Predictor Of Health Care Costs Research Articles

Predicting the Cost of Health Care Services: A Comparison of Case-mix Systems and Comorbidity Indices That Use Administrative Data.

Case-mix systems and comorbidity indices aggregate clinical information about patients over time and are used to characterize need for health care services. These tools were validated for their original purpose, but those purposes are varied, and they have not been compared directly in the context of predicting costs of health care services. To compare predictions of next-year health care service costs across 4 tools, including: the Johns Hopkins Adjusted Clinical Groups (ACG), the Elixhauser Comorbidity Index, Charlson-Deyo Comorbidity Index, and the Canadian Institute for Health Information (CIHI) population grouper. British Columbia administrative data from fiscal years 2012-2013 were used to generate case-mix variables and the comorbidity indices. Outcome variables include next-year (2013-2014) total, physician, acute care, and pharmaceutical costs, Outcomes were modeled using 2-part models. Performance was compared using adjusted R, root mean squared error, and mean absolute error using the predicted and the actual next-year cost. Models including the CIHI grouper (239 conditions) and ACG system had similar performance in most cost categories and slightly better fit than Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). Adding a dummy variable for nonusers in the models for CCI and ECI increased R values slightly. All these systems have empirical support for use in predicting health care costs, despite in some cases being developed for other purposes. No system is particularly effective at predicting next-year acute care cost, likely because acute events are often by definition unexpected. The freely available ECI and CCI comorbidity indices implemented using the highest-performing methods developed here may be a good choice in many circumstances.

94. Cost analysis of primary single-level lumbar discectomies

BACKGROUND CONTEXT Improving value in surgical care requires a detailed understanding of the true costs of surgical and operating room resources. PURPOSE In this study, we sought to identify predictors of health care costs and operating room resource use for patients undergoing initial single-level discectomies using a unique institutional cost dataset from the value driven outcomes (VDO) program developed by the University of Utah. STUDY DESIGN/SETTING A retrospective cost analysis of primary single-level lumbar discectomies. PATIENT SAMPLE A total of 622 patients undergoing initial single-level lumbar discectomy between January 2014 to April 2016 at the University of Utah Orthopaedic Center. OUTCOME MEASURES Health care costs, clinical length of stay, and operating room time. METHODS Univariate and multivariate generalized linear models (GLM) performed to identify predictors of health care costs, clinical length of stay (LOS, days) and operating room time (OR, minutes) using a unique costing tool. Modified park test procedure was implemented to determine GLM error distribution specifications and standard errors were clustered by provider. Cost outcomes were normalized using mean costs for a patient with normal body mass index (BMI) and a healthy American Society of Anesthesiologists (ASA) classification system. Average marginal effects in multivariate analysis were reported as percentage of normalized costs. RESULTS Advanced age, male gender, Hispanic, Black, unemployment, and being retired were significant positive predictors of costs in univariate analysis. Asian, Native American, outpatient procedures, and being a student were significant negative predictors of costs. Univariate analysis also showed that obesity, higher ASA class and insurance status were also positive and significant predictors of costs. In multivariate analysis, we found that obesity led to higher average marginal total direct (9%), total facility (15%), and facility OR costs (22%), as well as an average marginal effect of 24 more OR minutes per surgery. Conversely, being underweight was associated with lower average marginal total direct (-23%), total non-facility (-54%), total facility (-8%), facility OR costs (-15%), as well as lower clinical LOS (-.42 days), and 18 less OR minutes. While being overweight was not significantly associated with greater total direct costs, it was associated with higher total facility (8%), and facility OR costs (12%), with an average marginal effect of 11 more OR minutes per surgery. Age was associated with a longer LOS but not with OR costs in multivariate analysis. As expected, outpatient surgical costs, LOS and OR time were significantly lower than inpatient procedures. Patients with incapacitating disease ASA Class had the highest predicted OR time in GLM estimates with an average marginal effect of 28 minutes, while severe systematic disease was associated with greater total and nonfacility costs. In addition, Medicare patients had higher facility costs (14%) compared to privately insured patients. CONCLUSIONS Although many of our observed variables were significant predictors of costs in univariate analysis, multivariate GLM estimates showed that the main variables determining cost and operating room time and resource use were obesity (BMI), general health (ASA class), Medicare status and advanced age, and whether the surgery was an outpatient or inpatient procedure. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

