Abstract
Objective To compare quality of care in for-profit and not-for-profit nursing homes.
Design Systematic review and meta-analysis of observational studies and randomised controlled trials investigating quality of care in for-profit versus not-for-profit nursing homes.
Results A comprehensive search yielded 8827 citations, of which 956 were judged appropriate for full text review. Study characteristics and results of 82 articles that met inclusion criteria were summarised, and results for the four most frequently reported quality measures were pooled. Included studies reported results dating from 1965 to 2003. In 40 studies, all statistically significant comparisons (P<0.05) favoured not-for-profit facilities; in three studies, all statistically significant comparisons favoured for-profit facilities, and the remaining studies had less consistent findings. Meta-analyses suggested that not-for-profit facilities delivered higher quality care than did for-profit facilities for two of the four most frequently reported quality measures: more or higher quality staffing (ratio of effect 1.11, 95% confidence interval 1.07 to 1.14, P<0.001) and lower pressure ulcer prevalence (odds ratio 0.91, 95% confidence interval 0.83 to 0.98, P=0.02). Non-significant results favouring not-for-profit homes were found for the two other most frequently used measures: physical restraint use (odds ratio 0.93, 0.82 to 1.05, P=0.25) and fewer deficiencies in governmental regulatory assessments (ratio of effect 0.90, 0.78 to 1.04, P=0.17).
Conclusions This systematic review and meta-analysis of the evidence suggests that, on average, not-for-profit nursing homes deliver higher quality care than do for-profit nursing homes. Many factors may, however, influence this relation in the case of individual institutions.
Introduction
Nursing homes provide long term housing, support, and 24 hour nursing care for people who are unable to function independently. Conservative forecasts from the European Union suggest that the need for nursing home care will double in the next 40 years as the population ages.1 Many nursing home residents are bound to the routines, diets, and treatments prescribed by the home where they reside. In addition, many of them are unable to advocate for themselves because of physical, medical, cognitive, or financial limitations.
Concerns about quality of care in nursing homes are widespread among academic investigators,2 3 4 5 the lay press,6 7 8 9 10 11 and policy makers.1 12 Whether a facility is owned by a for-profit or a not-for-profit organisation may affect structure, process, and outcome determinants of quality of care. In the United States, for example, two thirds of nursing homes are investor owned, for-profit institutions; in the United Kingdom, more than half of healthcare beds belong to independent nursing homes for older people, most of which are operated by for-profit institutions.13 The type of ownership of nursing homes in Europe varies; countries with previously dominant public healthcare systems (such as Poland) now seek privatisation.14 In Canada, 52% of nursing homes are in for-profit ownership, and not-for-profit care is evenly split between charitable or privately owned not-for-profit facilities and government or publicly owned not-for-profit facilities.15 Both for-profit and not-for-profit nursing homes may have both public and private funding.
Several investigators have assessed the relation between for-profit/not-for-profit status and quality of care.16 If quality or appropriateness of care varies significantly by ownership, this should influence government policies related to regulatory assessments and the use of public funds for nursing homes. The objective of this systematic review and meta-analysis was to examine the quality of care in for-profit and not-for-profit (privately and publicly owned) nursing homes to enhance the evidence base for public policy. This work is part of our series of systematic reviews comparing health outcomes, quality and appropriateness of care, and payment for care in for-profit and not-for-profit care delivery institutions.17 18 19
Methods
Search strategy
We used a multimodal search strategy focused on 18 bibliographical databases, personal files, consultation with experts, reviews of references of eligible articles, and searches of PubMed (for related articles) and SciSearch (for articles citing key publications).
A medical librarian (NB) used medical subject heading terms and keywords from a preliminary search to develop database search strategies. In each database, the librarian used an iterative process to refine the search strategy through testing several search terms and incorporating new search terms as new relevant citations were identified. The search included the following databases from inception to April 2006: Medline, Embase, HealthSTAR, CINAHL, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Cochrane Central Database of Controlled Trials, NHS Economic Evaluation Database, AgeLine, Web of Science, Proquest Dissertations and Theses, ABI/INFORM Global, CBCA Reference, EconLit, Proquest European Business, PAIS International, and Worldwide Political Science Abstracts. Search terms included nursing home specific terms (such as nursing homes, homes for the aged, long-term care) combined with ownership terms (such as proprietary, investor, for-profit, and competition). The web appendix gives a complete description of our database search strategies.
Study selection
Eligibility criteria
Our inclusion criteria were as follows: patients—those residing in nursing homes in any jurisdiction; intervention—for-profit status of the institutions; comparator—not-for-profit status; and outcomes—measures of quality of care in for-profit and not-for-profit nursing homes.
Definition of quality of care
As described by the American Medical Association, quality of care is “care that consistently contributes to the improvement or maintenance of quality and/or duration of life.”20 Quality of care was conceptualised by Donebedian as having inter-related structure, process, and outcome components.21 Structure pertains to resources used in care (such as staffing). Process refers to action on the patient (such as use of restraint and urethral catheterisation). Outcome indicators assess the patient’s end result (such as pressure ulcers). Many quality of care instruments have been proposed, although none has been universally accepted.22 Consequently, we used measures that authors defined as representing “quality of care” or “appropriateness of care,” provided that they defined a priori what constituted “good” or “poor” quality of care. The most frequently used quality measures were as follows.
Number of staff per resident or level of training of staff—The US Medicare/Medicaid nursing home regulations emphasise the importance of this measure of structure.23 Studies have consistently shown a positive association between staffing and measures of both process and outcome quality.24 25 26
Physical restraints—Although use of physical restraints can prevent patients from injuring themselves, restraints diminish a patient’s self esteem and dignity. By restricting mobility, they lead to both physical deterioration and the formation of painful pressure ulcers.24 27 An Institute of Medicine report emphasised use of restraints as an important process measure.23
Pressure ulcers—The importance of this outcome quality measure was also stressed by the Institute of Medicine. Pressure ulcers are preventable and are associated with pain and infection risk.23
Regulatory (government survey) deficiencies—Deficiency citations by a regulatory body cover many aspects of nursing home care. Their strength lies in providing an overall measure of quality. Considerable work has gone into developing valid overall deficiency measures.4
Definition of nursing home
In keeping with other definitions,28 we defined a nursing home as a home for elderly people in which most residents require daily nursing care. We included all long term care facilities that met this definition, including those studies that specifically evaluated “skilled nursing facilities” and special care facilities such as those for patients with Alzheimer’s disease.
Assessment of study eligibility
Teams of two reviewers independently screened the titles and abstracts of all citations identified in our search, and if either reviewer thought that a citation might be eligible we retrieved the study for full text review. Research personnel who were not involved in the screening or data abstraction process masked the study results from the text and tables of potentially eligible articles by using a black marker. Teams of two reviewers independently evaluated each masked article to determine eligibility. All disagreements were resolved by consensus, with discussions with the project lead (VRC) about eligibility criteria as required. In the event of ambiguity about whether the outcome was a measure of quality of care, we erred on the side of being inclusive.
Data extraction and study quality evaluation
Multiple teams of two reviewers independently abstracted data from included articles. We collected data on geographical area, year, data source, unit of measurement (number of residents or nursing homes), and quality of care measure. We developed and applied a 0-5 scale for evaluating appropriate adjustments and a yes/no scale for inappropriate adjustments (box). We explored whether appropriate and inappropriate adjustment explained heterogeneity. Disagreements were resolved by consensus, with consultation of a third investigator when resolution could not be achieved.
Evaluation of quality of studies used in meta-analyses: appropriate and inappropriate adjustments
Appropriate adjustments (0-5)
One point for each of:
Having an adjusted analysis
Adjusting for age
Adjusting for severity of illness (comorbidities)
Adjusting for presence or absence or severity of dementia
Adjusting for payment status of residents (government funded v privately funded)
Inappropriate adjustments (yes/no)
Yes for adjusting for potential quality of care measures (that is, elements used to assess quality of care in a different study, such as pressure ulcer, restraint use, urinary catheterisation, staffing, or regulatory agency citations)
Statistical analysis
Many studies had for-profit versus not-for-profit comparisons including multiple measures of quality of care. When summarising results, we classified studies into three categories. (1) “All statistically significant differences favoured one ownership type”—studies fulfilled two requirements: at least one outcome with P<0.05 favoured either for-profit or not-for-profit and all outcomes with P<0.05 favoured the same funding structure (that is, all favour not-for-profit or all favour for-profit). (2) “Most but not all significant differences favoured one ownership type”—studies fulfilled two requirements: at least four quality measures had P<0.05 and three times as many outcomes with P<0.05 favour one ownership as favour the other. (3) “Mixed results”—all other results.
We pooled outcomes by using random effects models separately for the most frequently used quality of care measures: number of staff or level of training of staff, pressure ulcers, physical restraints, and regulatory (government survey) deficiencies. We considered P<0.05 to be statistically significant.
We used prevalence, rather than incidence, in reporting physical restraint use and pressure ulcers based on authors’ reporting of study outcomes. We report the odds ratios and 95% confidence intervals for these outcomes. When necessary, we converted other effect measures to odds ratios by using available data. For example, if the study reported a relative risk (RR) and the event proportion in for-profit nursing homes (Pfp), the odds ratios was calculated as (RR×(1− Pfp))/(1−Pfp×RR). Similarly, when the studies presented a β coefficient (an adjusted result representing difference in event proportions in for-profit and not-for-profit nursing homes, Pfp−Pnfp), if the event proportion (Pc) in the study population and sample sizes (Nfp and Nnfp) of the nursing homes in for-profit and not-for-profit were provided, solving the following two equations for Pnfp and Pfp, we computed the odds ratio: Pfp−Pnfp=β and (Pfp×Nfp+Pnfp×Nnfp)/(Nfp+Nnfp)=Pc. For the outcomes of deficiencies and staffing, we used the ratio of the effect from not-for-profit to for-profit nursing homes in pooling studies.
