Epidemiology and Biostatistics at the NIH Clinical Center conduct collaborative clinical research. Examples of current ongoing research are:

Adults with Chronic Health Care Needs

The purpose of this project is to develop population-level profiles and estimates for adults with chronic health care needs, including those with long-lasting health conditions and/or disabilities. These sub-populations are of particular interest because they are expected to have elevated health service needs over an extended period of time. While the current health care system frequently excels at responding to immediate medical needs such as injuries and acute illnesses, it is sometimes less successful providing ongoing care to people with chronic conditions and disabilities in order to improve their day-to-day lives. This stands in considerable contrast to the pediatric health services arena, which made an important transition in the mid 1990s to a population-level, criteria-based definition of children with chronic conditions and/or disabilities, now known as Children with Special Health Care Needs (CSHCNs). The criteria formulated to define this group have been used to develop nationally representative surveys and estimates of need for, access to, use and cost of services in this population.  Findings from these studies have been used to inform both policy making (such as the Family Opportunity Act of 2005) and service delivery models (such as the medical home initiative).  Adapting and applying this population-based methodology to study adults with disabilities or chronic conditions, or both, will fill an important void in the health services literature.  It will also provide policy makers and practitioners with more useful estimates of the size, demographics, functional abilities, health conditions, service needs, insurance needs, and associated costs of individuals who require ongoing health care and health-related services in the United States.

An Outcome Monitoring System for Post-Acute Care

This study will test the feasibility of implementing an Outcomes Monitoring System (OMS), designed for use across all post-acute care (PAC) settings, by implementing an innovative computer adaptive testing (CAT) instrument developed by scientists at Boston University among a cohort of 200 stroke patients at Kaiser Permanente of Northern California. Patients will be enrolled during their hospitalization for an acute stroke and will be followed over time.  The OMS will be administered at several points to characterize functional status after utilizing different PAC settings.  Working with Kaiser Permanente's providers and the designers of the OMS at Boston University, we will: 1) Implement the OMS to integrate outcome data collection and reporting across selected sites including homecare, out-patient rehabilitation facilities, skilled nursing facilities and inpatient rehabilitation facilities in the Kaiser Permanente System; and 2) Evaluate the feasibility, validity, and acceptability of the OMS.  A secondary objective will be to examine the outcome of stroke patients that are treated in the Kaiser Permanente system based on their site of care.

Analyzing the SSA Disability Evaluation Process

The Social Security Administration (SSA) continues to seek ways to effectively and efficiently adjudicate its disability claims.  Historically, SSA has attempted to provide adjudicators with screening tools designed to expedite claim allowances.  One such screening tool is the Listing of Impairments, which identifies categories of medical diagnoses by group.  However, evidence suggests that the diagnostic basis for the Listings has become less useful a marker of disability.  Data from the SSA indicate that early use of the Listings accounted for more than 90% of allowances, however by 2004, the Listings accounted for only 52% of the allowances.  It is in this context that the SSA sought help from the NIH (RMD) to examine existing SSA data to improve screening processes and to explore innovative methods for augmenting their existing disability evaluation process.  Thus, this project includes two major lines of research: 1) analysis of existing SSA data, and 2) assessing the feasibility of developing Computer Adaptive Testing (CAT) instruments that can be integrated into the SSA data collection and determination processes.  It will be necessary to identify important information gaps, suggest questions that should be asked when claimants first apply for disability benefits, and to identify characteristics of claims that are ultimately allowed in order to inform the screening process.  This information will also be used to determine additional content needs for modification of existing CATs or development of new CATs.  Preliminary work will help to inform us about project feasibility by evaluating the content, quality, and completeness of information that is collected at the various stages of the SSA evaluation process.  Once feasibility has been established, data analysis and CAT development will proceed concurrently for this multi-year project.

Medically-related personal bankruptcy in the Medicare population

The purpose of the medically-related bankruptcy project is to evaluate the extent to which Medicare and other public programs protect the elderly and disabled from financial insolvency after a health event requiring hospitalization.  If ill-health does lead to bankruptcy, it is important that policy makers have accurate information on the magnitude of the problem and which groups are most at risk. This project represents a unique opportunity to identify some of the key demographic and socio-economic factors that put some Medicare beneficiaries at high risk of financial insolvency.  Furthermore, the bankruptcy project will link Medicare claims data to Federal bankruptcy court records avoiding many of the pitfalls of previous studies that rely on survey results that are prone to respondent error. This novel linking of Medicare claims data and Federal bankruptcy records is vital for designing a social security system that provides optimal protection for vulnerable populations.