The costs of dementia subtypes to California Medicare fee-for-service, 2015

IntroductionDementia is among the costliest of medical conditions, but it is not known how these costs vary by dementia subtype. MethodsThe effect of dementia diagnosis subtype on direct health care costs and utilization was estimated using 2015 California Medicare fee-for-service data. Potential drivers of increased costs in Lewy body dementia (LBD), in comparison to Alzheimer's disease, were tested. Results3,001,987 Medicare beneficiaries were identified, of which 8.2% had a dementia diagnosis. Unspecified dementia was the most common diagnostic category (59.6%), followed by Alzheimer's disease (23.2%). LBD was the costliest subtype to Medicare, on average, followed by vascular dementia. The higher costs in LBD were explained in part by falls, urinary incontinence or infection, depression, anxiety, dehydration, and delirium. DiscussionDementia subtype is an important predictor of health care costs. Earlier identification and targeted treatment might mitigate the costs associated with co-occurring conditions in LBD.

Limitations of P-Values and R-squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment

ABSTRACT Stepwise regression building procedures are commonly used applied statistical tools, despite their well-known drawbacks. While many of their limitations have been widely discussed in the literature, other aspects of the use of individual statistical fit measures, especially in high-dimensional stepwise regression settings, have not. Giving primacy to individual fit, as is done with p-values and R 2, when group fit may be the larger concern, can lead to misguided decision making. One of the most consequential uses of stepwise regression is in health care, where these tools allocate hundreds of billions of dollars to health plans enrolling individuals with different predicted health care costs. The main goal of this “risk adjustment” system is to convey incentives to health plans such that they provide health care services fairly, a component of which is not to discriminate in access or care for persons or groups likely to be expensive. We address some specific limitations of p-values and R 2 for high-dimensional stepwise regression in this policy problem through an illustrated example by additionally considering a group-level fairness metric.

Assessing the Impact of Body Mass Index Information on the Performance of Risk Adjustment Models in Predicting Health Care Costs and Utilization.

Using electronic health records (EHRs) for population risk stratification has gained attention in recent years. Compared with insurance claims, EHRs offer novel data types (eg, vital signs) that can potentially improve population-based predictive models of cost and utilization. To evaluate whether EHR-extracted body mass index (BMI) improves the performance of diagnosis-based models to predict concurrent and prospective health care costs and utilization. We used claims and EHR data over a 2-year period from a cohort of continuously insured patients (aged 20-64 y) within an integrated health system. We examined the addition of BMI to 3 diagnosis-based models of increasing comprehensiveness (ie, demographics, Charlson, and Dx-PM model of the Adjusted Clinical Group system) to predict concurrent and prospective costs and utilization, and compared the performance of models with and without BMI. The study population included 59,849 patients, 57% female, with BMI class I, II, and III comprising 19%, 9%, and 6% of the population. Among demographic models, R improvement from adding BMI ranged from 61% (ie, R increased from 0.56 to 0.90) for prospective pharmacy cost to 29% (1.24-1.60) for concurrent medical cost. Adding BMI to demographic models improved the prediction of all binary service-linked outcomes (ie, hospitalization, emergency department admission, and being in top 5% total costs) with area under the curve increasing from 2% (0.602-0.617) to 7% (0.516-0.554). Adding BMI to Charlson models only improved total and medical cost predictions prospectively (13% and 15%; 4.23-4.79 and 3.30-3.79), and also improved predicting all prospective outcomes with area under the curve increasing from 3% (0.649-0.668) to 4% (0.639-0.665; and, 0.556-0.576). No improvements in prediction were seen in the most comprehensive model (ie, Dx-PM). EHR-extracted BMI levels can be used to enhance predictive models of utilization especially if comprehensive diagnostic data are missing.

How AI Plays Its Tricks: Interpreting the Superior Performance of Deep Learning-Based Approach in Predicting Healthcare Costs

Artificial Intelligence, especially deep learning based approaches, has been criticized for its \black-box approach in recent years. The lack of interpretability of their performance makes people uncomfortable when applying these models in critical situations. In this study, we aim to contribute to the interpretability of the deep learning model in predicting healthcare costs. We propose to use long short-term memory based recurrent neural network to incorporate the sequential information for more accurate healthcare cost predictions. We first compare the performance of our deep learning approach with traditional machine learning methods including linear regression, lasso regression, ridge regression, and random forest. Among all the methods, deep learning shows the best performance. The superior performance of the deep learning model is further confirmed in subgroup analyses of patients. We then propose a novel interpretation method to examine how the deep learning model performs differently from other methods when facing fluctuations in the monthly costs. We find that while most traditional prediction models are getting worse with greater fluctuation in the data, the deep learning model can incorporate the fluctuation information and gain in prediction accuracy. Our work makes important contributions to the interpretability of deep learning models for more accurate prediction of healthcare costs.

