Predicting costs of mental health care: a critical literature review

Cost evaluation research in the mental health field is being increasingly recognized as a way to achieve a more effective deployment of scarce resources. However, there is a paucity of studies that seek to identify predictors of psychiatric service utilization and costs. This paper aims to critically review the published research in the field of psychiatric service utilization and costs, and discusses current methodological developments in this field

Out-of-Pocket Costs and the Flexible Benefits Decision: Do Employees Make Effective Health Care Choices?

This study analyzes employees\u27 ability to select health insurance benefits that fit their needs.The study analyzes both the actual choices and the implications of those choices for employees, measured as out-of-pocket costs (OPC). By introducing OPC as a measure of decision quality, this study demonstrates its advantages over measuring only employee choice. Results from a sample of manufacturing employees suggest that most employees made cost-optimizing decisions, out-performing recommendations from a linear model. Employees also were financially better off overall with choice than they would have been had they all been placed into either medical plan option available to them. This study supports the value of choice, but does not support the assertion that employees always make benefits decisions that best fit their needs

The difficult task of predicting the costs of community-based mental health care. A comprehensive case register study.

Previous studies have attempted to forecast the costs of mental health care, using clinical and individual variables; the inclusion of ecological measures could improve the knowledge of predictors of psychiatric service utilisation and costs to support clinical and strategic decision-making

Predictive modeling of housing instability and homelessness in the Veterans Health Administration

OBJECTIVE: To develop and test predictive models of housing instability and homelessness based on responses to a brief screening instrument administered throughout the Veterans Health Administration (VHA). DATA SOURCES/STUDY SETTING: Electronic medical record data from 5.8 million Veterans who responded to the VHA's Homelessness Screening Clinical Reminder (HSCR) between October 2012 and September 2015. STUDY DESIGN: We randomly selected 80% of Veterans in our sample to develop predictive models. We evaluated the performance of both logistic regression and random forests—a machine learning algorithm—using the remaining 20% of cases. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from two sources: VHA's Corporate Data Warehouse and National Homeless Registry. PRINCIPAL FINDINGS: Performance for all models was acceptable or better. Random forests models were more sensitive in predicting housing instability and homelessness than logistic regression, but less specific in predicting housing instability. Rates of positive screens for both outcomes were highest among Veterans in the top strata of model‐predicted risk. CONCLUSIONS: Predictive models based on medical record data can identify Veterans likely to report housing instability and homelessness, making the HSCR screening process more efficient and informing new engagement strategies. Our findings have implications for similar instruments in other health care systems.U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Grant/Award Number: IIR 13-334 (IIR 13-334 - U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSRD))Accepted manuscrip

The Dynamics of Real-Time Online Information and Disease Progression: Understanding Spatial Heterogeneity in the Relationship

The re-emergence of infectious diseases such as measles and polio is creating logistics challenges for the state authorities to curb their spread and contain them. (CL, 2015) Real-time surveillance of infectious diseases is important to detect possible epidemics in advance to prevent shortages of medications (FDA, 2018). The outbreak of an infectious disease creates panic in the community and is accompanied by a sudden increase in the online interest in knowing more about the disease and its symptoms. Prior studies have found a strong relationship between web-based information and disease outbreak but the influence of dynamics of web-based information in real-time is often not considered (Zhang, 2017). The dynamics or rate of change of the online interest in a disease can inform or misinform about perspective cases of the disease in a region. Oftentimes, especially in this connected world individuals overreact to the situation which may send spurious online signals regarding the disease progression. Hence, we study the relationship between the dynamics of online information and the infectious disease outbreak. We also investigate if this relationship could be influenced by regional demographic factors. We analyze weekly online interest dynamics for five infectious diseases over a period of three years across 50 states of the United States. We control for several factors (including weather, demographics, and travelers) and utilize hierarchical functional data models to incorporate real-time dynamics and clustering at the regional level. Preliminary findings suggest that online interest dynamics have a significant relationship with disease outbreak and the effect is segregated at the regional level. These findings are important to develop a system for real-time surveillance and account for the influence of heterogonous online interest during an endemic outbreak

Predicting the consumption of foods low in saturated fats among people diagnosed with Type 2 diabetes and cardiovascular disease: the role of planning in the theory of planned behaviour

