Empirical Article

Longitudinal Changes in Nursing Home Resident–Reported Quality of Life: The Role of Facility Characteristics

Research on Aging 1–26 ª The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0164027514545975 roa.sagepub.com

Tetyana P. Shippee1, Hwanhee Hong2, Carrie Henning-Smith1, and Robert L. Kane1

Abstract Improving quality of nursing homes (NHs) is a major social priority, yet few studies examine the role of facility characteristics for residents’ quality of life (QOL). This study goes beyond cross-sectional analyses by examining the predictors of NH residents’ QOL on the facility level over time. We used three data sources, namely resident interviews using a multidimensional measure of QOL collected in all Medicaid-certified NHs in Minnesota (N ¼ 369), resident clinical data from the minimum data set, and facility-level characteristics. We examined change in six QOL domains from 2007 to 2010, using random coefficient models. Eighty-one facilities improved across most domains and 85 facilities declined. Size, staffing levels (especially activities staff), and resident case mix are some of the most salient predictors of QOL

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Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, USA 2 Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN, USA Corresponding Author: Tetyana P. Shippee, Division of Health Policy and Management, University of Minnesota, 420 Delaware Street SE, MMC 729, Minneapolis, MN 55455, USA. Email: [email protected]

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over time, but predictors differ by facility performance status. Understanding the predictors of facility QOL over time can help identify facility characteristics most appropriate for targeting with policy and programmatic interventions. Keywords longitudinal analysis, long-term care, nursing homes

Introduction Improving the quality of nursing homes (NHs) is a major social priority. To date, the primary measure of NH quality has been quality of care (QOC) as reflected in staff-reported clinical health outcomes (Castle & Ferguson, 2010). Although quality of life (QOL) is also a widely recognized central element of NH care, it has not been as widely addressed as QOC. Collecting information on QOL in addition to QOC helps to provide evidence-based feedback for providers and consumers and can be useful in targeting care improvements (Konetzka & Werner, 2010). Although efforts to improve QOL must consider resident-level characteristics to address case mix, such predictors are not likely to be targets of interventions. Instead, improving quality requires identifying elements of NH structure and the process of care. Most studies of NH QOL examine only a single point in time. Increasing our understanding of changes that improve QOL over time requires longitudinal designs (Xu, Kane, & Shamliyan, 2013). This study uses repeated cross-sectional data from Minnesota—one of the few states in the country that regularly collects QOL data—to examine change in facilityaggregated resident QOL over time and predictors of that change. Through better understanding of the predictors of facility-level QOL change over time, we can more effectively identify which NH characteristics should be targeted for policy and programmatic change. We can also then examine how existing policies have affected the predictors of QOL to date. In this article, we examine the relationship between NH facility-level characteristics and change in facility QOL over time using Donabedian’s QOC model (1992) and Zubritsky et al.’s (2013) framework. In addition, based on their QOL performance, we group facilities into QOL performance categories of ‘‘improved,’’ ‘‘declined,’’ and ‘‘mixed’’ and examine predictors of change on the facility level in QOL for each group. Understanding how NH facility characteristics (especially nonstatic factors) influence NH

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resident QOL over time can help identify facility characteristics most appropriate for targeting policy and programmatic interventions.

Literature Review QOL in nursing homes. QOL in NHs is a multidimensional construct that incorporates various aspects of residents’ lives and experiences, including physical environment and comfort, relationships with staff and other residents, food, activities, privacy, and autonomy (Burack, Weiner, Reinhardt, & Annunziato, 2012; Kane, 2001; Robinson, Lucas, Castle, Lowe, & Crystal, 2004; Shippee, Henning-Smith, Kane, & Lewis, 2013). QOL predictors have been understudied. However, development of QOL measures has allowed for examination of predictors of QOL at the resident and facility level (Degenholtz, Kane, Kane, Bershadsky, & Kling, 2006; Kane et al., 2003; Shippee et al., 2013). QOL is very sensitive to resident characteristics such as health status and physical/mental functioning (Abrahamson, Clark, Perkins, & Arling, 2012; Degenholtz et al., 2006; de Rooij, Luijkx, Declercq, & Schols, 2011; Elliott, McGwin, & Owsley, 2009; Fahey, Montgomery, Barnes, & Protheroe, 2003; Gonzalez-Salvador et al., 2000; Lucas et al., 2007; Mitchell & Kemp, 2000; Pekkarinen, Sinervo, Perala, & Elovainio, 2004). In theory, intervening on resident characteristics could improve QOL but in practice that strategy has limitations. For instance, it is unrealistic to use policy to change resident demographic characteristics that are related to QOL, such as age, gender, and health status. Instead, attention should be directed to institutional factors as important potential points of intervention. Facility characteristics such as size, ownership, location, staff retention, and staffing rates have been linked to NH quality performance (Allen, 2003; Harrington, Woolhandler, Mullan, Carrillo, & Himmelstein, 2001; Harrington, Zimmerman, Karon, Robinson, & Beutel, 2000; Munroe, 1990; O’Neill, Harrington, Kitchener, & Saliba, 2003). However, few of these studies focus explicitly on QOL. As mentioned, longitudinal studies are best for establishing links between putative factors that affect QOL, but most of the literature on QOL is cross sectional. The few studies that have examined changes in QOL over time at the resident level have found that both facility and resident characteristics are associated with changes in QOL. A study of the physical and care delivery transformation in Green House NH settings found traditional NH settings to be associated with worse QOL over time in comparison (Kane, Lum, Cutler, Degenholtz, & Yu, 2007). A different study of within-person change in QOL found that worse physical health is associated with declines in QOL