The critical importance of going beyond administrative data for health systems planning and integration

Introduction : Modern health care systems strive to be data driven, meaning they maximize the use of administrative data for evaluation and planning. However, the use of traditional administrative data has not had a major impact on health care costs nor clinical outcomes. Alberta, Canada is a province of 4.1 million persons with a single, public health care system. This system, where all clinical and utilization variables are captured within an entire population, afforded us the opportunity to assess whether the application of administrative data could lead to enhanced integration, reduced system costs and better care. Methods : We used the databases of the provincial government and its healthcare delivery arm (Alberta Health Services), to determine whether administrative data would provide sufficient information to drive integration across the health care system, thereby reducing costs and improving care. Administrative data available included all encounters with the health care system such as physician visits, hospitalizations, emergency room visits, diagnostic testing, and all other clinical costs except for pharmaceuticals. Both parametric statistics and machine learning techniques were used to assess the data and to attempt predictive modelling from the same. Subsequently, mixed-methods techniques were used to determine the validity of statistical assessments and models. Results : Administrative data identified the frail elderly and medically complex elderly as the major demographics driving healthcare costs. Further analysis suggested disease-related drivers of cost such as hypertension and diabetes on an outpatient basis, and heart failure and chronic obstructive pulmonary disease on an acute care basis. However, predictive modelling techniques were disappointing, even when machine learning was used. Moreover, groups identified as high cost or high system utilizers in any given time period turned out to have a 75% chance of being low cost/low users on a go-forward basis. When we explored root causes for health care utilization and costs, both by interviews and by adding in social data, we found that social factors were huge determinants and predictors of health care costs and utilization. For example, loneliness scores in a large sample of seniors correlated with future health care utilization and costs at an R2 value of 0.98. By contrast, the best machine learning models gave no more than a 0.6 correlation coefficient between administrative variables and subsequent costs/utilization. Finally, interviews with both patients and health care professionals confirmed that root causes of health care costs could be attributed to both an inadequacy of community social supports as well as to a lack of attachment and continuity with a primary care provider. Individual costs varied by five-fold in communities with strong social supports versus those without. Conclusion : The exclusive use of administrative data would have lead our healthcare system down a misleading path of allocating resources to better management and integration of chronic diseases between primary, acute and specialty care. Instead, by adding social variables as well as patient/provider narratives to our planning, a completely different emphasis resulted: strengthening primary care and enhancing social supports in the community.

Patient Activation Changes as a Potential Signal for Changes in Health Care Costs: Cohort Study of US High-Cost Patients.

Programs to improve quality of care and lower costs for the highest utilizers of health services are proliferating, yet such programs have difficulty demonstrating cost savings. In this study, we explore the degree to which changes in Patient Activation Measure (PAM) levels predict health care costs among high-risk patients. De-identified claims, demographic data, and serial PAM scores were analyzed on 2155 patients from multiple medical groups engaged in an existing Center for Medicare and Medicaid Innovation-funded intervention over 3years designed to activate and improve care coordination for high-risk patients. In this prospective cohort study, four levels of PAM (from low to high) were used as the main predictor variable. We fit mixed linear models for log10 of allowed charges in follow-up periods in relation to change in PAM, controlling for baseline PAM, baseline costs, age, sex, income, and baseline risk score. Total allowed charges were derived from claims data for the cohort. PAM scores were from a separate database managed by the local practices. A single PAM level increase was associated with 8.3% lower follow-up costs (95% confidence interval 2.5-13.2%). These findings contribute to a growing evidence base that the change in PAM score could serve as an early signal indicating the impact of interventions designed for high-cost, high-needs patients.

