antihyperglycaemic medications increased from 55% to 67%. Simultaneous patient-based and provider-based interventions may be broadly applicable to addressing rising pharmacy costs in healthcare across the USA.

Novel Targeted Therapies for Inflammatory Bowel Disease

Our growing understanding of the immunopathogenesis of inflammatory bowel disease (IBD) has opened new avenues for developing targeted therapies. These advances in treatment options targeting different mechanisms of action offer new hope for personalized management. In this review we highlight emerging novel and easily administered therapeutics that may be viable candidates for the management of IBD, such as antibodies against interleukin 6 (IL-6) and IL-12/23, small molecules including Janus kinase inhibitors, antisense oligonucleotide against SMAD7 mRNA, and inhibitors of leukocyte trafficking to intestinal sites of inflammation (e.g., sphingosine 1-phosphate receptor modulators). We also provide an update on the current status in clinical development of these new classes of therapeutics.