The present study tested the utility of an extended version of the theory of planned behaviour that included a measure of planning, in the prediction of eating foods low in saturated fats among adults diagnosed with Type 2 diabetes and/or cardiovascular disease Participants (N = 184) completed questionnaires assessing standard theory of planned behaviour measures (attitude, subjective norm, and perceived behavioural control) and the additional volitional variable of planning in relation to eating foods low in saturated fats Self-report consumption of foods low insaturated fats was assessed 1 month later In partial support of the theory of planned behaviour, results indicated that attitude and subjective norm predicted intentions to eat foods low in saturated fats and intentions and perceived behavioural control predicted the consumption of foods low in saturated fats As an additional variable, planning predicted the consumption of foods low in saturated fats directly and also mediated the intention-behaviour and perceived behavioural control-behaviour relationships, suggesting an important role for planning as a post-intentional construct determining healthy eating choices. Suggestions are offered for interventions designed to improve adherence to healthy eating recommendations for people diagnosed with these chronic conditions with a specific emphasis on the steps and activities that are required to promote a healthier lifestyle. (C) 2010 Elsevier Ltd. All rights reserve

Keep it Simple? Predicting Primary Health Care Costs with Measures of Morbidity and Multimorbidity

In this paper we investigate the relationship between patients’ primary care costs (consultations, tests, drugs) and their age, gender, deprivation and alternative measures of their morbidity and multimorbidity. Such information is required in order to set capitation fees or budgets for general practices to cover their expenditure on providing primary care services. It is also useful to examine whether practices’ expenditure decisions vary equitably with patient characteristics. Electronic practice record keeping systems mean that there is very rich information on patient diagnoses. But the diagnostic information (with over 9000 possible diagnoses) is too detailed to be practicable for setting capitation fees or practice budgets. Some method of summarizing such information into more manageable measures of morbidity is required. We therefore compared the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs using data on 86,100 individuals in 174 English practices. The measures were derived from four morbidity descriptive systems (17 chronic diseases in the Quality and Outcomes Framework (QOF), 17 chronic diseases in the Charlson scheme, 114 Expanded Diagnosis Clusters (EDCs), and 68 Adjusted Clinical Groups (ACGs)). We found that, in general, for a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power and that measures with more categories did better than those with fewer. The EDC measures performed best, followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Allowing for individual patient morbidity greatly reduced the association of age and cost. There was a pro-deprived bias in expenditure: after allowing for morbidity, patients in areas in the highest deprivation decile had costs which were 22% higher than those in the lowest deprivation decile. The predictive ability of the best performing morbidity and multimorbidity measures was very good for this type of individual level cross section data, with R2 ranging from 0.31 to 0.46. The statistical method of estimating the relationship between patient characteristics and costs was less important than the type of morbidity measure. Rankings of the morbidity and multimorbidity measures were broadly similar for generalised linear models with log link and Poisson errors and for OLS estimation. It would be currently feasible to combine the results from our study with the data on the number of patients with each QOF disease, which is available on all practices in England, to calculate budgets for general practices to cover their primary care costs.multimorbidity; primary care; utilisation; costs; deprivation; budgets

Predicting outcome in acute low back pain using different models of patient profiling

This is a non-final version of an article published in final form in Spine, 34(18), 1970 - 1975, 2009. Copyright © 2009 Lippincott Williams & Wilkins, Inc.Study Design. Prospective observational study of prognostic indicators, using data from a randomized, controlled trial of physiotherapy care of acute low back pain (ALBP) with follow-up at 6 weeks, 3 months, and 6 months. Objective. To evaluate which patient profile offers the most useful guide to long-term outcome in ALBP. Summary of Background Data. The evidence used to inform prognostic decision-making is derived largely from studies where baseline data are used to predict future status. Clinicians often see patients on multiple occasions so may profile patients in a variety of ways. It is worth considering if better prognostic decisions can be made from alternative profiles. Methods. Clinical, psychological, and demographic data were collected from a sample of 54 ALBP patients. Three clinical profiles were developed from information collected at baseline, information collected at 6 weeks, and the change in status between these 2 time points. A series of regression models were used to determine the independent and relative contributions of these profiles to the prediction of chronic pain and disability. Results. The baseline profile predicted long-term pain only. The 6-week profile predicted both long-term pain and disability. The change profile only predicted long-term disability (P 0.05). A similar result was obtained when the order of entry was reversed. When predicting long-term disability, after the 6-week profile was entered at the first step, the change profile was not significant when forced in at the second step. However, when the change profile was entered at the first step and the 6-week clinical profile was forced in at the second step, a significant contribution of the 6-week profile was found. Conclusion. The profile derived from information collected at 6 weeks provided the best guide to long-term pain and disability. The baseline profile and change in status offered less predictive value

Predicting Employee Health Care Decisions in a Flexible Benefits Environment

[Excerpt] The purpose of this study is to identify the determinants of employees\u27 health care selections in a flexible benefits environment. The goal is to develop a model which will enable managers to predict the health care selections of employees. The research tasks required to accomplish this goal are extensive, and are in progress. The following report will summarize the results of analyses completed to date, the analyses that are in progress, the data required to complete these analyses, and the outcomes that can be expected when the study is done