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over time (Degenholtz, Rosen, Castle, Mittal, & Liu, 2008). Similarly, lower initial QOL is associated with QOL declines over time (Selwood, Thorgrimsen, & Orrell, 2005)., Finally, a study looking at social engagement measures in the Minimum Data Set found that urinary incontinence and cognitive and functional decline (Dubeau, Simon, & Morris, 2006) are both associated with declines in resident QOL over time. A recent systematic literature review of NH QOL (Xu et al., 2013) identified one longitudinal study that examined QOL over time on the facility level, but the study focused only on Green House NHs (Kane et al. 2007). Conceptual frameworks. We draw on Donabedian’s QOC (1992) and Zubritsky et al.’s health-related QOL (2013) frameworks to better understand the relationship between NH facility characteristics and resident QOL. Donabedian’s model (1992) was developed to understand the causal sequences for providing QOC. It assumes that quality is a multidimensional construct, which consists of structure, process, and outcomes. Structural characteristics include organizational context in which the care is delivered. Process is the manner in which services are delivered. Outcomes are the effect of the health care on the health of individuals. The Donabedian framework, while useful for understanding the sequence of how a particular health care setting affects individual outcomes, does not incorporate individual characteristics as predictors of outcomes and is not specifically developed for long-term care (LTC) settings. A conceptual model developed by Zubritsky et al. (2013) focuses on health-related QOL for persons in LTC. The model views QOL as multidimensional and influenced by the characteristics of both individual and the environment. In this article, we focus on the role of environmental characteristics on resident QOL while accounting for resident-related covariates. Based on the Zubritsky et al.’s (2013) model, we conceptualize environmental predictors as consisting of both the physical environment (size and structure) and the organizational factors of the service system (care delivery and staffing). Previous research shows that facility size (Allen, 2003; Shippee et al., 2013), ownership (Harrington et al., 2001; O’Neill et al., 2003), location (Coburn, 2002), resident acuity case mix (Shippee et al., 2013), and nursing home staffing rates (Castle, Engberg, & Men, 2007; Harrington et al., 2000; Hyer et al., 2011; Shippee et al., 2013) are associated with resident experiences and thus are potentially important facility-level covariates in our models. We review the literature on the role of efforts aimed at improving NH environmental and structural characteristics for resident QOL subsequently.

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Efforts targeting structural NH characteristics aimed to improve QOL in NHs. Common strategies targeting structural NH characteristics aimed to improve NH resident QOL have included (1) changing the physical environment to make it less institutional (e.g., culture change), (2) changing care delivery and staffing (e.g., increasing nurse ratios), and (3) state-level efforts creating reimbursement policies aimed at NH care delivery to improve resident QOL. The most encompassing approach aimed at changing the structure of NHs falls under the culture change movement which includes altering physical environments to create more ‘‘home-like’’ residences, allowing more resident participation in directing daily activities, creating more opportunity for collaborative decision making, changing staffing patterns to facilitate closer relationships between staff and residents, and focusing on systematic quality improvement processes (Koren, 2010). Another approach focused on structural NH characteristics aimed to increase levels of direct care staffing and staff training to improve resident QOL (Berry, 2011; Hyer, Temple, & Johnson, 2009). However, the evidence on the role of staffing on resident QOL is mixed. Some studies have shown that nursing assistant staffing is associated with fewer QOL deficiencies (Harrington et al., 2000), but other studies have found that registered nurses, licensed practical/vocational nurses, and total nursing staff have no significant relationship with QOL (Degenholtz et al., 2006; Harrington et al., 2000; Johnson-Pawlson & Infeld, 1996; TemkinGreener, Zheng, Cai, Zhao, & Mukamel, 2010). Yet, activity staff hours were associated with higher QOL in cross-sectional studies (Abrahamson et al., 2013; Degenholtz et al., 2006; Shippee et al., 2013). Inconsistent findings about the role of staffing and QOL could be due to how staff are used (Castle & Ferguson, 2010), effectiveness of staff performance, and/or quality of training (Kane et al., 2004). Some states have introduced policies that specifically address QOL, such as nursing home quality incentive payments to improve QOC and QOL. A recent study shows the positive relationship between pay for performance and nursing home culture change implementation (Miller et al., 2013). In 2006, Minnesota created the performance-based incentive payment program (PIPP) as an alternative to standard pay-for-performance quality improvement programs (Arling et al., 2013). Instead of externally mandating interventions, PIPP invites individual facilities to develop quality improvement projects based on their own unique resident population and needs. Funding is awarded based on a competitive application process administered by the Minnesota Department of Human Services. Facilities set their own targets using measurable outcomes which they are expected to meet, at the risk of losing up to 20% of their project funding if they fall short. During the first

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four rounds of funding (2007–2010), 66 projects were initiated at 174 facilities and only 3 fell short of their target outcomes. This study allows us to examine the correlation of MN PIPP program participation with QOL from 2007 to 2010 by including a measure of PIPP participation as a facility-level predictor of QOL. Research aims. Guided by Donabedian’s QOC (1992) and Zubritsky et al.’s health-related QOL (2013) frameworks, we examined whether factors associated with QOL in cross-sectional designs also predict changes in facilitylevel QOL over time using repeated cross-sectional data at the facility level. We modeled predictors separately for facilities that show different patterns of QOL change.