CE2 - COST-EFFECTIVENESS OF A NEW SINGLE INHALER TRIPLE THERAPY VERSUS A DUAL INHALED CORTICOSTEROID PLUS LONG-ACTING BETA AGONIST IN PATIENTS WITH CHRONIC OBSTRUCTIVE PULMONARY DISEASE (COPD) IN SPAIN

To evaluate the cost-effectiveness of once-daily fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) 92/55/22μg compared with twice-daily budesonide/formoterol (BUD/FOR) 320/9μg among patients with symptomatic COPD at risk of exacerbations from the Spanish National Healthcare System (NHS) perspective. The validated GALAXY COPD disease progression model used linked risk equations to predict healthcare costs, quality adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICER), over a three-year time horizon. Baseline characteristics for the Spanish population were extracted from the literature and FULFIL study (NCT02345161), which compared FF/UMEC/VI with BUD/FOR, in patients with COPD. Treatment effects were based on the FULFIL intent-to-treat 24-week data at incremental FEV1 of 171mL. For the base case, the duration of treatment effect was set to 3 years. Direct healthcare costs (€, 2018) of treatments, exacerbations and COPD management were informed by Spanish public sources and published literature. A 3% discount rate for costs and benefits was applied. One-way and probabilistic (PSA) sensitivity analyses were performed. Compared with BUD/FOR, FF/UMEC/VI treatment resulted in fewer moderate and severe exacerbations (2.206 and 0.322 vs 1.794 and 0.191) and a mean (95% CI) incremental cost and health benefit of €739 (€348, €1,091) and 0.1 (0.08, 0.12) QALYs, resulting in an ICER of €7,387 per QALY gained. The ICER was most sensitive to size of exacerbation treatment effect, duration of treatment effect, varying value (±20%) of resource use/event cost inputs, and time horizon. In the PSA, in 95% of 1000 simulations, the ICER fell between €3,336 and €12,176 for FF/UMEC/VI versus BUD/FOR, confirming robustness of the results. The probability of FF/UMEC/VI being cost-effective against BUD/FOR was 100% above a willingness-to-pay threshold of €14,000 per QALY gained. According to the accepted ICER threshold of €30,000 in Spain, FF/UMEC/VI was a cost-effective treatment option when compared with BUD/FOR, across all scenarios and sensitivity analyses.

Multipoint Pacing Reduces Predicted Health Care Costs in the Majority of Cardiac Resynchronization Therapy Patients

Introduction The Abbott MultiPoint Pacing (MPP) IDE trial showed safety and efficacy of MPP in cardiac resynchronization therapy patients. Patients programmed to pace from anatomically distant poles (MPP-AS) responded at significantly higher rates than those programmed to other MPP settings. Hypothesis MPP-AS is associated with reduced heart failure-related (HFR) healthcare utilization compared to quadripolar biventricular pacing (QUAD-BiV). Methods All QUAD-BiV and MPP-AS patients from the MPP IDE trial were included. Costs were modeled using a Markov cohort model (1-month cycle length, 10-year time horizon). Rates of HFR hospitalization, emergency room visits, outpatient visits, office visits and death were calculated from the MPP IDE trial. Devices programmed to QUAD-BiV were assumed to have a 7-year battery life, and MPP-AS was assumed to shorten battery life by 9 months. Costs were estimated for each visit type using the Marketscan Database (2012-2016, updated to 2017 using Medical CPI). The simulation was repeated 10,000 times. Results In the MPP IDE trial, 52 patients had MPP-AS programmed and 232 had QUAD-BiV. The MPP-AS group had 2 HFR non-emergency inpatient stays. The QUAD-BiV group had 10 HFR non-emergency inpatient stays, 4 HFR inpatient stays admitted via the ER, 2 HFR outpatient ER visits, 3 HFR outpatient hospitalizations and 3 HFR office visits. Mortality was assumed to be 7% for both groups. The MPP-AS group cost an average of $72,139 (95% CI: $33,070, $117,299) per patient and the QUAD-BiV group cost an average of $92,933 (95% CI: $33,070, $167,211) per patient. MPP-AS resulted in average cost savings of $20,794 (95% CI: -$37,957, $97,124) and in simulation was less expensive than QUAD-BiV 66.6% of the time. MPP-AS had lower expected cost for patients surviving less than 6.25 years or more than 7 years. Conclusions Over a 10-year horizon, MPP-AS was associated with fewer HFR health care events, and in simulation, was less expensive than QUAD-BiV most of the time.

