The preliminary set of theory and evidencebased core riskadjusters in the first phase of the project, where we focused on the original 11 HHQI outcomes, was drawn from a number of domains covered by the OASIS instrument. In selecting the core set as well as supplemental risk factors, special attention was paid to variables that are clinically relevant and suitable for inclusion in electronic health records. The preliminary set of core riskadjusters is listed in Table 2. The only riskadjusters that are not clinical or patient characteristics likely to be included in an electronic health record in this preliminary set are those under the Informal Support/Assistance and Living Situation subdomains.

Preliminary Analyses

Currently, different subsets of home care patients are assessed when determining an agencys performance on each OBQI quality indicator. The three utilization outcomes are computed for all episodes except those ending in death (i.e., approximately 98% of episodes are included). For all other outcomes, two additional criteria are used to determine whether or not a given episode will be included. First, the episode must end in discharge to the community (approximately 70% of episodes), because the endpoint measures used to calculate improvement or stabilization on the nonutilization outcomes are collected only on the more comprehensive assessment made for those patients discharged to the community. Second, the start of care (SOC) assessment item for the outcome must permit the patient to have the potential to have the outcome. OBQI health status improvement measures are binary indicators of whether the patients status at discharge is better than at baseline. Individuals who cannot improve because they do not have any deficit in the quality indicator at baseline are excluded from estimates of improvement. OBQI health status stabilization measures are binary indicators of whether the patients status at discharge is the same or better than at baseline. Individuals who cannot deteriorate because they are in the worst category of the quality indicator at baseline are excluded from stabilization estimates.
The initial developmental sample from which the University of Colorado identified individuals with the potential to have an outcome is 125,000 episodes. However, the developmental sample was supplemented by the University of Colorado for four of the 11 HHQI outcomes due to low numbers of episodes where patients had the potential to have the outcome. The developmental sample was 250,000 episodes for Improvement in Upper Body Dressing, Improvement in Transferring, and Improvement in Oral Medications, and approximately 350,000 episodes for Improvement in Confusion.
Respecification of Core RiskAdjustors
After replicating the riskadjustment models developed by the University of Colorado, alternative models were estimated using exactly the same coding of riskadjusters as in current models with two exceptions where theory or prior evidence suggested other codings were likely to be more meaningful. Instead of a continuous measure of the age of the home care patient, four categories were specified: <65; 65 to <75 (reference category); 75 to < 85; 85 or older. The other change was the creation of a single numeric scale from the individual OASIS ADL and IADL measures at baseline. Spector and Fleishman (1998) examined the psychometric properties of ADLs and IADLs and concluded that they represent a single construct. We approximated the scale developed by Spector and Fleishman by classifying persons as either independent or dependent on human help to complete each ADL and IADL. The scale is a simple count of the number of ADLs and IADLs that the patient needs human help to complete. It ranges from 0 to 14.
After initial models were estimated, we examined the direction and consistency of the effect of the core riskadjusters across the 11 HHQI quality indicator outcome models. A number of the original riskadjusters were integer scales that did not appear to be linearly related to the HHQI quality indicators and/or the effect on the outcome measures was the opposite of what would be expected.

Hearing impairment was dropped from the core set of measures because of inconsistent effects and limited conceptual importance.

Vision impairment was respecified into two dummy variables with a reference category of no impairment.

Speech impairment was grouped into four categories with no speech impairment as the reference category and a top category that combined levels 3, 4 and 5.

The original depression measure is a count of depressive symptoms, ranging from 0 to 5, which is highly skewed toward no symptoms; it was respecified as two dummy variables (i.e., 1 symptom only, 2 or more symptoms) with a reference category of no symptoms.

A set of mutually exclusive indicators was created to measure frequency of urinary incontinence (during the night, during the day, night and day, and urinary catheter present) with a reference category of no incontinence.

A set of mutually exclusive categorical variables was created for bowel incontinence similar to those created for urinary incontinence.

