Overview. Patterns of Care and Outcomes in Chronically Ill VA Beneficiaries. Veterans Vs. General Population (RE Klein, Office of the Actuary, VHA)

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1 Overview Patterns of Care and Outcomes in Chronically Ill VA Beneficiaries Carol M. Ashton MD MPH Director, VA Center for Quality of Care and Utilization Studies Professor of Medicine Baylor College of Medicine Context: veterans and the VA health care system Stimulus for the study Methodological issues in conducting variations studies Changes over time in health service use and survival US Veterans Veterans Vs. General Population (RE Klein, Office of the Actuary, VHA) 25.8 million veterans as of December % are under age % are women Population is composed of multiple cohorts of various sizes (e.g. WWII, Vietnam) Source: Veteran Population Files, Education Median income Unemployed Not HS grad HS grad only Some college >4 yrs college Age Age Age Men Women Veterans 12% 35% 29% 23% $28,900 $37,100 $35, % 3.1% Nonveterans 18% 31% 25% 26% $24,000 $36,200 $28, % 3.6% Goal of VA Programs and Services To provide for equity between veterans and nonveterans in socioeconomic measures such as educational attainment, median personal income, and employment rate. The VA Health Care System: Capital and Human Resources 172 hospitals, 134 nursing homes, and 519 outpatient clinics arranged (since 1995) into 22 integrated service networks 11,241 physicians, 34, 071 registered nurses, 182,661 total employees ~100 of US medical schools use VA hospitals as major training sites Fiscal year 1999 data

2 The VA Health Care System: Workload ADC, acute hospital ADC, psychiatric ADC, long term care ADC, home & community-based Outpatient visits FY95 16,028 13,330 43,617 17, million FY99 8,371 5,144 42,700 21, million ADC=average daily census VA Health Care System: Eligibility Veterans Health Care Reform Act of 1996: enrollees assigned to one of 7 priority groups; universal benefits package 33% of enrollees have service-connected problem or are disabled (categories 1-4) 41% not service-connected but fall below income/net-worth criteria (category 5) (no co-pay) FY99: 4.1 million enrollees (80% no co-pay); unique patients treated: 3.2 million VA Health Care System: Financing FY01 Congressional appropriation for VA medical care: $20.9 billion Challenge: resource allocation to constituent facilities Full capitation since 1997 (Veterans Equitable Healthcare Allocation, VERA); mostly based on utilization Capitation rates based on health/disease burden being evaluated and phased in (Laura Petersen & colleagues in Houston) VA A [Socialist] Anomaly in a Capitalist Country JK Iglehart, NEJM 1985 VA: federal, tax-financed provider and payer for care, not insurance program Fairly homogeneous patient population Salaried physicians Centralized administration; uniform policies and procedures Identical clinical and administrative health care databases, system-wide 1995: A Confluence of Events New Under Secretary for Health, KW Kizer Reform of eligibility laws (care for beneficiaries, not provision of services) Phase-in of full capitation system Switch to primary care delivery model Drive to reduce hospital use Reorganization of 160 VA medical centers into 22 integrated networks Goal of the Reorganization to provide structural incentives for efficiency, quality, and improved access. Office of the Under Secretary: A Vision for Change, February 1995

3 Our Study Questions A Cohort-based Analysis of Utilization and Survival Rates Across the VA Health Care System, Funded by the VA Health Services R&D Service CM Ashton, NJ Petersen, J Souchek, NP Wray, TJ Menke, and Others What are historical patterns of hospital use in the 22 networks? (geographic differences) Is the reorganization having its intended effects on hospital use? (change over time) Is the reorganization having any unintended effects on health status of beneficiaries? Hypotheses Patients and Data Sources Reductions in VA hospital use will be compensated for by increases in ambulatory care use If VA hospital use is curtailed too sharply one-year survival rates will decrease VA urgent care visit rates will increase Use of non-va hospitals will increase Nine cohorts:, Pneumonia,,,, Chronic Renal Failure, Bipolar Disorder, Major Depressive Disorder, Schizophrenia VA data sources: Patient Treatment Files, Outpatient Clinic Files, Death File Medicare: Enrollment Files, MEDPAR Files (Steve Wright, VA HQ) A Variations Study Design Issues Four top issues for HSR: efficacy, effectiveness, variations in use, and quality (Brook & Lohr) Variation: place-to-place or temporal differences in levels of per capita service consumption Of interest only when all the usual explanations have been accounted for (SES, prevalence, diagnosis mix, health status, etc.) Variations in use are most interesting when tied to variations in outcome which rate is right? Deciding what sorts of utilization to compare; quantifying utilization Choosing the level of aggregation; assigning patients Defining period of risk for utilization Making valid comparisons Quantifying variation: how much is too much? Statistical analysis issues