Estimates and predictors of health care costs of esophageal adenocarcinoma: a population-based cohort study

BackgroundEsophageal adenocarcinoma (EAC) incidence is increasing rapidly. Esophageal cancer has the second lowest 5-year survival rate of people diagnosed with cancer in Canada. Given the poor survival and the potential for further increases in incidence, phase-specific cost estimates constitute an important input for economic evaluation of prevention, screening, and treatment interventions. The study aims to estimate phase-specific net direct medical costs of care attributable to EAC, costs stratified by cancer stage and treatment, and predictors of total net costs of care for EAC.MethodsA population-based retrospective cohort study was conducted using Ontario Cancer Registry-linked administrative health data from 2003 to 2011. The mean net costs of EAC care per 30 patient-days (2016 CAD) were estimated from the payer perspective using phase of care approach and generalized estimating equations. Predictors of net cost by phase of care were based on a generalized estimating equations model with a logarithmic link and gamma distribution adjusting for sociodemographic and clinical factors.ResultsThe mean net costs of EAC care per 30 patient-days were $1016 (95% CI, $955–$1078) in the initial phase, $669 (95% CI, $594–$743) in the continuing care phase, and $8678 (95% CI, $8217–$9139) in the terminal phase. Overall, stage IV at diagnosis and surgery plus radiotherapy for EAC incurred the highest cost, particularly in the terminal phase. Strong predictors of higher net costs were receipt of chemotherapy plus radiotherapy, surgery plus chemotherapy, radiotherapy alone, surgery alone, and chemotherapy alone in the initial and continuing care phases, stage III-IV disease and patients diagnosed with EAC later in a calendar year (2007–2011) in the initial and terminal phases, comorbidity in the continuing care phase, and older age at diagnosis (70–74 years), and geographic region in the terminal phase.ConclusionsCosts of care vary by phase of care, stage at diagnosis, and type of treatment for EAC. These cost estimates provide information to guide future resource allocation decisions, and clinical and policy interventions to reduce the burden of EAC.

A case-mix classification system for explaining healthcare costs using administrative data in Italy

BackgroundThe Italian National Health Service (NHS) provides universal coverage to all citizens, granting primary and hospital care with a copayment system for outpatient and drug services. Financing of Local Health Trusts (LHTs) is based on a capitation system adjusted only for age, gender and area of residence. We applied a risk-adjustment system (Johns Hopkins Adjusted Clinical Groups System, ACG® System) in order to explain health care costs using routinely collected administrative data in the Veneto Region (North-eastern Italy). MethodsAll residents in the Veneto Region were included in the study. The ACG system was applied to classify the regional population based on the following information sources for the year 2015: Hospital Discharges, Emergency Room visits, Chronic disease registry for copayment exemptions, ambulatory visits, medications, the Home care database, and drug prescriptions. Simple linear regressions were used to contrast an age-gender model to models incorporating more comprehensive risk measures aimed at predicting health care costs. ResultsA simple age-gender model explained only 8% of the variance of 2015 total costs. Adding diagnoses-related variables provided a 23% increase, while pharmacy based variables provided an additional 17% increase in explained variance. The adjusted R-squared of the comprehensive model was 6 times that of the simple age-gender model. ConclusionsACG System provides substantial improvement in predicting health care costs when compared to simple age-gender adjustments. Aging itself is not the main determinant of the increase of health care costs, which is better explained by the accumulation of chronic conditions and the resulting multimorbidity.

Association Between Cardiorespiratory Fitness and Health Care Costs: The Veterans Exercise Testing Study

ObjectiveTo determine the association between cardiorespiratory fitness (CRF) and annual health care costs in Veterans. Patients and MethodsThe sample included 9942 subjects (mean age, 59±11 years) undergoing a maximal exercise test for clinical reasons between January 2005 and December 2012. Cardiorespiratory fitness, expressed as a percentage of age-predicted peak metabolic equivalents (METs) achieved, was categorized in quartiles. Total and annualized health care costs, derived from the Veterans Administration Allocated Resource Center, were compared using multiple regression, controlling for demographic and clinical characteristics. ResultsA gradient for reduced health care costs was observed as CRF increased, with subjects in the least-fit quartile having approximately $14,662 (P<.001) higher overall costs per patient per year compared with those in the fittest quartile, after controlling for potential confounding variables. Each 1-MET higher increment in fitness was associated with a $1592 annual reduction in health care costs (5.6% lower cost per MET), and each higher quartile of fitness was associated with a $4163 annual cost reduction per patient. The effect of CRF was more pronounced among subjects without cardiovascular disease (CVD), suggesting that the results were not driven by the possibility that less-fit individuals had greater CVD. Cost savings attributable to higher fitness were greatest in overweight and obese subjects, with lower savings observed among those individuals with a body mass index less than 25 kg/m2. In a model including historical, clinical, and exercise test responses, heart failure was the strongest predictor of health care costs, followed by CRF (P<.01). ConclusionLow CRF is associated with higher health care costs. Efforts to improve CRF may not only improve health but also result in lower health care costs.