A set of mutually exclusive categorical variables was created to indicate the type of help provided by the primary caregiver (i.e., the primary caregiver provides help with ADLs (with or without providing help with IADLs), help with IADLs only, or some other type of help) with a reference category of no primary caregiver.
We also categorized dyspnea which was included in the riskadjustment models of the ADL outcomes. The original integer scale was not linearly related to these outcomes. In some models of ADL outcomes, the direction of the effect of dyspnea was positive, suggesting improvement in ADL outcomes as the level of impairment increased (although generally decreasing in magnitude as impairment level increased). In other models the effect of higher levels of impairment on ADL outcomes was negative although never statistically significant. Despite its unexpected and inconsistent effects, we left dyspnea in the preliminary alternative riskadjustment models for ADLs because of its conceptual importance. Dyspnea did have the expected effect on the utilization outcomes, with the probability of Emergent Care and Acute Care Hospitalization rising as the severity of dyspnea increased.
Respecification of Baseline and Prior Values of Outcome Indicators
The baseline and prior values of the outcome indicators were treated as continuous variables, following the approach of the University of Colorado, in our initial analyses. Higher values always represent a sicker state. Subsequently, these indicators were respecified as categorical variables to test the assumption that baseline and prior variables are linearly related to the outcome indicators. The respecification improved the explanatory power of the riskadjustment modelsin a few cases, substantially.
Summary of Preliminary Modeling Results
Six models were estimated for each outcome. We began with a model limited to the core set of clinical, demographic and payment riskadjusters, including the baseline value of the outcome measure if it was not already among the core variables. Outcomespecific riskadjusters were added at subsequent steps: Model 2 included other clinical characteristics at baseline that might plausibly affect the outcome, and Model 3 included measures of clinical status prior to home health admission. Four clinical therapies at baseline (i.e., oxygen therapy, IV/infusion therapy, enteral/parenteral nutrition, and ventilator) then were added to the riskadjustment models for all 11 outcomes (Model 4). The living arrangements and social support indicators subsequently were added to all models (Model 5). Finally, LOS was added solely to allow comparison of current and alternative model statistics and parameter estimates.
By Model 3 (i.e., after the addition of the prior health status measures) the riskadjustment models developed in the preliminary analyses generally approached but did not exceed the explanatory power of the HHQI riskadjustment models developed by the University of Colorado. The effect of the measures of health status prior to admission on the explanatory power of the riskadjustment models varied depending on the outcome indicator. They had a modest effect in the improvement in ADL models as well as the one improvement in an IADL model (i.e., Improvement in Management of Oral Medication). Prior health status riskadjusters had virtually no effect in the remaining models of health status outcomes and were not included in the riskadjustment models of the two utilization outcomes.
The social support indicators, while conceptually important, added almost nothing to the explanatory power of riskadjustment models that already included clinically relevant variables. The one exception was the Improvement in Oral Medication riskadjustment model where there was a one percentage point increase in the Rsquared statistic after the addition of the core social support measures and a statistically significantly improvement in the fit of the model (p < 0.001).
The generally lower explanatory power of the preliminary alternative models is not surprising since the stepwise logistic regression technique used to develop the current models is likely to result in models with close to the best explanatory power possible for the data set analyzed. In addition, the exclusion of LOS from the alternative models, because it can be affected by the quality of care provided and therefore is not an appropriate riskadjuster, results in a reduced Rsquared value for the alternative utilization outcome models relative to the current models.
Whether the alternative models are more parsimonious than the University of Colorado models depends on whether the models are considered individually or all 11 are considered together. Only two of the preliminary riskadjustment models were more parsimonious than the corresponding models developed by the University of Colorado to riskadjust the 11 initial HHQI outcome indicators. The total number of OASIS items and elements used to riskadjust all 11 HHQI outcome indicators, however, was smaller.