Research Design Sample. We used data from 369 Medicaid-certified NHs in Minnesota from 2007 to 2010. An independent firm collected self-reported QOL survey data on a random sample of residents in each NH. We merged that data with residents’ clinical and payer data from the minimum data set (MDS). The associated facility characteristics were drawn from facility reports through the Minnesota Department of Human Services (DHS) and health inspection rating information from the Minnesota Department of Health (MDH). Resident QOL was compiled from the Resident Quality of Life and Satisfaction with Care Survey. The tool is administered annually to a random sample of residents in all Medicaid-certified nursing homes in Minnesota. It is conducted via a two-stage random sampling, in which facilities provide a list of long-stay and short-stay residents. The interview sample includes separate random samples of long- and short-stay residents at each facility (Vital Research, 2010). Residents were eligible for either list if they were not in isolation due to communicable illness and if their guardian did not decline participation on behalf of the resident. (Proxy interviews were not used in this study.) In 2010, 96% (27,724) of residents were eligible to participate and 58% (16,187), were sampled to be approached. Of these, 15% had unsuccessful interview attempts with the most common reasons being inability to respond (5%), refusal (4%), and severe cognitive impairment (2%), leaving a survey response rate of 85% (n ¼ 13,433). For 2010, our full models used 10,969 resident surveys. An average of 35 interviews per facility were completed (Vital Research, 2010). Face-to-face interviews used a 52-item survey covering previously validated QOL domains (Kane et al., 2003). The survey uses a simplified yes/

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no binary response structure to include respondents with mild to moderate cognitive impairment (except for mood items which use a Likert-type scale from 1 to 4). Response rates for the QOL questions matched those from the original tool development work, which demonstrated that QOL could be reliably assessed even among many NH residents with cognitive impairment (Kane et al., 2003). The majority (58%) of respondents missed only 1 to 5 of 52 items, with 14% responding to all 52 items. Patterns of missingness for the dependent variable differed by resident characteristics, with older, longer stay, and more cognitively impaired residents being less likely to have a complete survey. We addressed missing values by using the multiple imputation approach in Stata via the ‘‘mi’’ procedure (Rubin, 1996; StataCorp LP, 2011). All survey questions and selected resident characteristics were imputed at the resident level before creating the average QOL scores by facilities and years. We used pooled results from 10 imputed data sets to estimate the fixed effects. Findings were robust to alternate strategies of handling missing data, including list-wide deletion. To adjust for resident-level case mix, we used resident clinical data derived from MDS 2.0 data for all NH residents with a valid QOL report. MDS includes data on residents’ functional status, physical health, and other outcomes. All resident-level characteristics were aggregated to the facility level based on mean resident characteristics. Most independent variables had few missing values (less than 6%). The bulk of the facility-level characteristics came from facility reports to the DHS, including ownership type, size, resident acuity, metropolitan status, payer mix, nursing administrator turnover, staff hours per resident day, and other predictors. We had no missing data on facility characteristics. Measurement of QOL. Our measure of QOL consists of six QOL domains: environment, personal attention, food enjoyment, engagement, negative mood, and positive mood. This work is based on the original conceptual work by Kane et al. (2003) with updated factor analyses to better match the MN sample and the revised survey instrument (Shippee et al., 2013). We also constructed a summary score that includes all domains. In sensitivity analyses, we compared both unweighted and weighted scores. The findings were essentially unchanged. Our final tables display the unweighted overall summary score. The environment domain includes 4 items addressing ease of navigating one’s room, arrangement of personal items, and the ability to take care of one’s own possessions. Personal attention includes 6 items asking residents about whether staff members treat the resident politely and with respect,

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whether they are handled gently, whether they can get help when they need it, and whether they feel listened to by staff. Food enjoyment includes 3 items asking residents whether they enjoy the food and mealtimes and whether or not their favorite foods are served in the facility. Engagement includes 9 items asking whether there are activities that the resident enjoys, whether staff members know what the resident likes, whether they feel known as a person by staff and other residents, and whether they would consider any staff or other residents as friends. Negative mood includes 6 items asking residents how often in the past 2 weeks they have been bored, angry, worried, sad, afraid, or lonely. Finally, positive mood includes 3 items asking residents how often in the past 2 weeks they have felt peaceful, interested in things, and happy. In factor analyses, all domains loaded with a scores > .60 (Shippee et al., 2013). These domains present specific areas where QOL can be assessed and addressed with policy and programmatic interventions. To obtain facility-level QOL measurements, we averaged each of the residents’ six different QOL scores individually across each facility for each year. Same approach applies to the summary score. Thus, each facility has seven mean QOL scores (including the summary score) for every year. All domains except mood had a 0–1 scale initially; mood had 1–4 scale. For purposes of comparison, we rescaled all domains to 0–100, with higher scores indicating better QOL across domains. Negative mood was rescaled so that higher values indicate less negative mood. Covariates. In line with the Zubritsky et al’s. (2013) model, both facility- and resident-related characteristics (aggregated to facility level) were considered to be associated with QOL in NHs. Facility characteristics were broadly conceptualized as consisting of physical environment (size and structure) and organizational factors of the service system (care delivery and staffing). Facility characteristics. Physical environment characteristics included location (rural, metropolitan, and micropolitan), percentage of private rooms, and size (fewer than 75 beds vs. 75 beds or more). We also included aggregate resident acuity level (derived from the Minnesota Case Mix Classification Index, a score based on resource utilization groups, or RUGs, which is determined from MDS items and has been shown to predict utilization among NH residents), ownership (for profit, nonprofit, and government), chain status (part of a chain vs. not), and sources of payment upon admission on the facility level (percentage of residents on Medicare or Medicaid). Organizational characteristics. It consisted of care delivery and staffing measures. These include staff retention (percentage of staff not leaving each

