How can the data found on be used and why is this information important?

More than 1.4 million Americans live in nursing homes today. By 2020, an estimated 12 million will need long-term care, whether in a nursing home, assisted living facility, chronic care hospital or from an at-home health service provider. Brown University researchers have created a database aimed at providing information to improve the nation's long-term care system and the lives of the elderly who rely on that system. hosts data regarding the health and functional status of nursing home residents, characteristics of care facilities, and state policies relevant to long term care services and financing. The data will allow researchers to trace a clear relationship between state policies and local market forces and the quality of long-term care. Researchers can use this website to examine care processes and resident outcomes within the context of their local markets and regulatory practices. Policymakers can use the information to shape state and local guidelines, policies, and regulations that promote high-quality, cost-effective, equitable care to older Americans. Together, it is anticipated that these audiences will have the data, tools and results to better understand the long term care system, and to achieve improvements in how care is organized, financed and delivered.

What is the Residential History File (RHF)?

The RHF is a data resource developed at the Brown University Center for Gerontology and Healthcare Research. It is built using Medicare Enrollment data, Medicare claims data, and MDS data. It can be used to track individuals as they move through the long-term care system, including between different care settings and different care types (e.g. hospice). We use this to calculate re-hospitalization rates, etc.

Why are all MDS prevalence measures based on facility population on the first Thursday in April?

Research has shown that the nursing home population fluctuates both by season during the year and by day of the week. The nursing home population is highest during the winter months and lowest during the summer months. In addition, we have found that nursing home admissions and discharges fluctuate during each week, with the greatest number of admissions occurring on Mondays and the greatest number of discharges occurring on Fridays. We sought to avoid these issues by calculating all MDS prevalence measures based on the nursing home population on the first Thursday in April each year.

What is the methodology behind the Residential History File?

The goal of the RHF is to create a per-person chronological history of health service utilization and location of care within a pre-specified calendar (e.g. throughout a calendar year). The first step of the algorithm assigns utilizations/locations to days in a calendar. Depending upon the type of claim, the basic information from a claim is the location of care (hospital, nursing home, emergency room or observation days, and home) and type of provider (e.g. free-standing, hospital based, or swing bed). The sequence of data entered into the calendar is determined by a hierarchy formed according to our trust in the reliability of the claim, and the type of information it provides. Inpatient claims are first filled into dates of the calendar followed by days marking emergency department (ER) and observation days paid as outpatient claims. Next Skilled Nursing Facility (SNF) claims are entered onto days, followed by outpatient claims for skilled nursing service in a nursing home, and lastly home health claims are filled into days. The above claims are location specific. Hospice claims, on the other hand, are not location specific, since hospice can be provided in community or institutional settings; therefore episodelets of any type that overlap a period of hospice services will be called hospice in that location. In particular there are nursing home and community hospice episodelets. Once the calendar is filled by all information obtained from claims (except for hospice), the remaining non-filled days are referred to as gap days. During gap days individuals may have received continued, non-SNF covered nursing home care, or were in an assisted living facility, receiving other services not paid by Medicare, or are at a non-institutional setting. At this point we use MDS assessments to infer probable periods of time of nursing home care. MDS assessments are conducted according to a CMS mandated schedule. Admission assessments are required within two weeks of admission, quarterly assessments around 90 days, and annual assessments are required each calendar year, around the time of the closest designated quarterly assessment. Discharge tracking assessments are required by CMS and may be used to determine date of nursing home discharge when they are present. Using the regulation schedule we can infer periods of nursing home care within gap periods. For example, quarterly assessments may be used to project nursing home care between two MDS assessments (not to exceed 120 days) or between an MDS assessment date and the end of the preceding episodelet. MDS assessments within gap episodes can project nursing home care following the assessment date if the gap time from the assessment leads into an inpatient episodelet within 90 days. Additionally, since MDS assessments must be conducted within 14 days of nursing home admission, we consider any gap days during the 14 days prior to a MDS admission assessments to be nursing home stay days. Consecutive days with the same utilization are aggregated into episodelets of care.

How was the RHF used to determine the population on the first Thursday in April?

Using the above methodology, the RHF of all persons who had either an MDS assessment or a SNF claim during 1999-2006 was compiled. We then identified the date of the first Thursday in April in each of these study years, and determined the people who had a nursing home type episodelet that covered that date. A nursing home episodelet could be a SNF stay, a non-SNF stay, or receiving hospice care in nursing home.

How are the data on this website different from those found on CMS Nursing Home Compare?

The Nursing Home Compare website utilizes just the CMS approved quality measures. describes the characteristics of people using nursing homes in ways that are generally not available:

  1. provides data on all nursing home admissions
  2. also provides prevalence measures based on all those in a nursing home on a given day, rather than basing these on anyone with a record in the quarter
  3. also provides data about facility residents in summary form [e.g. RUGS, nursing case mix index (NCMI), cognitive performance score (CPS)] for both prevalence and all admissions*
  4. allows the user to manipulate data to look at how nursing homes relate to one another and to their counties and states, as well as compare data across counties and across states
  5. presents a number of variables for the first time anywhere, including provider level hospitalization rates and admissions per bed
* see the CMS website for information about their summary measures

How do I merge the county code used in LTCFocus (FIPS) to the SSA county code used in other data sources?

The county code used in LTCFocus is the FIPS county code. Using this crosswalk, you discover you need to trim the first two characters off what they are calling the FIPS code (we used a three-digit code while the crosswalk has five digits). The first two characters are the state code and the last three are our FIPS code. If you are doing this in SAS, the easiest way to do this is county = substr(FIPS,3,3). This county variable should then merge to the LTC county variable. 

What if I want to track one facility over time?

To track a facility longitudinally, use our accpt_id to PROV1680 crosswalk. While PROV1680 is the ID number reported in OSCAR/CASPER, accpt_id is the ID number we assign to each facility in order to track them over time despite changes of facility name or ownership. This crosswalk is available on the data downloads page, the link to which you receive when you request our data.

What should I do if I am having trouble downloading the data?

You should be sure you have the most recent version of the unzipping program. You should be able to download this online.

Where can I find a data dictionary?

The data dictionary is an additional file that is included along with the data files provided when you receive your Data Download email.