Medicaid-Financed Institutional Services: Characteristics of Nursing Home and ICF/IID Residents and Their Patterns of Care. C. Associations Between Lengths of Stay in Nursing Homes and State Policy-Related Variables

08/01/2013

Policymakers often want to know whether there is any association between their policies and the use of services at the state level. To explore this question, we assessed the relationship between the length of enrollees' nursing home stays and policy-related measures. Although these associations may be informative to those who seek to improve LTC services, we caution that causal relationships cannot be inferred from our findings.

Given states' efforts to re-balance their systems of LTSS toward greater provision of HCBS, it is natural to ask whether there is a relationship between the length of nursing home stays in a state and the amount of HCBS that the state provides. We examined the relationship between length-of-stay and four different measures of balance: (1) the percentage of Medicaid LTC expenditures allocated to HCBS; (2) the percentage of LTC users' total Medicaid expenditures accounted for by HCBS; (3) the percentage of Medicaid LTC recipients who used HCBS; and (4) the percentage of potential Medicaid LTC recipients -- namely, the number of low-income elderly or disabled individuals living in the state14 -- who used HCBS.

TABLE II.8. Changes in the Percentages of Very Short and Very Long Nursing Home Spells Associated with Increases in State Policy Variables
Policy Change Change in the Percentage
of First New Spells
  Lasting Less Than 3 Months 
(percentage points)
Change in the Percentage
of First New Spells
  Lasting More Than 12 Months  
(percentage points)
A 10 percentage point increase in the percentage of Medicaid LTC expenditures allocated to HCBS +2.5 -2.2
A 10 percentage point increase in the percentage of LTC users' total Medicaid expenditures accounted for by HCBS users +2.7 -2.3
A 10 percentage point increase in the percentage of Medicaid LTC recipients using HCBS +3.5 -2.8
A 10 percentage point increase in the percentage of potential Medicaid LTC recipients using HCBS   +5.3 -3.6
An increase of 10 in the number of nursing home beds per 1,000 elderly -2.6 +2.2
SOURCE: Mathematica analysis of 2006-2007 MAX data for 37 states and the District of Columbia with representative FFS nursing home and ICF/IID data and valid HCBS data (excludes data from Arizona, Indiana, Maine, Massachusetts, Michigan, Minnesota, Montana, New Hampshire, Oregon, Pennsylvania, Rhode Island, Texas, and Utah). Nursing home bed data were obtained from Houser et al. (2009) at http://assets.aarp.org/rgcenter/il/d19105_2008_ats_1.pdf (page 65). Figures for trimmed data are in Appendix Table D.3.
NOTES: New spells in this table are spells beginning between July 1, 2006, and December 31, 2006, inclusive; spells are censored at December 31, 2007. HCBS includes 1915(c) waiver services, personal care services, residential care services, home health, adult day services, and private duty nursing services.

In general, the percentage of nursing home stays lasting less than three months was higher in states that allocated a greater share of their Medicaid LTC expenditures to HCBS or had higher HCBS participation rates, although the magnitude of the measured associations was small. Increases of 10 percentage points in the four re-balancing measures were all associated with an increase in the percentage of first new spells lasting less than three months of 5 percentage points or less (Table II.8). There were corresponding decreases in the percentage of spells lasting more than 12 months.15 These findings, particularly those documenting positive relationships between HCBS spending/use as percentages of LTC spending/use and the percentage of stays lasting less than three months, are consistent with Wenzlow et al. (2008), who reached a similar conclusion using earlier data.16

  FIGURE II.2. The Relationship Between the Percentage of Medicaid LTC Expenditures Allocated to HCBS and the Length of Nursing Home Spells  
FIGURE II.2, Scatter graph: Shows the relationship between the percentage of Medicaid long-term care expenditures allocated to HCBS and the length of nursing home spells expressed as a regression of the percentage of all first nursing home spells lasting less than three months as a linear function of percentage of Medicaid long-term care expenditures allocated to HCBS. At the left end of the regression line, approximately 27% of nursing home stays lasted less than 3 months corresponding with 11% of Medicaid long-term care expenditures allocated to HCBS. The line increases slope, ending at 41% of nursing home stays lasting less than 3 months corresponding with 73% of Medicaid long-term care expenditures allocated to HCBS.
SOURCE: MAX, 2006-2007.

As noted earlier, these are not necessarily causal relationships. Greater spending on HCBS could reflect a state's commitment to providing services in the community to a broad population or more intensive services to a limited population, both of which would enable more elderly and individuals with disabilities to continue living in the community. If so, the share of nursing home stays lasting three months or less might decrease as LTC users were either transitioned or diverted from nursing home care while individuals requiring shorter-term post-acute care continued to use nursing facilities. At the same time, as more enrollees who are able to live in the community begin to use HCBS, nursing homes might continue to admit only the most highly impaired LTC users, resulting in a potentially higher share of stays exceeding one year. The results reported here suggest, on balance, a shortening rather than a lengthening of stays with increased provision of HCBS (Figure II.2 and Figure II.3).