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year) and hours of care per resident day by different staff specialties (e.g., activity staff, licensed social workers, certified nursing assistant; registered nurses (RNs) , and licensed practical nurses). We also included measures of QOC, a proxy for process of care delivery, a score from the Minnesota Nursing Home Report Card website, which uses established quality measures such as use of physical restraints, incidence of worsening bowel continence, incidence of cured pressure sores, and whether the facility participated in the Minnesota-based PIPP program in any year. All facility-related covariates are time varying except location and ownership. Resident-related characteristics. Resident characteristics are aggregated at the facility level to describe the composition of individual facilities rather than individual residents. Sociodemographic factors included mean length of stay (in years), percentage married (vs. widowed/divorced/never married), percentage with a high school education (vs. less than a high school education), percentage female, percentage White (vs. non-White), and mean age. Health characteristics included mean counts of selected chronic conditions (including cancer, Parkinson’s disease, multiple sclerosis, stroke, arthritis, diabetes mellitus, and hip fracture) scored from 0 to 4, where 0 ¼ no chronic conditions, 1 ¼ 1 condition, 2 ¼ 2 conditions, 3 ¼ 3 conditions, and 4 ¼ 4þ chronic conditions); percentage of residents with an anxiety or mood disorder; percentage of residents with Alzheimer’s disease; mean difficulties with activities of daily living (scored from 0 to 28; high score indicates more impairment); and mean cognitive status (0–6, recoded, with 0 indicating very severe impairment and 6 indicating no impairment). For resident-related independent variables, we calculated frequency or mean for dichotomous or continuous variables by facilities and years. Year is the time metric for the study.

Analytic Strategy The analysis was divided into two main stages. First, to examine predictors of change in QOL for all facilities in our sample across the 4 years, we estimated a series of mixed effects random coefficient models (Singer & Willet, 2003). This modeling approach observes change in the dependent variable and allows us to partition the variability in QOL into two components: between-facility variability and within-facility variability (Singer & Willett, 2003). The former expresses the amount of variation in QOL due to differences between facilities, while the latter expresses the variability in QOL

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within facilities over 4 years. The modeling features two levels of analysis, with time (Level 1) nested within facilities (Level 2). Second, to examine predictors of QOL over time for facilities that show different patterns of change, we grouped facilities into three performance categories, namely declined, mixed, and improved. To create these categories, we calculated binary indicators whether the facility mean score for each domain improved or declined from 2007 to 2010. Facilities that improved by at least one integer in at least 5 of 6 domains were categorized as ‘‘improved’’ (n ¼ 85). Facilities that had declined in at least 5 of 6 domains were categorized as ‘‘declined’’ (n ¼ 81). Facilities that showed declines or improvements in 2–4 domains were categorized as ‘‘mixed performance’’ (n ¼ 203). No facility failed to show any change. We used these cutoffs to examine more extreme cases while ensuring that we had sufficient statistical power to do so. For each group, we compared covariate characteristics in 2010 and then estimated random coefficient models predicting changes in the QOL summary score by performance status. In these models, we controlled for a binary measure of year (2007 vs. all subsequent years) as a means of adjusting for baseline and ceiling effects.

Results Table 1 presents sample characteristics from 2010 for all 369 NHs included in our study. An average of 31 residents were sampled per facility. Half of the facilities were located in an urban area (compared with rural or suburban), nearly half (47%) were large (75 beds or more), 60% were nonprofit, and half were part of a chain. The average acuity (case mix) score was 1.05 (range 0.10–1.41). The average facility mean for Medicaid on admission was 14% and the average facility mean for Medicare on admission was 69%. About three quarters of all staff were retained from the previous year and staff direct care hours per resident day ranged considerably by type of specialty, from 0.11 (licensed mental health/social workers) to 2.36 (certified nursing assistants). The mean quality improvement score was 71.09 (range 39.71–89.68) and 36% of facilities participated in PIPP (by 2010). Table 1 also lists resident characteristics aggregated at the facility level. Residents had a mean length of stay of just over 3 years. The majority (97%) of residents in our data had lengths of stay over 90 days. One fifth were married, approximately two thirds had a high school education or greater and were female. Nearly all residents were White. On average, 12% of residents had Alzheimer’s disease and 64% had some anxiety or mood disorder. Mean cognitive performance was 2.30 (out

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Table 1. Descriptive Characteristics of Nursing Homes, 2010. Range

Mean (SD)

Facility-level characteristics Metro location (vs. micro or rural) 0/1 0.50 Acuity 0.10–1.41 1.05 (0.11) Percentage of Private rooms 0/1 0.39 (0.29) Large size (>75 beds) 0/1 0.47 Ownership For profit 0/1 0.28 Nonprofit 0/1 0.60 Government 0/1 0.11 Chain status 0/1 0.51 Payer source (on admission) Medicaid 0–1.00 0.14 (0.15) Medicare 0–0.99 0.69 (0.17) Staff retention 0.28–0.97 0.74 (0.11) Direct care hours per resident day 3.76–6.53 5.08 (0.63) Activities staff 0–0.62 0.25 (0.09) CNAs 0–4.23 2.36 (0.47) Licensed mental health/social workers 0–1.22 0.11 (0.07) LPNs 0.13–1.63 0.72 (0.22) RNs 0–1.53 0.44 (0.20) Quality improvement score 39.71–89.68 71.09 (10.30) Participation in PIPP 0/1 0.36 Resident-level characteristics aggregated at the facility level Mean length of stay (years) 0.19–11.52 3.08 (1.09) % Marrieda 0/1 0.21 %High school education or higher 0/1 0.65 % Female 0/1 0.69 % White 0/1 0.97 Mean age 52.00–91.19 84.02 (6.30) Mean chronic disease count 0.07–1.86 1.09 (0.28) % Anxiety/mood disorders 0/1 0.64 % Alzheimer’s disease 0/1 0.12 Mean activities of daily living impairments 0.10–19.83 14.02 (2.88) Mean cognitive status 1.17–3.60 2.30 (0.52) N of facilityb 369 N of residc 31 (9) Note. CNA ¼ certified nursing assistant; LPN ¼ licensed practical nurse; RN ¼ registered nurse. All dichotomous variables are scored 0 and 1 (0 ¼ no or otherwise). The standard deviation of a dichotomous variable is omitted because it is a function of the mean. All continuous independent variables are reported with standard deviations in the parenthesis. Standard errors are reported for dependent variables, as they are constructed with imputed data. Mean proportion of dichotomous resident-level variables is marked with ‘‘%’’ in front of their variable names. a Never married, widowed, separated, and divorced are considered nonmarried. b Number of facilities participating in each year. c Average number of samples in each facility; numbers are rounded up at the first decimal points.