We avoided repetition of data on the same resident from different studies by preferentially using data from the larger dataset when necessary. One author (GHG) made these decisions by using blinded copies of articles while unaware of study outcomes. We requested supplemental data when available data was insufficient for analysis. We evaluated heterogeneity with both a χ2 test and the I2 statistic, interpreting a low I2 as 25% or lower and a high I2 as 75% or higher.29 We examined funnel plots for evidence of publication bias. We applied a univariate meta-regression random effects model to each pooled outcome to evaluate potential sources of heterogeneity.
Hypotheses to explain heterogeneity
Our a priori hypotheses for sources of potential heterogeneity included analysis of privately owned and publicly owned nursing facilities in the same category, appropriate and inappropriate adjustments, the year of data collection, geography and political environment, and primary compared with secondary data collection. We did univariate meta-regression for each potential cause of heterogeneity. We present subgroup results if the likelihood of the differences between subgroups being due to chance was P<0.10. Our a priori hypotheses to explain heterogeneity are detailed below.
Analysing privately and publicly-owned not-for-profit facilities in the same category—We hypothesised that privately owned not-for-profit facilities may deliver superior care compared with publicly owned facilities, and thus comparisons between not-for-profit and for-profit facilities may yield different results if publicly owned facilities are included, as seen in previous studies.19 We decided, a priori, to present the result of a for-profit versus privately owned not-for-profit meta-analysis separately from a for-profit versus not-for-profit meta-analysis regardless of whether privately or publicly owned not-for-profit status explained heterogeneity of the pooled estimate.
Extent of appropriate and inappropriate adjustment—We have defined concepts of appropriate and inappropriate adjustment in the data extraction section above. We compared studies with above median scores against those with scores below the median for assessment of appropriateness. Similarly, we compared studies with inappropriate adjustment against those without inappropriate adjustment, excluding studies that did not have an adjusted analysis.
Year of data collection—Legislation on quality of care in nursing homes was introduced in the United States under the Federal Nursing Home Reform Act (part of Omnibus Budget Reconciliation, 1987). Most of the studies we reviewed were from the United States. As a result, we compared data collected before and during 1987 versus after 1987.
Geography—We compared data collected inside and outside the United States, as geography and political environment are potential sources of heterogeneity.
Primary versus secondary data collection—We compared data acquired by primary (direct) data collection with those acquired by secondary (administrative agency) data collection.
Results
Of the 8827 articles screened, we selected 956 for blinded full text review. Figure 1 details the steps in this review. Our agreement on the eligibility of studies was very good (κ=0.73 on the basis of two questions: does the study evaluate nursing homes, and does the study compare quality of care in for-profit and not-for-profit facilities?). Disagreements stemmed from implied but not stated definitions in the articles regarding good and poor quality and implied but not stated quality of care measures. We requested supplementary data from 36 authors; 25 responded, of whom three did new analyses in response to our queries.
Fig 1 Flow chart of steps in systematic review
We found 82 studies, spanning 1965 to 2003, comparing for-profit and not-for-profit nursing homes.w1-w82 We found 40 studies in which all statistically significant analyses (P<0.05) favoured not-for-profit homes and three in which all statistically significant analyses favoured for-profit homes. Similarly, 34 studies compared for-profit and privately owned not-for-profit nursing homes. In 16 of these, all statistically significant comparisons favoured higher quality in privately owned not-for-profit homes; none had all statistically significant analyses favouring higher quality in for-profit homes.
Tables 1 and 2 present a summary of the characteristics and outcomes of all studies included in this review and summarise the results of comparisons for quality measures evaluated by three or more studies. Tables 3 and 4 present the detailed study characteristics and outcomes of those studies that compared for-profit and privately owned not-for-profit facilities. Similarly, tables 5 and 6 present the detailed study characteristics and outcomes of studies that compared for-profit and not-for-profit (publicly and privately owned) facilities.
Table 1.
Number of studies with quality of care comparisons favouring particular ownerships*: overall and staffing results
| Quality of care measure | Summary of study characteristics | All statistically significant comparisons favoured NFP | Most statistically significant comparisons favoured NFP | Mixed results or direction unclear | Most statistically significant comparisons favoured FP | All statistically significant comparisons favoured FP |
|---|---|---|---|---|---|---|
| Quality overall with any quality of care measure (FP v NFP) | 82 studies with data from 1965-2003 (1 from Australia, 5 from Canada, 1 from Taiwan, 74 from United States); 15 collected primary data, and 1 supplemented primary data with government survey data | 40 | 2 | 37 | 0 | 3 |
| Quality overall with any quality of care measure (FP v private NFP) | 34 studies with data from 1965-2003 (1 from Australia, 1 from Canada, 38 from United States); 3 collected primary data, and 1 supplemented primary data with government survey data | 16 | 2 | 16 | 0 | 0 |
| More, or more extensively trained, staff | 23 comparisons with data from 1965-2003 (2 from Canada, 21 from United States) | 16 | 0 | 7 | 0 | 0 |
FP=for-profit; NFP=not-for-profit.
*Studies were classified into three categories: “all significant differences favour one ownership type” (at least one outcome with P<0.05 favoured either FP or NFP and all outcomes with P<0.05 favoured the same ownership—that is, all favoured NFP or all favoured FP); “most, but not all, significant differences favoured one ownership type” (at least four quality measures with P<0.05 and three times as many outcomes with P<0.05 favoured one ownership than favoured the other); “mixed results” (all other results).
Table 2.
Number of studies with quality of care comparisons favouring particular ownerships: other results*
| Quality of care measure | Summary of study characteristics | Favoured NFP (P<0.05) | Non-significantly favoured NFP | Direction unclear | Non-significantly favoured FP | Favoured FP (P<0.05) |
|---|---|---|---|---|---|---|
| Lower pressure ulcer prevalence | 24 comparisons with data from 1984-2003 (1 from Canada, 23 from United States) | 7 | 10 | 3 | 3 | 1 |
| Lower physical restraint prevalence | 21 comparisons with data from 1987-2003 (all from United States) | 10 | 4 | 0 | 3 | 4 |
| Fewer deficiencies on government surveys | 19 comparisons with data from 1976-2003 (all from United States) | 10 | 5 | 2 | 2 | 0 |
| Lower urethral catheterisation prevalence | 10 comparisons with data from 1984-2003 (all from United States) | 4 | 2 | 3 | 0 | 1 |
| Lower mortality | 4 comparisons with data from 1984-99 (1 from Canada, 3 from United States) | 1 | 2 | 1 | 0 | 0 |
| Lower psychoactive drug use prevalence | 4 comparisons with data from 1997-2003 (all from United States) | 3 | 1 | 0 | 0 | 0 |
| More feeding tubes | 3 comparisons with data from 1990-9 (all from United States) | 3 | 0 | 0 | 0 | 0 |
| Lower hospital admission rate | 3 comparisons with data from 1994-9 (1 from Canada, 2 from United States) | 1 | 1 | 0 | 1 | 0 |
FP=for-profit; NFP=not-for-profit.
*Single overall comparisons were made for each of pressure ulcer, physical restraint, and deficiency outcomes, rather than multiple comparisons being made within the same study.
Table 3.
