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INTRODUCTION: Greater transit use is associated with higher levels of physical activity, which is associated with lower health risks and better health outcomes. However, there is scant evidence about whether health care costs differ based on level of transit ridership. METHODS: A sample (n=947) of members of Kaiser Permanente in the Portland, Oregon area were surveyed in 2015 about their typical use of various modes of travel including transit. Electronic medical record-derived health care costs were obtained among these members for the prior three years. Analysis examined proportional costs between High transit users (3+ days/week), Low transit users (1-2 days/week), and Non-users adjusting for age and sex, and then individually (base models) and together for demographic and health status variables. RESULTS: In separate base models across individual covariates, High transit users had lower total health care costs (59-69% of Non-user's costs) and medication costs (31-37% of Non-users' costs) than Non-users. Low transit users also had lower total health care (69%-76% of Non-users' costs) and medication costs (43-57% transit of Non-user's costs) than Non-users. High transit users' outpatient costs were also lower (77-82% of Non-users). In fully-adjusted models, total health care and medication costs were lower among High transit users' (67% and 39%) and Low transit users' (75% and 48%) compared to Non-users, but outpatient costs did not differ by transit use. CONCLUSIONS: Findings have implications for the potential cost benefit of encouraging and supporting more transit use, although controlled longitudinal and experimental evidence is needed to confirm findings and understand mechanisms.

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OBJECTIVES: This study compares the ability of 3 risk-assessment models to distinguish high and low expense-risk status within a managed care population. Models are the Global Risk-Assessment Model (GRAM) developed at the Kaiser Permanente Center for Health Research; a logistic version of GRAM; and a prior-expense model. GRAM was originally developed for use in adjusting Medicare payments to health plans. METHODS: Our sample of 98,985 cases was drawn from random samples of memberships of 3 staff/group health plans. Risk factor data were from 1992 and expenses were measured for 1993. Models produced distributions of individual-level annual expense forecasts (or predicted probabilities of high expense-risk status for logistic) for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution to analyze the models' ability to distinguish high and low expense-risk status. Forecast stability was analyzed through bootstrapping. RESULTS: GRAM discriminates better overall than its comparators (although the models are similar for policy-relevant thresholds). All models forecast the highest-cost cases relatively well. GRAM forecasts high expense-risk status better than its comparators within chronic and serious disease categories that are amenable to early intervention but also generates relatively more false positives within these categories. CONCLUSIONS: This study demonstrates the potential of risk-assessment models to inform care management decisions by efficiently screening managed care populations for high expense-risk. Such models can act as preliminary screens for plans that can refine model forecasts with detailed surveys. Future research should involve multiple-year data sets to explore the temporal stability of forecasts.

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Health care information technology is changing rapidly and dramatically. A small but growing number of clinicians, especially those in staff and group model HMOs and hospital-affiliated practices, are automating their patient medical records in response to pressure to improve quality and reduce costs. Computerized patient record systems in HMOs track risks, diagnoses, patterns of care, and outcomes across large populations. These systems provide access to large amounts of clinical information; as a result, they are very useful for risk-adjusted or health-based payment. The next stage of evolution in health-based payment is to switch from fee-for-service (claims) to HMO technology in calculating risk coefficients. This will occur when HMOs accumulate data sets containing records on provider-defined disease episodes, with every service linked to its appropriate disease episode for millions of patients. Computerized patient record systems support clinically meaningful risk-assessment models and protect patients and medical groups from the effects of adverse selection. They also offer significant potential for improving quality of care.

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OBJECTIVES: This study evaluated the cost-effectiveness of a smoking cessation and relapse-prevention program for hospitalized adult smokers from the perspective of an implementing hospital. It is an economic analysis of a two-group, controlled clinical trial in two acute care hospitals owned by a large group-model health maintenance organization. The intervention included a 20-minute bedside counseling session with an experienced health counselor, a 12-minute video, self-help materials, and one or two follow-up calls. METHODS: Outcome measures were incremental cost (above usual care) per quit attributable to the intervention and incremental cost per discounted life-year saved attributable to the intervention. RESULTS: Cost of the research intervention was $159 per smoker, and incremental cost per incremental quit was $3,697. Incremental cost per incremental discounted life-year saved ranged between $1,691 and $7,444, much less than most other routine medical procedures. Replication scenarios suggest that, with realistic implementation assumptions, total intervention costs would decline significantly and incremental cost per incremental discounted life-year saved would be reduced by more than 90%, to approximately $380. CONCLUSIONS: Providing brief smoking cessation advice to hospitalized smokers is relatively inexpensive, cost-effective, and should become a part of the standard of inpatient care.

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OBJECTIVES: To highlight the types and sources of data on medical risk and outcomes routinely collected by managed care organizations over time; to summarize the quality and consistency of these data; and to describe some of the difficulties that arise in collecting, pooling, and using these data. DESIGN: Synthesis of the experiences of two risk-adjustment modeling projects in assembling large volumes of demographic, diagnostic, and expense data from several health maintenance organizations (HMOs) over multiple years. SETTING: Six large HMOs from the Northwest, North Central, and Northeast regions of the USA. INTERVENTIONS: Health plans were approached to participate in a risk-adjustment study, presented with an extensive variable-by-variable data request, and, if willing to participate, asked to specify a desired process for extracting, copying, and transferring selected variables to the study site for purposes of research. Depending on local circumstances, three different approaches were used: (i) health plan staff obtained the data and organized them into the requested study format; (ii) study staff were provided access to health plan data systems to perform the extractions directly; and (iii) health plans hired contract programmers to perform the extractions under the direction of the study team. Key measures of risk and cost were extracted and merged into analysis files. MAIN OUTCOME MEASURES: Complete and consistent eligibility maps, demographic information, inpatient and outpatient diagnoses, and total health plan expense for each enrollee. RESULTS: We have been successful in collecting and integrating complete utilization, morbidity, demographic, and cost data on total memberships of five large HMOs as well as a subset from a sixth HMO, all for multiple years. CONCLUSION: While HMOs vary greatly in the quality and comprehensiveness of their data systems, these attributes have been improving across the board over time. Automated health plan data systems represent potentially valuable sources of data on health risks and outcomes and can be used to benchmark disease management programs and risk adjust capitation payments and medical outcomes.

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