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of a possible 6 higher indicates better score; 0 would indicate very severe impairment, 6 would indicate no impairment. Facility mean cognitive performance scores ranged from 1.17 to 3.6.) In Table 2, we display the results from the mixed effects models to examine the effects of resident-related and facility-related predictors on change in QOL domains for all facilities in the sample during the 4-year observation period. We present coefficients with asterisks indicating level of significance (we omit standard errors due to space constraints available upon request). Year had a positive relationship with food and engagement but was not significant for any other domain. Domains differed in the number and type of significant predictors, but generally resident characteristics were more salient than facility characteristics. Among facility characteristics, more severe case mix (acuity) and large size had a negative effect on QOL in multiple domains. More activity staff and RN hours per resident day and better quality improvement scores had a positive effect on QOL in multiple domains. The effect of activities staff hours was particularly pronounced for social engagement, personal attention, and meal enjoyment domains. For every additional hour of activity staff time per resident day, there was a 9-point increase in the facility-aggregate social engagement score and an 8-point increase in the meal enjoyment score. Among resident characteristics, having more married, female, White, and older residents was positively related to multiple QOL domains, while higher percentage of anxiety/mood disorders, higher educational attainment, more functional limitations, and lower cognitive status had negative relationships across domains. Among domains, personal attention, food, and positive mood had the greatest number of significant facility covariates, suggesting that these domains may be most responsive to facility-level interventions. The random intercept variance for each model, which represents differences from the overall mean level of response for each domain, controlling for all covariates, ranged between 1.87 and 3.98. The random intercept variance was the smallest for environment and the highest for food enjoyment. It was significant for all domains, indicating significant interfacility variation in initial levels of QOL. The estimated slope variance ranged between 0.45 and 1.11, indicating generally low between-facility variation in slopes (also reflected in nonsignificant interactions between time and key independent variables; results available upon request). The estimated covariance between the intercept and the slope ranged from 0.54 (engagement) to 0.43 (environment). The negative covariances indicate that facilities with low initial levels of QOL at baseline had greater

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Year Facility-related covariates Metro location Acuity % Private rooms Large size (>75 beds) Ownership (ref. for profit) Nonprofit Government Chain status Payer source Medicaid Medicare Staff retention Direct care hours per resident day Activities staff CNAs Licensed mental health/social workers LPNs RNs Quality improvement score Participation in PIPP 1.45* 2.34** 0.03** 0.16

0.77 2.05 0.00 0.12

0.20 0.81 0.01

0.87 0.92 0.01 4.59** 0.08 2.99

0.68 0.20 0.55*

1.04* 0.84 0.25

2.84 0.29 4.42

0.13 9.27* 0.00 1.08*

0.10 4.27* 0.01 0.65*

0.81 3.60 0.00 0.19

0.17 4.03** 0.02 0.63

8.34** 0.47 0.12

0.30 1.74 0.01

0.89 1.15 0.22

0.32*

Food

0.14

Personal Attention

0.15

Environment

0.38 1.66 0.03 0.18

8.96*** 0.47 1.97

2.65 2.72 0.00

0.52 0.18 0.66

0.63 5.46 0.00 1.81***

0.26*

Engagement

0.26 1.69 0.01 0.25

5.89** 0.08 2.02

5.28* 3.08 0.02

0.21 0.60 0.39

0.81 1.73 0.01 0.21

0.26

Negative Mood

0.34 2.36* 0.04* 0.17

2.69 0.49 2.84

1.20 0.32 0.01

0.64 0.25 0.61

0.78 3.52 0.01* 0.80*

0.00

Positive Mood

(continued)

0.09 2.51** 0.02* 0.22

5.29*** 0.22 0.05

1.28 0.61 0.00

0.70* 0.60 0.45

0.36 5.18** 0.01 0.75**

0.15

Summary Score

Table 2. Mixed Effects Random Coefficient Models for QOL Scores in the Quality of Life Longitudinal Survey of Nursing Homes, 2007–2010.