Characteristics of studies comparing private for-profit and private not-for-profit nursing home quality of care
| Study | Place; year; data source*; No of residents or nursing homes | Factors controlled or adjusted for | |
|---|---|---|---|
| Appropriate: age, severity of illness, severity of dementia, and payment status adjustments | Inappropriate: quality measures used in other studies; measures of intensity of care | ||
| Levey et al 1973w1 | Massachusetts; 1965 and 1969; state public health department; 129 homes in each year | Payment status | None |
| Cohen and Dubay 1990w2 | United States; 1981; MMACS; 694 FP and 235 private NFP homes | Severity of illness (long term care index of function), dementia (% confused or disoriented), payment status (% of Medicare patients in facility) | None |
| Elwell 1984w3 | New York state; 1976; Residential Health Care Facilities Report (NY); 258 FP and 130 private NFP homes | Severity of illness (ADLs), dementia (proportion of residents with totally impaired alertness), payment status (proportion of days paid for by Medicaid) | None |
| Lee 1984w4 | Iowa; 1980-1; Iowa Department of Health; 254 FP and 103 private NFP homes | Unadjusted analysis | Unadjusted analysis |
| Wiesbrod and Schlesinger 1986w5 | Wisconsin; 1976; State Division of Health; 220 FP and 134 private NFP homes | Adjusted analysis but none of 4 selected appropriate factors included | None |
| Lemke and Moos 1989w6 | United States; year not listed; research nurses; 44 FP and 44 private NFP homes | Unadjusted analysis | Unadjusted analysis |
| Pearson et al 1992w7 | Australia; 1988-90; authors collected data; 120 FP and 80 private NFP homes | Severity of illness (% of high need residents) | Staffing (% of nurses who were RNs) |
| Graber 1993w8 | North Carolina; 1991; OSCAR; 167 FP and 14 private NFP homes | Unadjusted analysis | Unadjusted analysis |
| Aaronson et al 1994w9 | Pennsylvania; 1987; MMACS; 269 FP and 180 private NFP homes | Varied by analysis: staffing—severity of illness (long term care index of resident function), payment status; pressure sores—age (% aged ≥85), severity of illness (long term care index of resident function), payment status; restraint use—dementia (proportion of confused patients per 100 beds), payment status (Medicaid use rate) | Varied by analysis: staffing—none; pressure sores—restraint use; restraint use—RN to resident ratio |
| Moseley 1994w10 | Virginia; 1983-5; state medical assistance services using long-term care information system; 174 homes with 2362 FP and 787 private NFP residents | Age, severity of illness (ADLs), dementia (oriented/disoriented) | None |
| Sainfort et al 1995w11 | Wisconsin; 1982; research teams; 44 FP and 46 private NFP homes | Unadjusted analysis | Unadjusted analysis |
| Holmes 1996w12 | Michigan; 1989; MMACS; 275 FP and 60 private NFP homes | Severity of illness (ADLs), payment status (% Medicaid patient days), dementia (% of residents with cognitive deficiencies) | None |
| Johnson-Pawlson and Infeld 1996w13 | Maryland; 1991-2; OSCAR; 137 FP and 55 private NFP homes | Severity of illness (ADLs), payment status (% of residents covered by Medicare) | Staffing (RN and full time equivalent nurse positions/patient) |
| Spector and Fortinsky 1998w14 | Ohio; 1994; MDS; 843 homes | Age, dementia (cognitive performance) | None |
| Spector et al 1998w15 | United States; 1987; NMES; 1695 FP and 535 private NFP homes | Age, dementia, payment status (Medicaid coverage %) | None |
| Hughes et al 2000w16 | Continental United States; 1997; OSCAR; 10 666 FP and 3342 private NFP homes | Dementia, payment status | Staffing (in facility model), antidepressant drug use |
| Troyer 2001w17 | Florida; 1994-6; OSCAR; unclear | Payment status (private pay/Medicaid/Medicare funding) | None |
| Chou 2002w18 | United States; 1984-94; NLTCS; 1770 FP and 1044 private NFP residents | Age, severity of illness (ADLs, before admission), dementia (cognitive score on admission) | None |
| Harrington et al 2002w19 | United States; 1997-8; OSCAR; 9009 FP and 3789 private NFP homes | Severity of illness (ADLs), dementia (in secondary analysis only), payment status (% Medicaid residents) | None |
| Grabowski and Hirth 2003w20 | United States; 1995; OSCAR; 11 174 FP and 4688 private NFP homes | Severity of illness (ADLs), payment status | None |
| Berta et al 2004w21 | Ontario; 1996-2002; RCFS; not clear | Unadjusted analysis | Unadjusted analysis |
| Grabowski and Angelelli 2004w22 | United States; 1998-2000; OSCAR and MDS; 9478 FP and 3434 private NFP homes | Adjusted analysis but none of 4 selected appropriate factors included | None |
| Grabowski and Castle 2004w23 | United States; 1991-9; OSCAR; 18 432 homes, selecting those with 5 consecutive yearly assessments with upper and lower quartile scores for each quality measure | Unadjusted analysis | Unadjusted analysis |
| Grabowski 2004w24 | Continental United States; 1996; MEPS and OSCAR; 815 homes, with 1856 FP and 673 private NFP residents | Age, severity of illness (ADLs), dementia, payment status | None |
| Grabowski et al 2004w25 | United States; 1998-9; MDS and OSCAR; 15 128 homes (13 819 for daily pain information, 13 169 for pressure ulcer information, 13 859 for physical restraint information) | Adjusted analysis but none of 4 selected appropriate factors included | None |
| Konetzka et al 2004w26 | United States; 1996-2000; OSCAR; 11 968 FP and 5077 private NFP homes | Severity of illness (ADLs), dementia (% with), payment status (% private pay) | None |
| Konetzkaet al 2004w27 | United States; 1996; MEPS; 529 FP and 192 private NFP residents | Severity of illness (ADL dependence), dementia (cognitive performance), payment status (payer source) | Staffing (RNs and LPNs/100 residents, nursing assistants/100 residents |
| Lapane and Hughes 2004w28 | Ohio; 1997 and 2000; MDS; 390 FP and 109 private NFP in 1997; 391 FP and 114 private NFP homes in 2000 | Age, dementia, payment status (% of residents with Medicaid/Medicare) | None |
| Lapane and Hughes 2004w29 | IL, MA, MS, NY, OH, and SD; 2000; MDS and OSCAR; 1560 FP and 494 private NFP homes | Age, dementia (cognitive functioning), payment status (% of residents being paid for by Medicare/Medicaid) | Staffing (RN and LPN full time equivalents, and nursing assistants per 100 beds) |
| Rantz et al 2004w30 | Missouri; 2000-1; MDS and research nurses; 60 FP and 26 private NFP homes | Unadjusted analysis | Unadjusted analysis |
| Zhang and Grabowski 2004w31 | United States; 1987—MMACS, 1993—OSCAR; 5092 facilities for matched analysis between the 2 years | Severity of illness (ADL score), payment status (proportion Medicare funded) | None |
| Akinci and Krolikowski 2005w32 | Northeastern Pennsylvania; 2000-2; OSCAR; 46 FP homes and 38 private NFP homes | Unadjusted analysis | Unadjusted analysis |
| Bardenheier et al 2005w33 | United States; 1995, 1997, and 1999; NNHS; 1409 homes in 1995, 1488 in 1997, and 1423 in 1999 | Age, payment status (payment source) | None |
| Zinn et al 2005w34 | United States; 2002-3; MDS; 10 763 FP, 4802 private NFP, 994 public | Adjusted analysis but none of 4 selected appropriate factors included | None |
ADLs=activities of daily living; LPN=licensed practical nurse; FP=for-profit; NFP=not-for-profit; RN=registered nurse.
*MDS (minimum data set): quarterly survey of residents in US Medicaid certified facilities (resident level quality assessed); MMACS (Medicare and Medicaid Automated Certification System)/OSCAR (Online Survey, Certification and Reporting): facility level survey completed every 9-15 months for US Medicare/Medicaid certification (OSCAR replaced MMACS in 1991); MEPS (Medical Expenditure Panel Survey): see NMES; NMES (National (US) Medical Expenditure Survey): survey of nationally representative sample of people in nursing and personal care homes and facilities for mentally challenged people (collects information on health expenditures); NLTCS (National (US) Long Term Care Survey): survey of nationally representative sample of elderly, disabled, Medicaid beneficiaries in community or institutional settings (tracks expenditures, family caregiving, and Medicaid service use); NNHS (National (US) Nursing Home Survey): survey of nationwide sample of nursing homes conducted by the National Center for Health Statistics (includes non-Medicare/Medicaid facilities; tracks service use and costs); OSCAR: see MMACS; RCFS (Residential Care Facilities Survey): Statistics Canada census of residential care facilities.
Table 4.