Determinants of health\xa0care costs in the senior elderly: age, comorbidity, impairment, or proximity to death?

Ageing is assumed to be accompanied by greater health care expenditures but the association is also viewed as a ‘red herring’. This study aimed to evaluate whether age is associated with health care costs in the senior elderly, using electronic health records for 98,220 participants aged 80 years and over registered with the UK Clinical Practice Research Datalink and linked Hospital Episode Statistics (2010–2014). Annual costs of health care utilization were estimated from a two-part model; multiple fractional polynomial models were employed to evaluate the non-linear association of age with predicted health care costs while also controlling for comorbidities, impairments, and death proximity. Annual health care costs increased from 80 years (£2972 in men, £2603 in women) to 97 (men; £4721) or 98 years (women; £3963), before declining. Costs were significantly elevated in the last year of life but this effect declined with age, from £10,027 in younger octogenarians to £7021 in centenarians. This decline was steeper in participants with comorbidities or impairments; £14,500 for 80–84-year-olds and £6752 for centenarians with 7+ impairments. At other times, comorbidity and impairments, not age, were main drivers of costs. We conclude that comorbidities, impairments, and proximity to death are key mediators of age-related increases in health care costs. While the costs of comorbidity among survivors are not generally associated with age, additional costs in the last year of life decline with age.

Dependence Stage and Pharmacoeconomic Outcomes in Patients With Alzheimer Disease.

The level of assistance patients with Alzheimer disease (AD) require in their care may be an important predictor of resource use, costs of care, and quality of life. The Dependence Scale (DS), a measure of care-assistance required, was used to estimate costs of care and quality of life of patients with AD categorized into 6 dependence stages based upon the summated item scores of the DS. Data were derived from a 3-year, noninterventional study of 132 patients with probable AD (ages, 50 to 85 y) and caregiver dyads. We investigated the association between DS scores and health care costs, health-related quality of life (HRQoL), caregiver burden and estimated annual costs and HRQoL for 6 dependence stages in adjusted models. DS scores were significant predictors of health care costs, HRQoL, and caregiver burden. The estimated annual health care costs and a measure of HRQoL (EuroQoL-5D) ranged from $11,418 and 1.00 for those at very mild dependence stage to $101,715 and 0.26 for those at very severe dependence stage. DS scores classified into 6 dependence stages provides a useful method to estimate unique levels of care-associated costs and health utilities for pharmacoeconomic evaluations of new treatments for AD.

Health resource utilization and costs for stage IV bladder cancer in community oncology settings.

e16032 Background: Current real-world data on patients with metastatic bladder cancer in the community setting are limited. This study describes patient characteristics, health resource utilization (HRU), and costs for this population. Methods: Medical records data from adults diagnosed with stage IV bladder cancer (T4b, node-positive, or distant metastatic disease) between 1/1/2008 and 6/1/2015 were retrospectively collected from a network of 10 US community oncology practices. Patient characteristics, HRU, and costs were assessed. Regression models were used to estimate group effects on utilization and costs through three progression-based lines. Results: Of 508 patients, 75% were male, 79% white, 15% African American, and 71.5% were aged ≥65 years (median age, 71 years; range 35-92 years). Overall, 76.4% were current or past tobacco users. The most prevalent comorbidities were diabetes (23.4%), renal disease (16.5%), and chronic obstructive pulmonary disease (COPD; 12.4%). Overall, 70.1% had distant metastases at diagnosis, including bone (24.0%), lung (23.4%), and liver (14.6%). Of 341 patients analyzed for HRU and cost, 58.9% had hospital visits and 11.7% had emergency department (ED) visits. Age, smoking history, and presence of diabetes were predictors of hospitalization (all P&lt; 0.05). Overall mean monthly total healthcare cost was $18,778 ± $47,006 (median $6285), which was driven by hospitalizations ($14,660 ± 44,686, median $1717). Mean monthly ED cost was $776 ± $5530, median $0. Mean monthly cost for office visits was $186 ± $189, median $152. Procedures in the medical oncology setting had a mean monthly cost of $707 ± $1179, median $395. Other costs included systemic therapy ($267 ± $704, median $117), infused supportive care drugs ($758 ± $1458, median $0), and other drugs ($1422 ± $3745, median $136). Age, presence of diabetes, presence of any comorbidities, body mass index, and impaired composite performance status were predictors of healthcare costs (all P&lt; 0.05). Conclusions: This study showed significant HRU and costs for metastatic bladder cancer patients suggesting high unmet need.