Technical Advisory Group (TAG) Meeting

A TAG meeting was conducted in Washington, DC, on August 20, 2004. Members of the TAG, which included industry representatives, were experts in home health care quality, riskadjustment, and home health policy. The TAG made a number of comments and recommendations based on a review of preliminary analysis results and other background documents.
Strong support was expressed for identifying a core set of riskadjusters (for statistical reasons as well as for face validity and interpretation of riskadjustment models). TAG members agreed that the original file of riskadjusters obtained from the University of Colorado had some limitations and that additional OASIS data should be requested to allow further development of three types of riskadjusters: diagnoses, social support, and payer. Diagnoses were aggregated into broad body system categories on the original file. With the specific diagnosis information collected on OASIS, it will be possible to specify diagnoses that occur frequently in the home care population (e.g., diabetes) as well as conceptually important medical conditions. It was pointed out that some important diagnoses typically are recorded as secondary, not primary, diagnoses (e.g., multiple sclerosis) and that diagnosis riskadjusters should take OASIS secondary diagnoses into account.
The TAG also recommended further examination of living arrangement and social support riskadjusters after the original OASIS variables are obtained because of their high face validity for clinicians. There was a discussion about more detailed living arrangement data and whether knowing that the patient lives with his or her spouse, as opposed to other family members, is likely to perform better as a riskadjuster. TAG members pointed out that it is possible that too much assistance could delay improvement in some activities. Also, it was suggested that the project team think about whether it is possible to identify spouses who can help with care versus those who cannot or who may require their own care.
There was a discussion of the original payer data (M0150) as well. Medicaid as a payer is to some extent an indicator of economic status. It also is likely to be an indicator of more permanent disability and/or chronic disease. One of the industry experts also suggested that agency staff completing OASIS assessments tend to check Medicare as a payer if there is any chance that the episode might be billed to Medicare. A very large share of episodes (greater than 94%) on the file obtained from the University of Colorado report Medicare as a payer. In addition to home health agency coding practices, this is partly due to the way episodes of home health care are selected for OBQI outcome analysis. All episodes must start and finish in the calendar year. This eliminates many long episodes that are more likely to have Medicaid as the payer including episodes where home health was provided the entire year but admission and discharge are outside the calendar year.
The rationale for examining the baseline therapy measures (i.e., oxygen therapy, IV/infusion therapy, enteral/parenteral nutrition, and ventilator) separately from other clinically relevant riskadjusters was discussed by the TAG. The riskadjustment experts agreed that it generally is a bad idea to include actual services in payment or outcome riskadjustment models since it may encourage inappropriate use of the services. The clinical and industry experts, however, pointed out that these services were invasive and would not be initiated without very clear clinical indications and medical orders. These measures generally had little impact on the explanatory power of the 11 HHQI riskadjustment models but may be appropriate as outcomespecific riskadjusters in some cases.
One TAG member indicated that sensory measures (e.g., vision, speech) tend to vary in their relationship with outcomes and that the project team may want to consider dropping them from the core set of riskadjusters and including them as outcomespecific riskadjusters when appropriate. It also was suggested that Life Expectancy be dropped from consideration since agencies questioned its reliability and it is unclear whether it will be included in future versions of OASIS.
Overall, there was agreement that the sequential model building approach used by the project team was logical. There also was agreement that LOS should not be included as a riskadjuster. Members of the TAG also agreed that agencylevel analyses are an important part of the assessment of differences between current and alternative riskadjustment models.