Quality of care measures and outcomes of studies comparing private for-profit and private not-for-profit nursing homes (favoured directions represent those with higher quality care)
| Study | Quality measure | Outcome |
|---|---|---|
| Levey et al 1973w1 | Dietary options; doctor’s order book showing activity; nursing kardex showing activity; activities for patients’ availability (religious, recreation); patients’ records being complete; personal care availability; physical plant utilities; restorative services availability; staffing—No of nursing shifts not covered per week, licensed nursing hours, total nursing hours | Mixed results: not significant for all measures (direction not noted) |
| Cohen and Dubay 1990w2 | Staffing: RNs, LPNs per bed | Mixed results: non-significantly favoured private NFP |
| Elwell 1984w3 | Multi-bed rooms (proportion of patients in them); staffing—allied health hours/resident/day, nursing hours/resident/day, physician hours/resident/week, RN hours/resident/day | Most significant comparisons favoured private NFP: having fewer multi-bed rooms favoured FP (P<0.001); all other measures favoured private NFP (P<0.025) |
| Lee 1984w4 | Nursing and personal care delivery index (by inspection and resident interviews on 17 items); quarterly care review completion; residents’ satisfaction by interview; room conditions ratings by inspection; staffing—staff/resident ratio | Mixed results: resident satisfaction by interview and room conditions by inspection favoured FP (P<0.05); favoured private NFP for quarterly care review completion and staffing; non-significantly favoured private NFP for nursing and personal care delivery (P=0.077) |
| Wiesbrod and Schlesinger 1986w5 | Deficiencies in Wisconsin licensing survey | Mixed results: non-significantly favoured private NFP for church owned homes; non-significantly favoured FP for non-church owned (P<0.1) |
| Lemke and Moos 1989w6 | Service availability; staff richness; staffing—No of full time equivalent staff members/resident; subjective comfort; subjective control; subjective rapport; subjective resident autonomy; subjective security | All significant (P<0.05) comparisons favoured private NFP: only significant difference was for subjective rapport, which favoured private NFP; private NFP also offered more comfortable physical environment and more health services |
| Pearson et al 1992w7 | Freedom of choice; healthcare treatment; home-like environment; privacy and dignity; social independence; variety of experience | All significant (P<0.05) comparisons favoured private NFP: non-significantly favoured private NFP for having healthcare treatment, privacy, and dignity; favoured private NFP for all others |
| Graber 1993w8 | Deficiencies in OSCAR; ombudsman office complaints | All significant (P<0.05) comparisons favoured private NFP: non-significantly favoured private NFP overall; non-significantly favoured private NFP for deficiencies, but significantly favoured private NFP for complaints (P<0.01) |
| Aaronson et al 1994w9 | Pressure ulcer prevalence; restraint use prevalence; staffing—RNs, LPNs, and aides per 100 beds | Mixed results: favoured FP for pressure ulcer (P<0.05); favoured private NFP for staffing (P<0.05); non-significantly favoured private NFP for restraint use |
| Moseley 1994w10 | Composite measure of inappropriate care (underprovision of routine medical care, skilled nursing care, and physical therapy or overprovision of psychotropic drugs, physical restraints, or urinary catheterisation); functional improvement over 9 months | All significant (P<0.05) comparisons favoured private NFP: favoured private NFP (P<0.001) for composite measure; non-significantly favoured private NFP for functional improvement |
| Sainfort et al 1995w11 | Outcome based quality such as grooming, mood, awareness of condition, physical condition, promotion of family ties, continuity of lifestyle; process based quality such as plan of care, medical records, planning and evaluation, admission/transfer, residents’ influence, staff’s attitudes to residents, staff communication, communication between residents, variety/adequacy of activities, match of residents to activities, volunteer programme, meal variety/presentation, nutrition/diet | Mixed results: non-significantly favoured private NFP for outcome measures; difference not stated for process measures |
| Holmes 1996w12 | Deficiencies in MMACS per facility | Favoured private NFP (P value not stated) |
| Johnson-Pawlson and Infeld 1996w13 | Deficiencies in Long-Term Care Survey | Non-significantly favoured private NFP |
| Spector and Fortinsky 1998w14 | Pressure ulcer prevalence | Non-significantly favoured private NFP |
| Spector et al 1998w15 | Functional disability at year end; hospital admission incidence; infection prevalence; mortality during 1987; pressure ulcer prevalence | All significant (P<0.05) comparisons favoured private NFP: non-significantly favoured FP for functional disability and hospital admission incidence; non-significantly favoured private NFP for pressure ulcer prevalence; favoured private NFP for infection prevalence (P<0.05); non-significantly favoured private NFP for mortality (P<0.1) |
| Hughes et al 2000w16 | Psychotropic drug use (use is poorer quality than no use); deficiencies in OSCAR per resident day; staffing—No of RN hours/day, total No of nursing hours per patient day | Favoured private NFP (for all measures) (P<0.001) |
| Troyer 2001w17 | Deficiencies in OSCAR per resident day | Favoured private NFP (P<0.05) |
| Chou 2002w18 | Mortality; prevalence of dehydration, pressure ulcers, and urinary tract infection | Mixed results: non-significantly favoured private NFP for all measures except pressure ulcer prevalence, which non-significantly favoured FP |
| Harrington et al 2002w19 | Deficiencies in OSCAR (quality care)*; staffing—(RN + LVN/LPN hours)/resident day and nursing assistant hours/resident day | Favoured private NFP for all three measures |
| Grabowski and Hirth 2003w20 | Prevalence of feeding tube, pressure ulcers, restraint use, and urinary catheterisation; staffing—proportion of total staff who are RNs, total nurse staff/resident/day | Most significant comparisons favoured private NFP: favoured private NFP (P<0.01) for all measures except urinary catheterisation prevalence, which favoured FP (P<0.01) |
| Berta et al 2004w21 | Staffing—RN + nursing assistant hours/resident/day, other direct care staff hours/resident/day | Favoured private NFP (P<0.05) in comparison of FP and (private NFP + public + FP) for all measures |
| Grabowski and Angelelli 2004w22 | Pain reported by residents; pressure ulcer prevalence; restraint use prevalence | Mixed results: favoured FP (P<0.05) for pain reported by residents and restraint use prevalence; favoured private NFP for pressure ulcer prevalence |
| Grabowski and Castle 2004w23 | Prevalence of feeding tube, pressure ulcers, restraint use, and urinary catheterisation | Mixed results: favoured private NFP (P<0.001) for consistently poor quality homes for each measure; favoured FP (P<0.001) for consistently good quality homes for each quality measure |
| Grabowski 2004w24 | Deficiencies in health/quality of care in OSCAR | Non-significantly favoured FP |
| Grabowski et al 2004w25 | Pressure ulcer prevalence | Favoured private NFP (P<0.05) |
| Konetzka et al 2004w26 | Deficiencies in OSCAR; staffing—nursing assistant hours/resident day, RN hours/resident day, RN + LPN hours/resident day | All significant (P<0.05) comparisons favoured private NFP |
| Konetzka et al 2004w27 | Transfer to hospital rate for patients with pneumonia | Favoured private NFP (P<0.01) |
| Lapane and Hughes 2004w28 | Depression treatment prevalence—assessed by antidepressant use and specifically by SSRI use | Non-significantly favoured FP for both measures |
| Lapane and Hughes 2004w29 | Depression treatment prevalence—assessed by antidepressant use and specifically by use of antidepressants other than tricyclic antidepressants | All significant (P<0.05) comparisons favoured private NFP: favoured private NFP (P<0.05) for antidepressant use, and non-significantly favoured private NFP for not using tricyclic antidepressants |
| Rantz et al 2004w30 | Performance on MDS quality indicators, confirmed by research nurses | Non-significantly favoured private NFP |
| Zhang and Grabowski 2004w31 | Prevalence of pressure ulcers, restraint use, and urinary catheterisation | All significant (P<0.05) comparisons favoured private NFP: favoured private NFP for all measures (P<0.001) except restraint use prevalence, which non-significantly favoured private NFP |
| Akinci and Krolikowski 2005w32 | Deficiencies in quality of care in Pennsylvania database; staffing—certified nursing assistant hours/day, LPN/LVN hours/day, RN hours/day, total staff hours/day | All significant (P<0.05) comparisons favoured private NFP: non-significantly favoured private NFP (P<0.05) for deficiencies, certified nursing assistant hours, and LPN/LVN hours; favoured private NFP for RN hours and total staff hours |
| Bardenheier et al 2005w33 | Vaccination for pneumococcus (% of homes) | Non-significantly favoured private NFP |
| Zinn et al 2005w34 | Prevalence of infection, pain, pressure ulcers, pressure ulcers adjusted for facility admission profile or loss of ADLs, and restraint use | Mixed results: favoured private NFP (P<0.1) for all measures except pain prevalence, which favoured FP (P<0.01) |
ADLs=activities of daily living; FP=for profit; LPN=licensed practical nurse; LVN=licensed vocational nurse; MDS=minimum data set survey; NFP=not for profit; OSCAR=Online Survey Certification and Reporting; RN=registered nurse; SSRI=selective serotonin reuptake inhibitor.
*Related to resident assessment, quality of nursing services, dietary services, physician services, rehabilitative services, dental services, pharmacy services, and infection control.
Table 5.