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1.87 0.48 0.43 369

0.01 0.00 0.00 0.01 0.00 0.03 0.40 0.00 0.04 0.90*** 0.01

Environment

1.88 0.45 0.27 369

0.01 0.04** 0.01 0.01 0.08*** 0.24*** 0.58 0.01 0.00 0.09 0.95***

Personal Attention

3.98 1.11 0.34 369

0.73** 0.03 0.04** 0.00 0.16*** 0.17** 0.83 0.01 0.02 0.13 0.23

Food

3.22 0.65 0.54 369

0.26 0.05** 0.05*** 0.04* 0.13*** 0.03 1.55* 0.03* 0.03 0.02 1.49***

Engagement

2.56 1.06 0.31 369

0.13 0.00 0.03* 0.01 0.07* 0.22*** 1.02 0.05*** 0.02 0.22* 0.23

Negative Mood

2.12 0.88 0.27 369

0.10 0.04* 0.01 0.05*** 0.03 0.12* 0.74 0.03** 0.00 0.42*** 0.53

Positive Mood

2.13 0.67 0.31 369

0.10 0.03* 0.02* 0.02 0.07*** 0.13*** 0.67 0.02** 0.00 0.30*** 0.44

Summary Score

Note. QOL ¼ quality of life. Results are presented as coefficients. c1 ¼ the intercept variance; c2 ¼ the slope variance; c3 ¼ the covariance. All covariates are time varying except year, county, and ownership. *P < .05, **P < .01, ***P < .001.

Resident-related covariates Mean length of stay (years) % Married % High school education or higher % Female % White Mean age Mean chronic disease count % anxiety/mood disorders % Alzheimer’s disease Mean activities of daily living Mean cognitive status Random effects: Variance c1 c2 c3 n

Table 2. (continued)

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changes in QOL over time, compared to those with high initial levels of QOL. Our second question examined predictors of change in QOL by NH performance status. We created a score based on having at least a one integer change between 2007 and 2010 for at least five of six QOL domains; using that formulation, 81 NHs experienced declines, 86 NHs experienced improvement, and 203 NHs showed a mixed pattern. To examine possible ceiling effects common with satisfaction measures (Bowling, 2009), we present mean QOL scores by domain for 2007 and 2010, along with a change score between 2007 and 2010, both overall and by performance status. In all six QOL domains and the summary score, those facilities that declined between 2007 and 2010 started with higher mean scores than those that improved. Thus, performance status indicates movement between years, rather than better overall performance in any given year. These findings also show evidence of regression to the mean. To adjust for this in our statistical models, we use random coefficient models that account for baseline effects on overall change (Table 3). Table 4 presents the characteristics of NHs based on their patterns of change in QOL over time. Although the 3 different groups of NHs look similar on a number of characteristics, some key differences emerged in relation to rural/urban location, size, ownership, chain status, percentage private rooms, quality improvement score, and PIPP participation. Improving facilities were less likely than declining and mixed facilities to be in urban areas. They were also less likely to be large or to be government owned and more likely to be part of a chain. And they had a higher percentage of private rooms than declining facilities. Improving facilities were more likely to have higher quality improvement score and to have participated in PIPP than declining and mixed facilities. Few differences were observed regarding resident characteristics. Table 5 uses random coefficient models for subgroup analyses of QOL summary score based on their performance status, with a binary control for year (2007 vs. all subsequent years) as a means of controlling for ceiling effects. Among facilities where performance declined over time, metro location and more activity staff hours per day had a positive impact on overall QOL, while higher acuity had a negative impact on overall QOL. Among mixed facilities, metro location had a negative association with overall QOL score, and activity staff hours per resident day had a positive impact on overall QOL score. And, among facilities that improved, large size had a negative impact, while more RN hours per resident day and better quality improvement scores were positively associated with better QOL.

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Quality-of-life domains Environment Personal attention Food Engagement Negative mood Positive mood Summary score N of facility N of resid

85.0 92.2 86.4 79.2 65.6 77.0 80.9

2007

83.5 92.9 87.5 80.0 66.6 77.3 81.3 369 31 (9)

2010 1.5 0.7 1.1 0.8 1.0 0.3 0.4

Change

All Facilities

87.6 94.3 90.0 82.3 67.8 79.5 83.6

2007 81.5 90.9 84.4 77.5 63.0 74.4 78.6 81 30 (8)

2010

Declined

6.1 3.4 5.6 4.8 4.8 5.1 5.0

Change

Table 3. Mean Quality-of-Life Domain Scores by Performance Status.

85.3 92.5 86.4 79.5 65.6 77.2 81.1

2007 83.1 93.0 87.8 80.0 66.9 77.3 81.3 203 30 (8)

2010

Mixed

2.2 0.6 1.4 0.4 1.3 0.1 0.3

Change

81.7 89.3 82.6 75.4 62.9 74.3 77.7

2007

86.2 94.4 89.8 82.1 69.0 79.8 83.6 85 30 (8)

2010

4.5 5.1 7.2 6.8 6.1 5.5 5.8

Change

Improved

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Table 4. Facility Characteristics by Performance Status, 2010. Declined