Final Data Analyses: RiskAdjustment Models

Development of Final Set of Core and Supplemental RiskAdjusters
The selection of the final set of core riskadjusters was based on findings from the preliminary analyses, comments of TAG members, and examination of a small number of additional OASIS items provided by the University of Colorado following the TAG meeting. The analyses conducted after receipt of additional OASIS data included respecification of the Living Situation and Informal Support/Assistance riskadjusters. Specifically, alternative specifications were explored utilizing the more detailed data on living arrangements (with the lives with spouse/family category in initial models separated into two categories) and the person providing assistance.
The additional data and respecification, however, did not substantially affect the contribution of the living situation and informal support/assistance measures to the explanatory power of the HHQI riskadjustment models that already included demographic, payer and clinical measures. The one exception is the riskadjustment model for Improvement in Medication Management. When the living arrangement and social support measures were added to a model with demographic, payer and clinical measures (i.e., added to Model 3), the Rsquared statistic increased from 15.7% to 16.7%. These conceptually important measures were excluded from the alternative models because of the limited contribution to the explanatory power of the riskadjustment models.
Table 3 lists the final set of core riskadjusters in the alternative models along with their specification. A total of 43 OASIS items were used to construct the core riskadjusters. The demographic and insurance measures clearly are likely to be included in electronic health records and the remaining items are all clinically relevant. The one core riskadjuster that varies from model to model is the baseline value of the outcome indicator. The baseline value, specified as a categorical variable, tends to make a relatively large contribution to the explanatory power of riskadjustment models. It appears to be adjusting for differences in the probability of improving (or stabilizing) related to the number of levels of the OASIS item.
Riskadjusters specific to each outcome, other than measures of health status prior to admission, are listed in Tables 4a4d. They are reported by domain of the outcome indicator (e.g., Table 4a lists the riskadjusters specific to ADL outcome models). Some items are common to all riskadjustment models within a domain. For example, obesity is included in the riskadjustment models of all ADL outcomes. Other items are specific to a single outcome. For example, whether a patient smokes is specific to the Improvement in Dyspnea riskadjustment model. Generally, 23 outcomespecific items were added to each riskadjustment model. All of these items are clinical factors.
Tables 5a5d list the measures of clinical status prior to home health admission that were added to the riskadjustment models of selected OBQI outcomes. As noted above, these OASIS items were examined separately from other outcomespecific riskadjusters because of questions about their reliability and possible elimination from the OASIS instrument. There were no directly related, conceptually important prior health status riskadjusters used for four OBQI outcomes (i.e., Improvement in Dyspnea and the three utilization outcomes).
Comparison of Current and Alternative Models
The OBQI quality indicators are grouped into six broad domains by the University of Colorado: (1) ADLs, (2) IADLs, (3) Physiologic indicators, (4) Emotional/Behavioral measures, (5) Cognitive measures, and (6) Utilization Outcomes (see Table 1). We first present results from all models and then by domain. The models developed by the University of Colorado are referred to as the current models; the two final alternative models are referred to as the core alternative model (which includes only core riskadjusters) and the full alternative model (i.e., Model 3 which includes outcome specific and prior OASIS items, or Model 2 where there are no relevant prior items).
The full alternative models typically have slightly lower explanatory power than the current riskadjustment models. Specifically, the Rsquared statistic for the full model tends to be within 12 percentage points of the Rsquared statistic for the model developed by the University of Colorado. There is a similar pattern for the c statistic. While the number of OASIS items and elements is sometimes larger and sometimes smaller for the alternative models compared with current models, the overall number of OASIS items and elements employed when riskadjusting all 31 OBQI outcome indicators is considerably smaller for the full alternative models (64 versus 88 OASIS items, and 93 versus 135 OASIS elements).
ADL and IADL Outcomes. The ADL and IADL outcomes represent 23 of the 41 OBQI quality indicators and over twothirds of the 31 outcome indicators currently riskadjusted by the University of Colorado. The performance (i.e., explanatory power as measured by the Rsquared statistic) of the alternative and current riskadjustment models for ADL and IADL outcomes is presented graphically in Figure 1 and Figure 2. Table 6a and Table 7a summarize the model statistics for all ADL and IADL outcome models, respectively, and Table 6b and Table 7b present the detailed regression results for the full alternative models estimated for the 23 ADL and IADL outcomes.^{4}
As previously discussed, most of the full alternative ADL and IADL models have slightly lower explanatory power than the current models. This is not surprising since a stepwise approach was used to develop the current models. An exception is the alternative riskadjustment model for the Improvement in Ambulation outcome where the Rsquared statistic is more than six percentage points greater than the Rsquared statistic for the current model. The ADL and IADL stabilization outcomes, it should be noted, are highly skewed (i.e., a very high proportion of those potentially able to stabilize do stabilize). This may explain the relatively low Rsquared and relatively high c statistics for both current and alternative models.
The outcomespecific riskadjusters generally contribute very little to the explanatory power of the ADL and IADL riskadjustment models that already include the core riskadjusters. In contrast, the prior OASIS items contribute substantially to the explanatory power (roughly two percentage points to the Rsquared statistic) of almost all of the riskadjustment models of improvement in ADLs and IADLs, but not stabilization in ADLs and IADLs. There is a similar pattern for c statistics.