Characteristics of studies comparing for-profit and not-for-profit nursing home quality of care (public and private NFP homes)
| Study | Place; year; data source*; No of residents or nursing homes | Factors controlled or adjusted for | |
|---|---|---|---|
| Appropriate: age, severity of illness, severity of dementia, and payment status adjustments | Inappropriate: quality measures used in other studies; measures of intensity of care | ||
| Winn 1974w35 | Washington state; 1971; mailed questionnaire to administrators; 24 FP, 24 NFP | Unadjusted analysis | Unadjusted analysis |
| Riporttella-Muller and Slesinger 1982w36 | Wisconsin; July 1977-June 1978; Wisconsin Department of Health and Wisconsin Nursing Homes Ombudsman Program; 462 homes | Adjusted analysis but none of 4 selected appropriate factors included | None |
| Nyman 1984w37 | Wisconsin; 1978-9; 1979 Wisconsin Nursing Home Survey, Quality Assurance Project Pre-test, and Cost-Quality Study dataset; 88 cases of nursing home violations (No of nursing homes not indicated) | Payment source; severity of illness (need for intermediate, personal, or residential care by payment source) | None |
| Brunetti et al 1990w38 | North Carolina; 1987; surveys to nursing home administrators; 236 nursing homes (164 FP, 40 NFP) | Certification (Medicare only, Medicaid only, or Medicare and Medicaid) | None |
| Munroe 1990w39 | California; 3 December 1985 to 30 December 1986; Office of Statewide Health Planning and Development of California; 455 homes | Illness severity (ADLs/IADLs); payment status | Proportions of residents with catheters and decubiti; ratio of RN to LVN hours per resident day |
| Cherry 1991w40 | Missouri; 1984; Missouri State Board of Health; 134 homes | Payment status | RN, LPN, aide hours per resident |
| Kanda and Mezey 1991w41 | Pennsylvania; 1980, 1982, 1985, 1987; Long Term Care Facilities Survey conducted by State Health Data Center, Pennsylvania Department of Health; 407 homes for 1980, 395 for 1982, 395 for 1985, 461 for 1987 | Age of residents (in RN staffing comparison, when each year was analysed separately) | None |
| Cherry 1993w42 | Missouri; 1984; Missouri Division of Aging Routine Inspections and Missouri State Board of Health; 210 nursing homes | Adjusted analysis but none of 4 selected appropriate factors included | Nurse ratio |
| Zinn et al 1993w43 | Pennsylvania; 1987; MMACS, Pennsylvania Long Term Care Facility Questionnaire; 438 homes | Payment status | RNs per resident |
| Zinn 1993w44 | 46 continental US states; 1987; AHCA and MMACS; approximately 14 000 homes | % private pay; % confused; % Medicare; functional severity index | RN, LPN, aide staffing; rate of catheter use, restraint use, and tube feeding |
| Graber and Sloane 1995w45 | North Carolina; 1991; OSCAR, North Carolina Division of Medical Assistance, Office of State Health Planning; 195 homes | Illness severity (% intubated patients, facility disability level, % with incontinent residents) | RN ratio; LVN/nursing assistant ratio; % of residents on psychotropic drugs |
| Christensen and Beaver 1996w46 | Oregon; 1991-4; Oregon Board of Examiners of Nursing Home Administrators and State surveyors reports; 147 nursing homes (37 NFP or government and 110 FP) | Unadjusted analysis | Unadjusted analysis |
| Mukamel 1997w47 | New York (excluding New York City); 1986-90; New York State Department of Health; approximately 550 homes, 42.3% of residents in proprietary homes, 39.9% of residents in voluntary NFP homes, 17.8% in public homes | Unadjusted analysis | Unadjusted analysis |
| Anderson et al 1998w48 | Texas; 1990; Texas Medicare Nursing Facility Cost Reports and Client Assessment, Review, and Evaluation form; 494 nursing homes | % of private pay | RN, LPN, aide staffing |
| Bliesmer et al 1998w49 | Minnesota; 1988-91; Minnesota Department of Human Services Long-Term Care Division facility profiles and assessments of residents by RNs; 4103 residents in 1988, 4676 residents in 1989, and 4672 residents in 1990 | Age | Compliance with regulations |
| Castle and Fogel 1998w50 | United States; 1995; OSCAR, ARF; 15 074 homes | Illness severity (ADLs, incontinent bladder/bowel); payment status | Psychotropic drug use; staffing (high/medium/low RNs, LPNs, nursing assistants per resident) |
| Anderson and Lawhorne 1999w51 | Advance care directive prevalence; feeding tube prevalence; drug errors noted on survey; pressure ulcer prevalence; restraint use prevalence; staffing—direct care hours per resident per day; urinary catheterisation prevalence | All significant (P<0.05) comparisons favoured NFP: non-significantly favoured NFP for drug errors, non-significantly favoured FP for restraint use prevalence; favoured NFP for all other comparisons | None |
| Bravo et al 1999w52 | Eastern townships of Quebec (Canada); 1996; resident interviews; 301 residents from 88 nursing homes | Age; cognitive functioning (MMMS score); functional autonomy (SMAF score) | Staff to resident ratio |
| Castle 1999w53 | CA, CT, IA, MD, MA, OH OR, TN, TX, and VA: 1990 and 1993, Resident Assessment Instrument and OSCAR; 268 facilities (90% in each cohort FP) | Age; ADLs; severity of illness; severity of dementia; payment status | Staffing levels |
| Ballou 2000w54 | Wisconsin; 1987-95; Wisconsin Centre for Health Statistics and Wisconsin Bureau of Quality Assurance | Unadjusted analysis | Unadjusted analysis |
| Castle 2000w55 | United States; 1997; OSCAR; 17 024 homes | Dementia; ADLs | Staffing (RNs, LPNs, nurse aides, specialists per resident and nurse aide training); pressure ulcer incidence; urinary catheterisation; use of psychoactive drugs |
| Castle 2000w56 | United States; 1992 and 1997; OSCAR; 15 455 homes in 1992; 16 533 homes in 1997 | ADLs; payment status | Staffing (RNs, LPNs, nurse aides, rehabilitation assistants per 100 beds); antipsychotic drug use; residents with psychiatric problems |
| Castle 2001w57 | United States; 1999; OSCAR; 420 nursing facilities and OSCAR 1999 (~16 000 homes) | ADLs; dementia; payment status | Catheterisation; psychoactive drug use; physical restraint use; pressure ulcers; psychological disorders |
| Castle 2001w58 | United States; 1997; OSCAR; 16 871 homes | Age; ADLs | None |
| Castle 2001w59 | United States; 1992-7 and 1999; OSCAR (1992-7); 13 162 nursing homes | ADLs; private pay occupancy | Nurse staffing |
| Dubois et al 2001w60 | Eastern townships of Quebec (Canada); 1996; resident interviews; 88 nursing homes | Age | Staff to resident ratio; percentages of professionals among staff |
| Keith 2001w61 | A “Midwestern state”; 2 year period (year not specified); primary mail questionnaire and Area Agencies on Aging; questionnaire data from 633 volunteers and 1886 records | Adjusted analysis but none of 4 selected appropriate factors included | None |
| O’Neill et al 2001w62 | United States; 1999; OSCAR; 1098 homes | ADLs; dementia | Staffing (administration, medical director, RNs and LPNs, nurse aides per 10 residents) |
| Castle 2002w63 | United States; 1996-9; OSCAR; 14 042 homes | ADLs; payment status | Psychiatric problems |
| Lee et al 2002w64 | Taiwan; 1999; Quality Assessment Index; 28 homes (12 chain/FP, 12 independent/FP, and 4 NFP) | Adjusted analysis but none of 4 selected appropriate factors included | Ratio of nurses to average number of daily residents |
| Allen et al 2003w65 | Connecticut; 1998-2000; Connecticut Ombudsman Reporting System; 3443 complaints combined with related data from state’s 261 nursing homes | Medicaid percentage | Nurse/resident ratio |
| Allen et al 2003w66 | Connecticut; 1998-2000; Long-Term Care Ombudsman Program complaint data; 3360 complaints from 261 nursing homes | Medicaid occupancy | Staffing (full time employee ratio of RNs, LPNs, and certified nursing assistants to total number of beds/facility) |
| Anderson et al 2003w67 | Texas; date of survey administration not provided (secondary data from 1995); survey data from nursing home staff and 1995 Texas MDS; 164 nursing homes | Adjusted analysis but none of 4 selected appropriate factors included | None |
| Castle and Banaszak-Holl 2003w68 | United States; 1999; OSCAR; 15 834 homes | Dementia; severity of illness (ADLs) | None |
| Harrington and Swan 2003w69 | California; 1999; state cost reports; 1155 homes | Payment status | None |
| Weech-Maldonado et al 2003w70 | NY, KS, VT, ME, and SD; 1996; Health Care Financing Administration Investment Analyst Nursing Home Database (MDS+, OSCAR) | Adjusted analysis but none of 4 selected appropriate factors included | None |
| Baumgarten et al 2004w71 | Maryland; 1992-5; interviews with significant others or MDS+; 59 homes (1938 residents) | Unadjusted analysis | Unadjusted analysis |
| Lau et al 2004w72 | United States; 1996; MEPS NHC, 3372 residents | Age; Medicaid coverage; mental status; ADL limitations | RN to non-RN ratio; RN to resident ratio; influenza vaccination percentage |
| Castle and Engberg 2005w73 | MO, TX, CT, and NJ; 2003; primary data on staff turnover from mailed survey, OSCAR for remaining information; 526 homes | Illness severity (ADLs, incontinent bladder/bowel); dementia | Staffing (full time equivalent RNs, LPNs, nursing assistants/100 beds) |
| Chesteen et al 2005w74 | Utah; 1999; survey of certified nursing assistants, Utah Medicare/Medicaid certification program, and operational data reported to the state of Utah; 890 certified nursing assistants at 42 nursing homes | % Medicaid | None |
| Gruber-Baldini et al 2005w75 | 4 US states; year of data acquisition unclear; survey of resident care supervisors; 347 residents with dementia in 10 homes and 35 residential care/assisted living facilities | Cognitive status | % of supervisory staff trained; % of direct care providers trained |
| Intrator et al 2005w76 | United States (minus Alaska, District of Columbia, Hawaii, and Puerto Rico); 1993 to 2002; OSCAR and recent survey done by authors; 137 190 surveys from 17 635 distinct nursing facilities | Residents not paid for by Medicare or Medicaid (%), Medicare residents (%) | Total nurse hours per patient day>4.55 |
| McGregor et al 2005w77 | British Columbia; 2001; British Columbia Labour Relations Board; 167 homes | Severity of illness (levels of care) | None |
| Starkey et al 2005w78 | NY, ME, VT, and SD; 1996; MDS+, OSCAR; 1121 homes | Payment status | None |
| Stevenson 2005w79 | Massachusetts; 1998-2002; nursing home complaints received by Massachusetts DPH, OSCAR, and MDS QI; 539 nursing homes | ADLs | Survey deficiencies; staffing (nurse, aide); indwelling catheter; pressure sores |
| White 2005w80 | United States; 1997, 2001; OSCAR; ~10 000 homes in each year (unclear from article) | Payment status | None |
| Williams et al 2005w81 | 4 US states; year of data acquisition unclear; primary survey of resident care supervisors; 331 residents with dementia in 10 homes and 35 residential care/assisted living facilities | Cognitive status | Staffing |
| McGregor et al 2006w82 | British Columbia; 1 April-1 August 1999; British Columbia Linked Health Database; 43 065 residents | None for crude analysis | None for crude analysis |
ADLs=activities of daily living; DON=director of nursing; FP=for profit; IADLs=instrumental activities of daily living; LPN=licensed practical nurse; LVN= licensed vocational nurse; MMMS=modified mini-mental state examination; NFP=not for profit; RN=registered nurse; SMAF=functional autonomy measurement system.
*AHCA=American Health Care Association; ARF=Area Resource File; DPH=Department of Public Health; HCFA=Health Care Financing Administration; SAGE=Systematic Assessment of Geriatric Drug Use via Epidemiology; see table 3 for others.
Table 6.