Mixed

Improved

Facility-level characteristics Metro location (vs. micro or rural) 0.49 0.53 0.43 Acuity 1.04 (0.10) 1.05 (0.11) 1.04 (0.10) % private rooms 0.36 (0.27) 0.39 (0.29) 0.40 (0.30) Large size (>75 beds) 0.43 0.47 0.41 Ownership For profit 0.27 0.28 0.31 Nonprofit 0.62 0.60 0.61 Government 0.11 0.12 0.08 Chain status 0.45 0.50 0.56 Payer source (on admission) Medicaid 0.14 (0.16) 0.14 (0.14) 0.13 (0.15) Medicare 0.69 (0.19) 0.69 (0.16) 0.69 (0.17) Staff retention 0.76 (0.10) 0.74 ().10) 0.71 (0.12) Direct care hours per resident day 5.07 (0.59) 5.09 (0.62) 5.08 (0.71) Activities staff 0.24 (0.09) 0.25 (0.09) 0.25 (0.09) CNAs 2.33 (0.51) 2.38 (0.43) 2.32 (0.53) Licensed mental health/social 0.10 (0.05) 0.11 (0.04) 0.13 (0.13) workers LPNs 0.74 (0.21) 0.73 (0.21) 0.71 (0.24) RNs 0.42 (0.19) 0.44 (0.20) 0.45 (0.22) Quality improvement score 70.79 (10.60) 71.11 (10.02) 71.31 (10.79) Participation in PIPP 0.27 0.36 0.41 Resident-level characteristics aggregated at the facility level Mean length of stay (years) 3.09 (1.44) 3.10 (0.96) 3.01 (0.97) % Marrieda 0.21 0.21 0.21 % High school education or higher 0.65 0.66 0.63 % Female 0.67 0.70 0.69 % White 0.97 0.97 0.97 Mean age 83.42 (7.26) 84.44 (5.47) 83.47 (7.15) Mean chronic disease count 1.11 (0.31) 1.10 (0.27) 1.05 (0.28) % Anxiety/mood disorders 0.61 0.65 0.63 % Alzheimer’s disease 0.11 0.10 0.14 Mean activities of daily living 13.99 (2.98) 14.22 (2.85) 13.53 (2.85) impairments Mean cognitive status 2.29 (0.50) 2.32 (0.53) 2.26 (0.54) N of facilityb 81 203 85 N of residc 30 (8) 30 (8) 30 (8) Note. All dichotomous variables are scored 0 and 1 (0 ¼ no or otherwise). The standard deviation of a dichotomous variable is omitted because it is a function of the mean. All continuous independent variables are reported with standard deviations in the parenthesis. Standard errors are reported for dependent variables, as they are constructed with imputed data. Mean proportion of dichotomous resident-level variables are marked with ‘‘%’’ in front of their variable names. a Never married, widowed, separated, and divorced are considered nonmarried. b Number of facilities participating in each year. c Average number of samples in each facility; numbers are rounded up at the first decimal points. Downloaded from roa.sagepub.com at Selcuk Universitesi on February 5, 2015

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Table 5. Mixed Effects Model With Random Coefficient for QOL Summary Scores in the Quality of Life Longitudinal Survey of Nursing Homes. Declined 2007 (vs. later years) Facility-related covariates Metro location Acuity % private rooms Large size (>75 beds) Ownership (ref. for profit) Nonprofit Government Chain status Payer source Medicaid Medicare Staff retention Direct care hours per resident day Activities staff CNAs Licensed mental health/social workers LPNs RNs Quality improvement score Participation in PIPP Resident-related covariates Mean length of stay % Married % High school education or higher % Female % White Mean age Mean chronic disease count % Anxiety/mood disorders % Alzheimer’s disease Mean activities of daily living Mean cognitive status Random effects: Variance c1 c2 n

Mixed

Improved

0.58**

3.87***

1.40* 13.52** 0.02 0.09

0.96* 3.78 0.01 0.29

0.72 2.59 0.02 1.61*

1.05 2.05 0.18

0.76 0.14 0.27

1.00 1.91 0.90

2.90 0.76 0.01

1.19 0.85 0.01

1.66 0.31 0.01

9.49** 1.10 8.23 1.45 0.34 0.00 0.52

4.91* 0.37 1.75 0.01 1.44 0.01 0.44

2.86 0.21 1.77 0.79 5.33*** 0.04* 0.29

0.45 0.05* 0.07*** 0.03 0.01 0.24** 0.24 0.01 0.03 0.16 0.12

0.05 0.03 0.01 0.01 0.07** 0.15** 1.73** 0.03** 0.01 0.26*** 0.64*

0.11 0.01 0.02 0.00 0.14** 0.14* 0.16 0.03 0.03 0.36** 0.31

1.58 0.00 81

1.97 0.00 203

2.23 0.00 85

2.61***

Note. c1 ¼ the intercept variance; c2 ¼ the slope variance. All covariates are time varying except county and ownership. *p < .05, **p < .01, ***p < .001 (two-tailed tests).

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Discussion Summary We examined the relationship between NH resident multidimensional QOL and a broad array of facility-level characteristics while accounting for resident-related predictors. Our findings confirm that the factors associated with QOL in cross-sectional designs also predict changes in QOL over time, but the predictors differ across facilities that showed different patterns of change. Among facility characteristics, organizational factors, especially those related to different types of staffing, had consistent associations with various QOL domains. These findings help identify which facility characteristics may be most amendable to modification with largest gains for NH resident QOL. In our full sample analyses, guided by the Donabedian (1992) and Zubritsky et al. (2013) models, we examined the role of structural and organizational facility characteristics for NH resident QOL from 2007 to 2010 while also accounting for resident-relevant covariates. We found that structural characteristics, in particular greater resident acuity and larger facility size, had a significant negative association with facility-aggregated resident QOL. Nonprofit status, on the other hand (as compared to for profit), was positively associated with higher resident QOL. These relationships held across a number of domains, with a particularly large impact on overall scores for food/meal enjoyment, personal attention, and social engagement. Organizational characteristics had the most consistent associations across multiple QOL domains. In particular, staff hours of direct care (especially activity staff and RN hours) and quality improvement score had positive associations with QOL for a number of domains. Of these facility-relevant covariates, the findings about staffing hours are of particular interest. These findings point to the importance of not just the total number of staff hours per resident but of the effect that particular direct care providers has on resident’s overall QOL and the slope of QOL over time. Previous studies have linked nurse staffing to NH QOC (Castle & Anderson, 2011), and crosssectional studies have documented the importance of activity staff for QOL (Degenholtz et al., 2006; Shippee et al., 2013), but this is the first study to establish the importance of these staff members in longitudinal analyses for QOL over time. In addition, as suggested by the Zubritsky et al.’s (2013) model, resident-relevant predictors such as age, gender, race, marital status, functional, and cognitive health were consistent predictors across a number of domains. Grouping facilities into those that generally declined, had mixed performance, or improved in QOL from 2007 to 2010 allowed us to examine the