  FIGURE II.3. The Relationship Between the Percentage of Medicaid LTC Users' Total Medicaid Expenditures Accounted for by HCBS Users and the Length of Nursing Home Spells  
FIGURE II.3, Scatter graph: Shows the relationship between the percentage of total Medicaid expenditures for LTC users that went for HCBS and the length of nursing home spells expressed as a regression of the percentage of all first nursing home spells lasting less than 3 months as a linear function of percentage of total Medicaid expenditures for LTC users that went for HCBS. At the left end of the regression line, approximately 26% of nursing home stays lasted less than 3 months corresponding with 22% of total Medicaid expenditures for long-term care users allocated to HCBS. The line increases slope, ending at 42% of nursing home stays lasting less than 3 months corresponding with 82% of Medicaid long-term care expenditures allocated to HCBS.
SOURCE: MAX, 2006-2007.

Greater rates of HCBS utilization were also associated with somewhat shorter stays (Figure II.4 and Figure II.5). Although this could reflect an emphasis on providing HCBS as an alternative to institutional care in states with high-HCBS utilization rates, it is also possible that Medicaid programs in states with healthier populations are able to serve more of their enrollees in the community whereas less healthy states have higher rates of institutionalization, something that we did not control for in this study.

  FIGURE II.4. The Relationship Between the Percentage of Medicaid LTC Recipients Using HCBS and the Length of Nursing Home Spells  
FIGURE II.4, Scatter graph: Shows the relationship between the percentage of Medicaid long-term care users receiving HCBS and the length of nursing home spells expressed as a regression of the percentage of all first nursing home spells lasting less than 3 months as a linear function of Medicaid long-term care users receiving HCBS. At the left end of the regression line, approximately 22% of nursing home stays lasted less than 3 months corresponding with 35% of Medicaid long-term care users receiving HCBS. The line increases slope, ending at 42% of nursing home stays lasting less than 3 months corresponding with 87% of Medicaid long-term care users receiving HCBS.
SOURCE: MAX, 2006-2007.

Finally, the length of nursing home stays was related to the supply of LTC beds in the state, with the percentage of stays lasting three months or less lower in states with a higher number of nursing home beds per 1,000 elderly living in the state (Figure II.6). This could reflect a number of influences. For example, a state might license a larger number of nursing home beds because of population characteristics or circumstances that necessitate providing more nursing home care. Iowa has one of the highest bed supplies but also the highest average age among nursing home residents of 81; in addition, it has a highly rural population, which makes provision of community services more difficult.

  FIGURE II.5. The Relationship Between the Percentage of Potential Medicaid LTC Recipients Using HCBS and the Length of Nursing Home Spells  
FIGURE II.5, Scatter graph: Shows the relationship between the percentage of potential Medicaid long-term care users receiving HCBS and the length of nursing home spells expressed as a regression of the percentage of all first nursing home spells lasting less than 3 months as a linear function of the percentage of potential Medicaid long-term care users receiving HCBS At the left end of the regression line, approximately 27% of nursing home stays lasted less than 3 months corresponding with 5% of potential Medicaid long-term care users receiving HCBS. The line increases slope, ending at 42% of nursing home stays lasting less than 3 months corresponding with 33% of potential Medicaid long-term care users receiving HCBS.
SOURCE: MAX, 2006-2007, and the ACS, 2007.

In addition to the variables described above, we also examined the association between the percentage of stays lasting less than three months and two variables related to bed-hold policies: (1) the percentage of the per diem rate that the state's Medicaid program reimbursed for held beds in 2007; and (2) the maximum number of days that the program would reimburse for held beds. Large numbers of states were clustered at zero, 100 percent (in the first instance), or two weeks (in the second instance), and no meaningful associations were detected.17

Finally, the length of nursing home stays was related to the supply of LTC beds in the state, with the percentage of stays lasting three months or less lower in states with a higher number of nursing home beds per 1,000 elderly living in the state (Figure II.6). This could reflect a number of influences. For example, a state might license a larger number of nursing home beds because of population characteristics or circumstances that necessitate providing more nursing home care. Iowa has one of the highest bed supplies but also the highest average age among nursing home residents of 81; in addition, it has a highly rural population, which makes provision of community services more difficult.

  FIGURE II.6. The Relationship Between the Number of Nursing Home Beds Per 1,000 Elderly and the Length of Nursing Home Spells  
FIGURE II.5, Scatter graph: Shows the relationship between the number of nursing home beds per 1,000 elderly and the length of nursing home spells expressed as a regression of the percentage of all first nursing home spells lasting less than 3 months as a linear function of the number of nursing home beds per 1,000 elderly. At the left end of the regression line, approximately 42% of nursing home stays lasted less than 3 months corresponding with 15 nursing home beds per 1,000 elderly. The line declines in slope, ending at 26% of nursing home stays lasting less than 3 months corresponding with 77 nursing home beds per 1,000 elderly.
SOURCE: MAX, 2006-2007, and Houser et al. (2009).

In addition to the variables described above, we also examined the association between the percentage of stays lasting less than three months and two variables related to bed-hold policies: (1) the percentage of the per diem rate that the state's Medicaid program reimbursed for held beds in 2007; and (2) the maximum number of days that the program would reimburse for held beds. Large numbers of states were clustered at zero, 100 percent (in the first instance), or two weeks (in the second instance), and no meaningful associations were detected.18

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