Quality of care measures and outcomes of studies comparing for-profit and not-for-profit nursing homes (public and private NFP homes): favoured directions represent those with higher quality care
| Study | Quality measure | Outcome |
|---|---|---|
| Winn, 1974w35 | Staffing—No of equivalent hours per patient day (1 RN hour=1 h; other employees’ hours in proportion to 1 as their salary is to that of an RN), aide/orderly hours per patient day, LPN hours per patient day RN hours per patient day, total nursing care hours per patient day |
Non-significantly favoured NFP for all comparisons |
| Riporttella-Muller and Slesinger 1982w36 | Complaints to Wisconsin Nursing Homes Ombudsman Program; deficiencies in Wisconsin Office of Quality Compliance survey | All significant (P<0.05) comparisons favoured NFP: favoured NFP (P<0.001) for complaints; not significant (direction unclear) for deficiencies |
| Nyman 1984w37 | No of Medicaid violations weighted by severity in 1979, and composite variable for Wisconsin’s Quality Assurance Project; each quality measure examined with 2 models | All significant (P<0.05) comparisons favoured FP: for violations, one model significantly favoured FP (P<0.05) and the other non-significantly favoured FP; for the composite variable, non-significantly favoured FP and NFP in two different models |
| Brunetti et al 1990w38 | Cardiopulmonary resuscitation policy prevalence and quality of policy compared with 10 model criteria | Mixed results: presence of policy, non-significantly favoured FP; quality of policy, difference not noted |
| Munroe 1990w39 | Deficiencies at California state licensing “276 health deficiencies,” assessed for licensing (state) and certification (Medicare and Medicaid); staffing (turnover) | All significant (P<0.05) comparisons favoured NFP: non-significantly favoured FP for deficiencies; favoured NFP for staffing (P<0.001) |
| Cherry 1991w40 | Aggregate measure of staffing hours, pressure ulcer prevalence, urethral catheterisation, urinary tract infections/resident, and antibiotic use (poorly explained) | Non-significantly favoured NFP (only one aggregate outcome reported) |
| Kanda and Mezey 1991w41 | Staffing: RN staffing—No of full time RNs/100 beds, No of part time RNs/100 beds, total No of nursing staff/100 beds, proportion of part time and full time RNs to total nursing staff | All significant (P<0.05) comparisons favoured NFP: favoured NFP for part time RNs/100 beds (P<0.001), total nursing staff/100 beds (P<0.001); non-significantly favoured NFP for full time RNs/100 beds, proportion of full time and part time RNs to total nursing staff |
| Cherry 1993w42 | Poor nursing care (composed of four items) and non-compliance (defined as infraction in any of eight federally established categories of inspection) | All significant (P<0.05) comparisons favoured NFP; FP showed non-significantly more poor care and significantly greater non-compliance (P<0.01) |
| Zinn et al 1993w43 | Mortality—deaths per 100 residents; prevalence of pressure ulcers, restraint use, and urethral catheterisation | Non-significantly favoured NFP for all measures |
| Zinn 1993w44 | Staffing (RNs per resident, LPNs per resident, NAs per resident); catheter use rate; restraint use rate; tube fed rate; % not toileted | Mixed results: FP significantly associated with fewer RNs per resident, more LPNs per resident, higher catheter use rate, higher restraint use rate, and higher % not toileted; FP non-significantly associated with more NAs per resident and higher tube fed rate |
| Graber and Sloane 1995w45 | Restraint use prevalence at 1991 North Carolina Annual Survey | Non-significantly favoured NFP |
| Christensen and Beaver 1996w46 | Surveys of health and safety deficiencies and life safety code deficiencies | Significantly favoured NFP (P<0.005), meaning FP had more deficiencies |
| Mukamel 1997w47 | Deterioration of decubitus ulcers; physical restraint use prevalence; dehydration rates; deterioration in ADLs | Mixed results: FP associated with worse outcomes for deterioration in decubitus ulcers (P=0.004) and physical restraints (P=0.0001) and better outcomes for dehydration rates (P=0.0001); no significant difference for accident rates and No of deficiencies |
| Anderson et al 1998w48 | Average resident outcomes concerning verbal/physical aggression; other disruptive behaviour; geriatric-chair, wrist-mitten or vest-belt restraints; contracture; pressure ulcer; dehydration; urinary tract infection; fracture within preceding 3 months; and percentage improvements in resident outcomes between two time points | Not significant (direction not noted) |
| Bliesmer et al 1998w49 | Change in total dependence score (TDS) based on sum of eight ADLs: dressing, grooming, bathing, eating, bed mobility, transferring, walking and toileting | Mixed results: when deaths and discharges were excluded from the TDS scores, ownership status was no longer significant; FP status was significantly associated with higher chances of discharge in 2 of the 3 years examined (1990 (P<0.001) and 1991 (P<0.01)); NFP status was significantly associated with higher chances of death in 2 of the 3 years examined (1990 and 1991, P<0.001 for both) |
| Castle and Fogel 1998w50 | Restraint use prevalence | Significantly favoured FP (P<0.001) |
| Anderson and Lawhorne 1999w51 | Advance care directive prevalence; feeding tube prevalence; drug errors noted on survey; pressure ulcer prevalence; restraint use prevalence; staffing—direct care hours per resident per day; urinary catheterisation prevalence | All significant (P<0.05) comparisons favoured NFP: non-significantly favoured NFP for drug errors, non-significantly favoured FP for restraint use prevalence; favoured NFP for all other comparisons |
| Bravo et al 1999w52 | QUALCARE scale* | Not significant (direction not noted) |
| Castle 1999w53 | Psychoactive drug use prevalence | Non-significantly favoured FP |
| Ballou 2000w54 | Deficiencies (federal violations—definition unclear); staffing—RNs + LPNs per bed, total nursing staff per bed | Favoured NFP (unclear if significant) |
| Castle 2000w55 | Restraint use (changes with legislation) | Mixed results: favoured NFP (P<0.001) for not increasing restraint use with legislation; favoured FP (P<0.05) for decreasing restraint use with legislation |
| Castle 2000w56 | Restraint use citations | Favoured NFP (P<0.05) |
| Castle 2001w57 | Deficiencies in OSCAR† for 19 quality of care items; prevalence of pressure ulcers, psychoactive drug use, restraint use, and urethral catheterisation | All significant (P<0.05) comparisons favoured NFP; favoured NFP for deficiencies, psychoactive drug use, restraint use prevalence; not significant (direction unclear) for deficiencies, urethral catheterisation |
| Castle 2001w58 | Deficiency citations in OSCAR, subdivided into provision of appropriate services, training provisions and resident assessments | Favoured NFP (P<0.001) |
| Castle 2001w59 | Early adoptors of innovation (as measured through 13 special care units or subacute services) | Non-significantly favoured FP |
| Dubois et al 2001w60 | QUALCARE scale* | Not significant (direction not noted) |
| Keith 2001w61 | Ombudsman program complaints | Favoured NFP (P=0.001) |
| O’Neill et al 2001w62 | Deficiencies in OSCAR† (total deficiencies and severe deficiencies rated F and higher, where maximum No of deficiencies was 85 to reduce outlier effects); staffing—average total nursing hours per resident day | Favoured NFP (P<0.01) for all comparisons |
| Castle 2002w63 | Restraint use prevalence | Favoured FP: 1 citation (P<0.05); 2 consecutive yearly citations (P<0.01); 3 consecutive yearly citations (P<0.01) |
| Lee et al 2002w64 | QAI | Significantly favoured NFP for 3/5 categories and for total QAI score (P<0.05) |
| Allen et al 2003w65 | Ombudsman program complaints | Mixed results: non-significantly favoured NFP for both care complaints (P=0.79) and abuse complaints (P=0.20) |
| Allen et al 2003w66 | Ombudsman complaints | Significantly favoured NFP (P=0.021) |
| Anderson et al 2003w67 | Residents’ behaviour (verbal or physical aggressiveness or other disruptive behaviour); restraint use; complication of immobility; or sustaining a fracture in previous 3 months | Mixed results: non-significantly favoured FP for resident behaviours and higher restraint use; non-significantly favoured NFP for complications of immobility and fractures |
| Castle and Banaszak-Holl 2003w68 | Prevalence of pressure ulcers, psychoactive drug use, restraint use, and urinary catheterisation | Favoured NFP for each comparison (chains and non-chain owned nursing homes analysed separately) |
| Harrington and Swan 2003w69 | Staffing—total nurse and RN hours per resident day | Favoured NFP (P<0.01) |
| Weech-Maldonado et al 2003w70 | Outcome quality (cognitive decline, mood decline, pressure ulcer prevalence); process quality (restraint use prevalence, urinary catheterisation prevalence); staffing (ratio of RN hours to total nursing hours) | Mixed results: non-significantly favoured FP for outcome quality; nearly significantly favoured NFP (P<0.10) for process quality; non-significantly favoured NFP for staffing |
| Baumgarten et al 2004w71 | Pressure ulcer incidence | Favoured NFP |
| Lau et al 2004w72 | Inappropriate medical prescriptions by Beer’s criteria | Non-significantly favoured NFP |