Predictors of healthcare costs among patients with migraine in a commercial claims database (P1.195)

Predictors of healthcare costs among patients with migraine in a commercial claims database (P1.195)

Trajectories of depression and anxiety symptoms in coronary heart disease strongly predict health care costs

IntroductionThere is little information describing the trajectories of depression and anxiety symptomatology in the context of coronary heart disease (CHD), and their comparison according to sociodemographic and disability measures, cardiac risk factors, and health care costs.MethodsUsing a primary care cohort of 803 patients with a diagnosis of CHD, a latent class growth curve model was developed to study the distinct trajectories of depression and anxiety symptoms (using the hospital anxiety and depression scale) over a 3-year period comprised of 7 distinct follow-up points. Multinomial regression analysis was then conducted to study the association between latent classes, baseline risk factors, and total health care costs across time.ResultsThe 5-class model yielded the best combination of statistical best-fit analysis and clinical correlation. These classes were as follows: “stable asymptomatic” (n = 558), “worsening” (n = 64), “improving” (n = 15), “chronic high” (n = 55), and “fluctuating symptomatology” (n = 111). The comparison group was the “stable asymptomatic” class. The symptomatic classes were younger and had higher proportion of women, and were also associated with non-white ethnicity, being a current smoker, and having chest pain. Other measures of disease severity, such as a history of myocardial infarction and comorbidities, were not associated with class membership. The highest mean total health care costs across the 3 years were the “chronic high” and “worsening” class, with the lowest being the “improving” and “stable low” classes. The total societal costs for patients in the “chronic high” and “worsening” class were significantly higher, as compared to the “stable low” class.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Daily energy expenditure predictor of healthcare costs?

Daily energy expenditure predictor of healthcare costs?

Long-term socioeconomic consequences and health care costs of childhood and adolescent-onset epilepsy.

To estimate long-term socioeconomic consequences and health care costs of epilepsy with onset in childhood and adolescence. A historical prospective cohort study of Danish individuals with epilepsy, age up to 20years at time of diagnosis between January 1981 and December 2012. Information about marital status, parenthood, educational level, employment status, income, use of the health care system, and cost of medicine was obtained from nationwide administrative and health registers. We identified 12,756 and 28,319 people with diagnosed with epilepsy, ages 0-5 and 6-20years at onset, respectively. Using follow-up data for a maximum of 30years, 1,394 of those ages 0-5years at onset were compared with 2,897 controls persons without epilepsy, and 10,195 of those ages 6-20years at onset were compared with 20,678 controls without epilepsy. Compared with people without the epilepsy, those with epilepsy tended to have a lower level of education, to be less likely to be married, to be more likely to live alone, and to have higher divorce and unemployment rates, lower employment rates, and people with epilepsy were more likely to receive disability pension and social security. Income was lower from employment, which in part was compensated by social security, sick pay, disability pension and unemployment benefit, sick pay (public-funded), disability pension, and other public transfers. Predicted health care costs 30years after epilepsy onset were significantly higher among persons with epilepsy onset at 0-5 and 6-20years, including costs for outpatient and inpatient services (hospital services), emergency room use, primary health care sector (general practice), and use of medication. The long-term negative effects on all aspects of health care and social domains, including marital status, parental socioeconomic status, educational level, employment status, and use of welfare benefits compared with controls without epilepsy calls for increased awareness on childhood- and adolescent-onset epilepsy.