4 Types of Utilization Use and Outcome Rates Studied Hospital use: discharge rates, bed-day rates, user rates, length of stay; multiple-stay rates Ambulatory care: primary care visit rates; specialty referral rates; urgent care, relative value units Medication use Hospital use: discharge rates bed-day rates Ambulatory care: primary care visit rates psychiatry clinic visit rates testing and consultation visit rates urgent care visit rates One-year survival probabilities Level of Aggregation Analysis can be based on user population, enrolled population, or census population Geopolitical units (census region or division, state, county, city, zip code, block) vs. medical service areas (network, facilitylevel catchment area) Assignment: patients use services outside their home area---how to attribute? Cohort Building Strategy Patients were entered into cohort at time of first VA hospital stay for one of the 9 study conditions provided they d had no stays in prior 2 years for those conditions Cohort-building began in 1990; dynamic No duplication of patients across cohorts; no switching into another cohort in different year Exit: death, end of study period, 2 consecutive years of no contact with VA but known to be alive Period of Risk for Utilization Period of Risk for Utilization Idea is surveillance for use over some time period Cohort: a group of people who share a characteristic Inception cohort: entry into cohort at a uniform point in disease course End of risk period: end of study, death; switching out of health plan General Rules: Dead people don t use medical services. People who are already in the hospital can t be admitted to the hospital. People who are in the hospital don t make clinic or office visits.

5 Risk Period for Utilization: The Denominator Issue Head counts vs. person-time People enter and leave cohort at different times: a person in cohort for 60D has less opportunity for use than one in for 11M Head counts are always higher than person-years As long as person is in cohort, they contribute time at risk for use & events as well as use and events FY Congestive Heart Failure Cohort # in cohort at start of yr 18,195 21,789 24,677 27,198 29,190 # entering during yr # in cohort for any portion of yr 27,218 30,337 32,845 35,008 36,591 # dying during yr # personyrs of FU 20,265 23,413 26,050 28,331 30,205 Making Valid Comparisons: Risk Adjustment Risk = probability that an event will occur Start with conceptual model of factors known to be associated with greater or lesser risk for health service use; these are the factors that must be controlled for Goal: adjust for extraneous factors, to allow influence of practice patterns on service use to emerge Risk Adjustment Use of epidemiological & statistical techniques to standardize extraneous factors across populations or samples... so that between-group variations in utilization (or outcome) rates can be attributed more validly to the hypothesized cause (practice patterns) Unchangeable patient factors vs modifiable organizational/process of care factors Risk Adjustment in Cohort Study Primary diagnosis taken care of by using disease cohort methodology Indicators of social support & physiologic reserve (age, race, sex, marital status, $$) Indicators of clinical complexity (type of primary diagnosis within DRG; number of comorbidities; body systems affected by comorbidity) Length of time in cohort Other Extraneous Factors Associated With Health Service Use Area-wide supply of VA resources and services Area-wide supply of non-va services Prevalences of certain health (disease) habits Background population-based mortality rates (the enigma of the Southeast)

6 Quantifying Geographic Variation Options: range extremal quotient (highest value divided by the lowest) coefficient of variation (the standard deviation expressed as a proportion of the mean) systematic coefficient of variation Easiest to understand: extremal quotients of risk-adjusted utilization rates How Much Variation Is Too Much? Key is the link between utilization rates and health outcomes Concept of statistical vs administrative significance of geographic variations; translate utilization differences into what they represent to an administrator--clinical FTEE for clinic visits, extra beds/wards for hospital use Administrative Significance of Geographic Differences, Cohort, 1995 Reference: VA system-wide median utilization rate Network A Network B Network C Network D Additional (Fewer) Hospital Beds 18 3 (3) (15) Additional (Fewer) Primary Care Visits Per Week 60 (25) (14) 26 Statistical Analysis Issues Hierarchical structure of the data: patients nested within medical service areas, MSAs within networks Nesting affects variance structure because patients from one MSA (or MSAs from a network) are correlated Hierarchical (multi-level) modeling is applicable The problem of outliers VA Bed-day day Rates Per Person-year Pneumonia Renal Failure Bipolar Disorder Major Depression Schizophrenia Relative Change Primary Care Visit Rates Per Person-year Pneumonia Renal Failure Bipolar Disorder Major Depression Schizophrenia

7 Urgent Care Visit Rates Per Person-year Pneumonia Renal Failure Bipolar Disorder Major Depression Schizophrenia Testing and Consultation Visit Rates Per Person-year Pneumonia Renal Failure Bipolar Disorder Major Depression Schizophrenia One-year Survival Probabilities (Cox Proportional Hazards Models) Pneumonia Renal Failure Bipolar Disorder Major Depression Schizophrenia Summary of Changes in VA Utilization, VA hospital bed day rates fell by about 50% VA primary care visit rates increased slightly VA urgent care visit rates fell by about 40% VA testing and consultation visit rates increased by 10-20% One-year survival rates held steady Conclusions VA s multiple initiatives sharply curtailed VA hospital use between Decline in VA hospital use was accompanied by increase in use of ambulatory services Despite a 50% reduction in VA hospital use, VA beneficiaries with chronic illness did not suffer decreased access to comprehensive VA care Urgent care visit rates actually fell One-year survival rates were unchanged Limitations Survival: insensitive measure of outcomes, especially in psychiatric cohorts Inability to obtain data on non-va health care use by VA beneficiaries under age 65 or those enrolled in HMOs Cohorts composed only of severely ill (cohort enrollment based on initial hospital stay) Generalizability: women and high SES people under-represented in VA user population

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