| Castle and Engberg 2005w73 | Contracture prevalence; deficiencies in OSCAR†—focused on quality deficiencies (19/185 assessed); restraint use prevalence; pressure ulcer prevalence; psychoactive drug use prevalence (% of residents given anti-anxiety, sedative/hypnotic, and antipsychotic drugs); quality index—normalised measure of other indices (physical restraint prevalence, urethral catheterisation prevalence, contracture prevalence, pressure ulcer prevalence, psychoactive drugs use, and deficiency data) | All significant (P<0.05) comparisons favoured NFP: non-significantly favoured NFP for all comparisons except for restraint prevalence, which favoured NFP (P<0.01) |
| Chesteen et al 2005w74 | Health deficiency, severity of deficiency, and frequency of deficiency | Mixed results: non-significantly favoured NFP for all 3 measures, meaning that FP had worse deficiencies |
| Gruber-Baldini et al 2005w75 | Depression (of resident) prevalence, measured by modified Cornell scale for depression in dementia | Significantly favoured NFP (odds ratio 2.53 FP/NFP, 95% CI 1.29 to 4.98) |
| Intrator et al 2005w76 | Employment of nurse practitioners or physician assistants on staff | Non-significantly favoured FP |
| McGregor et al 2005w77 | Staffing: mean (dietary, housekeeping and laundry staff) hours per resident day; mean (RN, LPN and NA) hours per resident day; mean activity aide hours per resident day; mean dietary staff hours per resident day; mean housekeeping staff hours per resident day; mean laundry staff hours per resident day; mean LPN hours per resident day; mean NA hours per resident day; mean RN hours per resident day (each measure assessed in intermediate care, intermediate/extended care, and multilevel nursing home care settings) | All significant (P<0.05) comparisons favoured NFP—favoured NFP for all comparisons except: non-significantly favoured NFP for intermediate/extended care mean activity aide hours per resident day, multilevel care mean dietary staff hours per resident day, multilevel care mean laundry staff hours per resident day, intermediate care or intermediate/extended care mean LPN hours per resident day, mean NA hours per resident day in all three care settings, mean RN hours per resident day in intermediate and multilevel care settings; no direction to relation for multilevel care mean LPN hours per resident day; non-significantly favoured FP for mean laundry staff hours per resident day in all three care settings |
| Starkey et al 2005w78 | Cognitive decline between OSCAR assessments; mood decline between OSCAR assessments; prevalence of pressure ulcers, restraint use, and urinary catheterisation | All significant (P<0.05) comparisons favoured NFP: non-significantly favoured NFP for all measures except restraint use prevalence, which non-significantly favoured FP, and urethral catheterisation prevalence, which significantly favoured NFP |
| Stevenson 2005w79 | Ombudsman office complaints | Significantly favoured NFP (P<0.05) |
| White 2005w80 | Deficiencies in OSCAR†; pressure ulcer incidence (OSCAR); restraint use incidence (OSCAR)‡ | All significant (P<0.05) comparisons favoured NFP: significantly favoured NFP (P<0.05) in all measures except pressure ulcer incidence, which non-significantly favoured FP |
| Williams et al 2005w81 | Resident self reported pain using Philadelphia Geriatric Centre pain intensity scale | Significantly favoured NFP (odds ratio 2.99 FP/NFP, 95% CI 1.40 to 6.39) |
| McGregor et al 2006w82 | Hospital admission rate for anaemia, dehydration, falls, pneumonia, urinary tract infection, and pressure ulcers/gangrene; mortality | All significant (P<0.05) comparisons favoured NFP: favoured NFP for all measures except falls, urinary tract infection, and pressure ulcer admissions (non-significantly favoured NFP) and mortality (no direction) |
ADL=activities of daily living; FP=for-profit; NFP=not-for-profit; LPN=licensed practical nurse; OSCAR=Online Survey Certification and Reporting; QAI=quality assessment index (composite measure of staff presentation, operational efficiency, resident care quality, and institutional care plan); RN=registered nurse.
* 54 items grouped into 7 subscales: older person’s room, residence, physical care, medical maintenance, psychosocial care, human rights, and financial.
†OSCAR includes results of independent site surveys done every 9-15 months by auditors under contract from the Center for Medicare and Medicaid Services. The surveys detail compliance with each of 185 separate measures of quality that consider nursing home structure, processes, and outcomes. As a measure of quality, deficiency data has some limitations. Also, under-detection and under-reporting of deficiencies may occur. Deficiencies are categorised according to severity from A to L. F and higher denotes care that has potential to cause harm or immediate jeopardy to patients. In some years, deficiencies assessed varied from state to state.
‡Use of vests, belts, mittens, or wrist or ankle restraints. Chairs with locking trays (Geri-trays) are also included, whereas bed rails are not. Specifically, restraints imposed for discipline or convenience, and not needed to treat the resident’s medical symptoms were objectionable. Variable for restraint use is dichotomous—the home either did or did not receive this deficiency. Restraint use was verified by surveyors during the day for OSCAR.
We meta-analysed data for the four most commonly used quality measures. Table 7 presents a summary of the characteristics of studies meta-analysed, along with the results of sensitivity analyses to explain heterogeneity among studies in each meta-analysis. Two meta-analyses showed statistically significant results favouring higher quality care in not-for-profit nursing homes.
Table 7.
Results of testing of a priori hypotheses to explain heterogeneity
| Outcome | Summary study characteristics | Interaction P value | |||
|---|---|---|---|---|---|
| FP-NFP v FP-private NFP | Above median v below median appropriate adjustment score | Presence v absence of inappropriate adjustment, among studies with adjusted analysis | Data collection before or during 1987 v after 1987 | ||
| More extensively trained staff or more staff | 13 studies had poolable data, from 1971-2002; 3 removed for data overlap; 10 meta-analysed—4 collected data after 1987, 1 used primary data, 1 had data from Canada (remainder from United States) | 0.64 for FP-private NFP; ratio of effect sizes 1.09 (95% CI 1.07 to 1.12, P<0.001, I2=0%) | 0.15 | 0.99 | 0.66 |
| Lower pressure ulcer prevalence | 16 studies had poolable data, from 1987-2003; 5 removed for data overlap; 11 meta-analysed—2 used primary data, 1 had data from Canada (remainder from United States) | 0.76 for FP-private NFP comparison; ratio of effect sizes 0.89 (0.82 to 0.98, P=0.02, I2=39.3%) | 0.42 | 0.54 | All meta-analysed data collected after 1987 |
| Lower physical restraint prevalence | 13 studies had poolable data from 1987-2003; 5 removed for data overlap; 8 meta-analysed—1 used primary data | 0.84 for FP-private NFP comparison; ratio of effect sizes 0.94 (0.78 to 1.14, P=0.53, I2=84.9%) | 0.86 | 0.13 | All meta-analysed data collected after 1987 |
| Fewer deficiencies on government surveys | 13 studies had poolable data from 1976-2003; 6 removed for data overlap; 7 meta-analysed—2 collected data before 1987 | 0.56 for FP-private NFP comparison; ratio of effect sizes 0.92 (0.79 to 1.06, P=0.25, I2=63.1%) | 0.80 | 0.54 | 0.11; for data collected after 1987, pooled effect size 0.73 (95% CI 0.54 to 0.97, P=0.03, I2=67.9%) favouring NFP homes; for data collected before or during 1987, pooled effect size 1.09 (0.94 to 1.25, P=0.25, I2=0%) |
FP=for-profit; NFP=not-for-profit.
We found more or higher quality staffing in not-for-profit homes (ratio of effect 1.11, 95% confidence interval 1.07 to 1.14, P<0.001, I2=91.6%) (fig 2). We found a similar result favouring not-for-profit homes when assessing staffing hours alone, with a ratio of effect of 1.11 (1.08 to 1.14, P<0.001, I2=70.3%), an absolute hours increase of 0.42 (0.31 to 0.53) hours/resident/bed/day, and a relative hours increase of 11% (8% to 14%). When the only non-US study was excluded, we arrived at a similar ratio of effect for more or higher quality staffing in not-for-profit homes of 1.11 (1.07 to 1.15, P<0.001, I2=92.4%).
Fig 2 Ratio of effect sizes for staffing quality in for-profit (FP) and not-for-profit (NFP) nursing homes. Ratios listed represent effect size in NFP homes compared with that in FP homes. Ratio >1 indicates that NFP homes had more, or higher quality, staffing (that is, favours NFP)
We found a lower prevalence of pressure ulcers in not-for-profit homes (odds ratio 0.91, 95% confidence interval 0.83 to 0.98, P=0.02, I2=52.1%), with an absolute risk reduction of 0.59% (0.13% to 1.12%) and a relative risk reduction of 8.4% (1.9% to 16%) (fig 3). When the only non-US study was excluded, we arrived at a similar odds ratio favouring lower pressure ulcer prevalence in not-for-profit homes of 0.89 (0.82 to 0.97, P=0.007, I2=50.2%).
Fig 3 Odds ratios (OR) comparing pressure ulcer prevalence in for-profit (FP) and not-for-profit (NFP) nursing homes. OR <1 indicates lower risk of pressure ulcers in NFP facilities than in FP facilities, suggesting that NFP facilities deliver higher quality care
The remaining two meta-analyses showed non-statistically significant differences. We found less use of physical restraints in not-for-profit homes (odds ratio 0.93, 0.82 to 1.05, P=0.25, I2=74.6%) (fig 4) and fewer deficiencies in governmental regulatory assessments in not-for-profit homes (ratio of effect 0.90, 0.78 to 1.04, P=0.17, I2=59.8) (fig 5).