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effects on trajectories of change across facilities. Different predictors were significant for change in QOL domains across these groups. Among facilities that declined, acuity had a strong negative association with overall QOL improvement which could indicate decreasing capacity to handle more severe case mix over time (Lucas et al., 2007). By contrast, activity staff hours had a positive impact on overall QOL levels over time. The positive association between increases in QOL over time and activity staff hours suggests the importance of providing residents with participation in social or goal-directed activities. Interventions to improve QOL among this group of facilities may include increasing the number of hours per day of activity staff. NHs that showed mixed performance had the fewest significant predictors of change in QOL. However, they also showed a positive association with hours per resident day of activities staff. On average, facilities provided 15 min of activity staff per resident day. Increasing activity staff time may improve QOL across domains for declining and mixed performance NHs. Facilities that improved across domains had three significant predictors of change in QOL over time. First, large facility size had a strong negative association with overall QOL scores among these facilities. Second, RN hours per resident day (but not activity staff hours as we saw for two previous groups) had a positive association with higher levels of QOL over time. Third, quality improvement scores had positive associations with higher levels of QOL over time. Comparisons across declining, mixed, or improving facilities suggested differences between these groups in use of staff members and approaches to organizational aspects of care. RN hours per resident day had a positive association with QOL among improving facilities, while for declining or mixed facilities activity staff hours had a positive association with QOL. Perhaps high-performing facilities are more adept at engaging RNs with residents on a personal level, allowing them to develop meaningful relationships outside of the clinical encounter. In addition, improving facilities had higher RN hours at baseline. In improving facilities, RNs or other staff may be less squeezed and more able to provide better social engagement within the purview of their regular jobs (although this is speculative). In such cases, activities staff would be less vital to engagement than in mixed/declining facilities. Our finding that quality improvement scores had positive associations with QOL indicates that care delivery processes, for which QOC score is a useful proxy, have a significant consistent effect on QOL. Thus, efforts aimed at improving QOC may also indirectly benefit resident’s QOL.

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Limitations Our article, despite important and novel findings, has several limitations. First, our analyses use facility as a unit of analysis for changes in QOL over time while adjusting for aggregated resident characteristics. Our data preclude us from examining how individual resident’s QOL changes over time and what might predict such intraindividual change. Rather, we focused on identifying facility characteristics amenable to policy and programmatic intervention, which includes many facility characteristics but few, if any, resident characteristics. Additionally, our QOL scores must be interpreted with caution, both because of the tendency of older adults to rate satisfaction items high (Simmons & Schnelle, 1999) and because of data missing not at random. However, the majority of residents answered most questions, and we used a sophisticated imputation strategy (multiple imputation approach in Stata) to address this deficiency. Our measure of change was admittedly arbitrary but designed to fit the basic consistency of the data. Finally, although we identified facility characteristics that predict change over time in QOL, our measures were limited by data availability. More detailed and comprehensive facility-level measures are needed (especially as they relate to processes of care and innovations related to culture change). Future research should include more robust measures of physical environment and decision-making processes. Given the importance of staffing in our models, it would also be helpful to have more detailed data on staffing factors, including the use of agency staff, ‘‘universal workers,’’ and consistent resident assignment. Developing effective interventions will require additional work, including qualitative research that can disentangle some of the effects we identified in these analyses.

Conclusion Our findings reinforce the multidimensional nature of QOL and the ways in which QOL predictors vary by facility type. We found that organizational resources, especially staffing and the types of staff, have a consistent important impact on various domains of resident’s QOL. We also found that the number of significant facility-level correlates of QOL varied by domain, with personal attention having the greatest number of significant predictors. This may suggest that this domain is the most amenable to improvement using the facility-level characteristics we examine. In particular, we observed that staff hours per resident day (especially activities staff and nurses) can support positive change in QOL. This suggests that more funding and increased

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mandatory hours for staff could improve engagement and relationships between residents and staff. Further, our finding that severe case mix leads to lower QOL, especially in declining facilities, calls for environments and programs that better accommodate sicker, frailer residents. These findings could inform policy toward improving NH resident QOL. Acknowledgments The authors thank Minnesota Department of Human Services, Nursing Facility Rates & Policy Division for their support of this research.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding Support for this research was provided by the Fessler-Lampert Chair on Aging, University of Minnesota Center on Aging, and a grant from the National Center for Research Resources of the National Institutes of Health to the University of Minnesota Clinical and Translational Science Institute (1KL2RR033182-02) to the first author.

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Author Biographies Tetyana P. Shippee, PhD, is an assistant professor in the Division of Health Policy and Management at the University of Minnesota. Her research examines quality of life in long-term care and racial disparities in care. Email: [email protected] Hwanhee Hong, PhD, is a postdoctoral researcher in the Division of Biostatistics at the University of Minnesota. Her research interests are Bayesian hierarchical modeling, comparative effectiveness research, network meta-analysis, Medicare data analysis, clinical trial data analysis, and missing data. Email: [email protected] Carrie Henning-Smith, MPH, MSW, is a PhD candidate in the Division of Health Policy and Management at the University of Minnesota. Her research interests include aging and long-term care policy, demography, disability, and mental health. Email: [email protected] Robert L. Kane, MD, is a professor and Minnesota Chair in Long-Term Care and Aging and the Director of the Center on Aging at the University of Minnesota School of Public Health, Division of Health Policy and Management. Email: [email protected]

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