Greg Arling, PhD Indiana University Center for Aging Research ...

Greg Arling, PhD
Indiana University Center for Aging Research
Kathleen Abrahamson, PhD
Veterans Health Administration – Indianapolis
Valerie Cooke and Teresa Lewis
Minnesota Department of Human Services
Robert L Kane, MD
University of Minnesota School of Public Health

Determine the influence of facility and
market characteristics on community
discharges from Minnesota nursing
homes
Apply findings to a statewide
intervention to promote resident
transitions from nursing home to
community

Individual, facility, and community
characteristics likely combine to
influence transitions from nursing
home to community
Yet, prior research has focused on
individual characteristics excluding
the context of the discharge process

Community discharges are concentrated
heavily in the first 90 days after admission
Resident-level factors were highly
predictive of community discharge – health
and functional status, pay source, and
resident preferences
Yet, a sizable proportion of residents who
became long stay preferred returning to the
community (64%) and seemed capable of
doing so (40%)
Why did they remain in the facility?

Facility staff do not recognize or support
the resident’s desire to return home
Facilities do not have the resources or
services to facilitate transitions
Transitions threaten facility revenue,
e.g., reduced occupancy
Community based long term care
(CBLTC) alternatives are unavailable

Sample
• Annual cohort (July 2005-June 2006) of 22,469
admissions to 380 Minnesota nursing homes
Data Sources
• Resident – MDS assessments at admission and
90 days
• Facility – State Medicaid cost reports and
administrative systems
• Market – Area Resource File and state
administrative systems

Resident having a preference (MDS: H1a)
or support (MDS: H1b)for returning to the
community as recorded by NH staff at
admission
• Preference OR Support =1
• Neither Preference nor support = 0
Community discharge within 90 days of
admission
• Discharge to home or assisted living = 1
• Remained in the facility, died, or nursing home
transfer = 0

75 markets representing individual or
combinations of contiguous Minnesota
counties
Market defined according to:
• Among facilities in the market, the % of nursing
home admissions coming from that market
• Among persons in the market who are admitted to
nursing homes, the % entering a facility in that
market
Counties making up the markets are the
primary locus for community care funding
and delivery

Resident-level hierarchical general linear
models (HGLM) with P/S and CD as
outcomes and facility and market as random
effects
Facility-level empirical Bayes (EB) residuals
estimated from resident-level models
Facility-level hierarchical linear models
(HLM) with adjusted facility PS and CD rates
(EB residuals) as outcomes and market as a
random effect

Model the resident’s
• Preference/supportfor community discharge
• Community dischargewithin 90 days of
admission
Adjust for resident-level covariates
• Admission source – hospital or community
• Pay source - Medicare or Medicaid
• Major diagnoses – hip fracture or end-stage
disease
• ADL dependency, cognitive status, &Continence

Produce adjusted facility-level rates
• Proportion of residents preferring or support for
community discharge
• Rate of community discharges within 90 days of
admission
Adjusted facility rates are estimated as
empirical Bayes (EB) residuals representing
• Variation that remains between facilities in their
rates of preference/support and community
discharge
• After controlling for the characteristics of their
residents

Examine relationships between adjusted
facility-level rates of preference/support
and community discharge and
• Facility characteristics
• Market characteristics
HLM models
• Level 1: facility
• Level 2: market
• Market treated as a random effect

Facilities having a higher proportion of
residents preferring or having support to
return to the community (adjusted)
• Higher average resident acuity
• Higher percentage Medicare and lower
percentage Medicaid residents
• More nursing hours per resident day
• Located in markets with high average NH
occupancy

Facilities having a higher community
discharge rate (adjusted )
• Higher percentage of Medicare residents and
lower percentage Medicaid
• More nursing hours per resident day
• Higher facility occupancy rate
• Higher percentage of residents preferring or
having support for community discharge
• Located in markets with more CBLT recipients in
relation to NH recipients

Facility business model emphasizing
• Post-acute care&diversified revenue streams
(particularly Medicare and private pay)
• While Maintaining high occupancy levels
Sufficient nursing staff resources to
prepare residents for transitions
Facility culture that respects resident and
family preferences
Availability of CBLTC alternatives in the
local health care market

Community agencies should take the lead
in transition counseling, patient education,
CBLC referrals, etc.
Nursing facilities need to plan for and
facilitate transitions
Intervention should be targeted at
residents:
• Early in the NH stay (90-180 days)
• Want to return to the community
• Otherwise would become long stay
Targeted at facilities with low community
discharge rates

States should enact policies that encourage
facilities to:
• Reduce their unused bed capacity
• Balance their mix of payers
• Invest in nurse staffing
• Take other steps to develop business models
consistent with state goals for long-term care
rebalancing
States should expand HCBS funding,
particularly in markets with low community
discharge rates