Fig 4 Odds ratios (OR) comparing physical restraint prevalence in for-profit (FP) and not-for-profit (NFP) nursing homes. OR <1 represents less physical restraint use in NFP facilities than FP facilities, suggesting that NFP facilities deliver higher quality care
Fig 5 Ratio of effect sizes for regulatory deficiencies in for-profit (FP) and not-for-profit (NFP) nursing homes. Ratios listed represent effect size in NFP facilities compared with that in FP facilities. Ratio <1 represents fewer deficiencies in NFP homes, suggesting that NFP homes deliver higher quality care
Funnel plots for the four meta-analyses did not suggest publication bias. A priori hypotheses did not explain the observed heterogeneity (table 7).
Discussion
Our systematic review identified 82 studies comparing quality of care in for-profit and not-for-profit nursing homes. More studies had all statistically significant analyses showing higher quality in not-for-profit nursing homes than in for-profit nursing homes. Many studies, however, showed no significant differences in quality by ownership, and a small number showed statistically significant differences in favour of for-profit homes. This pattern held true when we compared for-profit homes with both privately owned and publicly owned not-for-profit facilities. Pooled analyses of the four most commonly used quality measures showed statistically significant results favouring higher quality care in not-for-profit homes for staffing and prevalence of pressure ulcers and non-statistically significant differences favouring not-for-profit homes in physical restraint use and regulatory agency deficiencies. The large observed heterogeneity was not explained by our a priori hypotheses.
Previous systematic reviews
Two previous systematic reviews have compared quality of care in for-profit and not-for-profit nursing homes. In 1991 Davis and colleagues found that many studies showed that higher quality of care was provided in not-for-profit nursing homes; however, weaknesses in the methodological design of the included studies limited the conclusions that could be drawn.30 In 2002 Hillmer and colleagues did a systematic review comparing for-profit and not-for-profit facilities (including publicly owned facilities), focusing on studies in North America completed after the previous review.31 This study also concluded that not-for-profit facilities provided better quality care than for-profit facilities.
Strengths and weaknesses of this review
We did a comprehensive search, which identified 60 studies not included in previous reviews. We assessed studies spanning four decades and published in any language. We masked study results before determining eligibility and did duplicate citation screening, data abstraction, and quality assessment. We contacted authors for missing data and received responses from most of them. We compared quality of care in both for-profit versus not-for-profit nursing homes and for-profit versus privately owned not-for-profit nursing homes, did pooled analyses of quality of care measures, and found largely consistent results.
Our review has limitations resulting from the characteristics of the studies included. No randomised trials have compared quality of care across nursing home ownership, and no such trials are ever likely to be done. Furthermore, most studies are from the United States, which raises questions of generalisability to other jurisdictions.
Studies are also limited in that no standard definition of quality of care exists. The result is that studies used a very wide variety of alternative measures of quality. Even when the same measures were used, standardised approaches to the application of those measures were lacking. For example, meta-analysis for number and qualifications of staff fails to take into account staff turnover, the use of agency staff, and the professional mix of staff.25
Moreover, several eligible studies used administrative databases, which further limits the comprehensiveness and quality of the data. For example, the American Online Survey Certification and Reporting (OSCAR) database comprises self reported data from nursing home administrators; surveyors verify only a sample. Careful duplicate abstraction of data from patients’ charts with a priori definitions or, ideally, direct assessment of care provision would be preferable.
Our meta-analyses are limited in that many authors could not remove publicly owned facilities from their datasets for our for-profit versus privately owned not-for profit analysis. However, in our sensitivity analyses, results comparing for-profit and not-for-profit facilities were not significantly different from those in which we restricted the not-for-profit facilities to those for which we could confirm ownership.
Heterogeneity
On the one hand, one might see our results as compellingly favouring not-for-profit facilities. The gradient between studies in which all significant measures favoured not-for-profit (40 studies) and those in which all measures favoured for-profit (3) is large (table 1). All four meta-analyses favoured not-for-profit institutions, and two reached statistical significance.
On the other hand, 37 studies had mixed results (some measures favoured for-profit, some not-for-profit) and considerable heterogeneity was present in the results of the meta-analyses. This suggests that although the average effect is clear, that effect probably varies substantially across situations. The variability is probably explained, in part, by a variety of factors that vary within categories of for-profit and not-for-profit homes, including management styles, motivations, and organisational behaviour. For example, for-profit facilities owned and operated by investor owned corporations may have different motivations than facilities owned by small private businesses or single proprietors. Not-for-profit facilities run by charities might differ in structure and process from those run by municipalities; not-for-profit facilities that are managed by for-profit nursing home companies may function differently from those that are not.
We have partially mitigated this problem with our a priori hypotheses (extent of appropriate adjustments, year of data collection, geography and political environment, primary compared with secondary data collection, and, in particular, public versus private ownership of not-for-profit facilities). None of these variables, however, explained the substantial heterogeneity of our results. The studies failed to specify characteristics of individual nursing homes in sufficient detail to allow analyses exploring factors such as those listed above (ownership by corporation, small business, charitable organisation of municipality; management of not-for-profit homes by for-profit providers).
Significance of this study
Most of the studies in our systematic review showed lower quality of care in for-profit nursing homes than in not-for-profit nursing homes. However, a large proportion of studies showed no significant difference in quality of care by ownership. In the long term care market, in which funding is often provided by the government at fixed rates, both for-profit and not-for-profit facilities face an economic challenge that may affect staffing and other determinants of quality of care. In the for-profit context, however, shareholders expect 10-15% returns on their investments,32 taxes may account for 5-6% of expenses, and facilities tend to have higher executive salaries and bonuses, so for-profit facilities have a strong incentive to minimise expenditures.33 Minimising expenditures may lead to lower quality staffing and higher rates of adverse events (such as pressure ulcers), which may be reflected in citations for deficiency.
Proving causality by using observational studies is difficult. Furthermore, given their variability, the results do not imply a blanket judgment of all institutions. Some for-profit institutions may provide excellent quality care, whereas some not-for-profit institutions may provide inferior quality of care.
Our findings are, however, consistent with findings of higher risk adjusted death rates in for-profit hospitals and dialysis facilities as shown in previous reviews,18 19 as well as providing insight into average effects. Given the absolute risk reduction in pressure ulcers of 0.59%, we can estimate that pressure ulcers in 600 of 7000 residents with pressure ulcers in Canada and 7000 of 80 000 residents with pressure ulcers in the United States are attributable to for-profit ownership. Similarly, given an absolute increase in nursing hours of 0.42 hours per resident per bed per day, we can estimate that residents in Canada would receive roughly 42 000 more hours of nursing care a day and those in the United States would receive 500 000 more hours of nursing care a day if not-for-profit institutions provided all nursing home care. These estimates are based on the 2006 census from Canada showing that 100 740 of 252 561 nursing home residents resided in for-profit nursing homes and the 2000 census from the United States showing a total of 1 720 500 nursing home residents.34 35 These estimates assume that two thirds of US nursing home residents live in for-profit facilities.
Further research and conclusions
Although this review has fully assessed the data available comparing for-profit and not-for-profit nursing home care, additional work is needed to compare the costs between these types of facilities and to evaluate the consistency of these findings outside of the United States and Canada. Although we have extensively evaluated the literature comparing quality of care in for-profit, charitable organisation owned, and government owned nursing homes, the available studies did not allow comparison of the possible impact of factors such as subcategory of for-profit ownership (for example, chain v non-chain, investor v small business ownership, municipality v federal government ownership). Nursing home management companies further complicate the relation between ownership and quality of care. These are all important areas that warrant further research.
What is already known on this topic
The quality and appropriateness of care delivered in nursing homes is a major concern for the public, policy makers, and media
Controversy exists about whether for-profit compared with not-for-profit ownership affects quality of care
What this study adds
Most studies suggest a trend towards higher quality care in not-for-profit facilities than in for-profit homes, but a large proportion of studies show no significant trend
We acknowledge the outstanding work of Deborah Maddock, Denise Healey, Shelley Anderson, Michelle Murray, Monica Owen, and Laurel Grainger who coordinated this study. We thank our foreign article reviewers Janek Brozek, Matthias Briel, Toshi Furukawa, Marjuka Makela, Ben de Mol, Paola Muti, Patricia Smith, Kristian Thorlund, and David Wei. We appreciate the work of Dana Keilty, Navneet Binepal, Tony Soeyonggo, and Minji Kim, who blinded articles for us. We thank Christina Lacchetti, Michael Levy, and Rajesh Hiralal, who reviewed articles for us, and Diane Heels-Ansdell for her statistical help. We also thank the authors of included studies who did additional analyses for our systematic review: Chappin White, Robert Weech-Maldonado, and Ann L. Gruber-Baldini.
Contributors: VRC, PJD, GHG, and TS conceived and designed the study. OB, MB, NB, VRC, DJC, PJD, GHG, SB, JWB, KEB, TH, JNL, BP, NCR, HS, BS, SBS, QZ, and KW were involved in data acquisition. VRC, GHG, QZ, and PJD analysed and interpreted the data. VRC drafted the manuscript. GHG, PJD, and QZ critically revised the manuscript for important intellectual content. QZ and SDW provided statistical expertise. VRC, PJD, and GHG are the guarantors.
Funding: Atkinson Foundation Grant; the study sponsor did not contribute to the study design. JWB is funded by a Canadian Institutes of Health research fellowship award. DJC, MB, and JNL are supported, in part, by their respective Canada Research chairs. PJD is supported by a Canadian Institutes of Health Research new investigator award. HS is funded by a European Commission: The Human Factor, Mobility and Marie Curie Actions scientist reintegration grant (IGR 42192).
Competing interests: None declared.
Ethical approval: Not needed.
Cite this as: BMJ 2009;339:b2732
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