Physiologic Outcomes. Figure 3 graphically presents the performance of the alternative and current riskadjustment models for the five physiologic outcomes currently riskadjusted in OBQI. Table 8a summarizes the model statistics for all physiologic outcome models and Table 8b presents the detailed regression results for the full alternative models estimated for the five physiologic outcomes that are currently riskadjusted, and the alternative models with only core riskadjusters for the four that are not currently riskadjusted in OBQI.
The outcomespecific riskadjusters tend to make a slightly greater contribution to the explanatory power of the physiologic outcome models compared to ADL and IADL outcome models. The effect of the prior OASIS items, on the other hand, is modest. Among the physiologic outcomes, the full alternative riskadjustment model for Improvement in UTI performs considerably worse than the current UTI riskadjustment model. The Rsquared statistic for Model 3 is 5.9% compared to 12.1% for the current model, and corresponding c statistics are 0.665 and 0.740 (see Table 8a). The main reason for this difference is the exclusion of home health episode LOS from the alternative model.
Emotional/Behavioral Outcomes. None of the emotional/behavioral outcomes currently is riskadjusted in OBQI. Only Model 1 (i.e., the model including only the core riskadjusters) was estimated for outcomes that are not currently riskadjusted. The model statistics for the alternative models for the three emotional/behavioral outcomes are reported in Table 9a. The detailed regression results for the final alternative models estimated for the emotional/behavioral outcomes are presented in Table 9b. The Rsquared and c statistics for all three models are low.
Cognitive Outcomes. There are three cognitive outcomes in OBQI but currently only Improvement in Confusion Frequency is riskadjusted. The rightmost bar in Figure 3 graphically presents the performance of the alternative and current riskadjustment models for Improvement in Confusion Frequency. Neither the outcomespecific nor the prior OASIS items contribute substantially to the explanatory power of the Improvement in Confusion Frequency model that already includes the core riskadjusters. Table 10a summarizes the model statistics for all cognitive outcome models. Table 10b presents the detailed regression results for the full alternative model estimated for Improvement in Confusion Frequency as well as the alternative models with only core riskadjusters for the two cognitive outcomes that are not currently riskadjusted in OBQI. The Rsquared and c statistics for all models are relatively low although the c statistic for the Stabilization in Cognitive Functioning riskadjustment model that includes only the core riskadjusters is 0.738 indicating adequate ability to predict what is a highly skewed outcome (i.e., over 90% of individuals who could stabilize did stabilize).
Utilization Outcomes. Figure 4 graphically presents the performance of the alternative and current riskadjustment models for the three utilization outcomes (all three are riskadjusted in OBQI). Table 11a summarizes the model statistics for all current and alternative utilization outcome models and Table 11b presents the detailed regression results for the full alternative models estimated for the utilization outcomes.
Two of the three outcomespecific variables at baseline (Dyspnea and IV/Infusion therapy) are highly statistically significant in the final, full riskadjustment models for all three utilization outcomes (p < 0.001). Nevertheless, the outcomespecific variables as a group have only a very small effect on the explanatory power of the riskadjustment models for the utilization outcomes. When added to models already including the core riskadjusters, the Rsquared and c statistics increase by at most roughly half a percentage point or 0.005, respectively. No prior OASIS items were included in the alternative models for these outcomes. As noted previously, the exclusion of LOS reduces the explanatory power of the alternative models for the utilization outcomes.
Comparison of Overall Number of OASIS Items and Elements Used in RiskAdjustment
The overall number of OASIS items used in current and alternative riskadjustment models (out of a total of 95 M0 items) is graphically presented in Figure 5. The core OASIS items in the alternative models are in the lower lefthand corner shaded in the darkest color. On the diagonal (in the next darkest shade) are the OASIS outcome specific and prior items included in the full alternative models (i.e., Model 3 for the outcomes with prior OASIS items and Model 2 where there are no relevant prior items). The OASIS items for the additional variables used in one or more of the current riskadjustment models but not in the alternative models are in the next darkest shade. Sixtyfour OASIS items were used to construct the riskadjusters included in one or more of the full alternative models, compared to 88 for the current models developed by the University of Colorado. There are seven OASIS items that are not used in either the current or alternative models (unshaded in the upperrighthand corner of Figure 5). The M0 items used for casemix classification in the Medicare prospective payment system are in bold with an asterisk.
Some OASIS items include multiple elements with each element separately assessed and marked (i.e., the OASIS items with instructions to mark all categories that apply). The OASIS elements used in current and alternative riskadjustment models are graphically presented in Figure 6 in the same manner as the OASIS items in Figure 5. There are a total of 180 OASIS elements with 93 used to construct the riskadjusters in the full alternative models compared to 135 in the models developed by the University of Colorado. All OASIS elements in the alternative riskadjustment models also are used in current models with two exceptions: the Current Payer elements Medicaid traditional feeforservice (M0150_3) and Medicaid HMO/managed care (M0150_4), both of which are highlighted on the left side of Figure 6. The M0 elements used for casemix classification in the Medicare prospective payment system are in bold with an asterisk.


Final Data Analyses: Agency Impacts

The results of the agency analyses are reported by outcome domain in Tables 1216. Overall, the results suggest that the quality ratings for most agencies and most outcomes are similar regardless of whether the current or alternative full model is used to riskadjust outcomes. The difference tends to be minimal (no more than one to two percentage points) between the current and alternative riskadjusted percent of an agencys patients with each outcome (see Figure 7). For a small share of agencies (i.e., those below the 5th or above the 95th percentile of the distribution), however, differences exceed four percentage points for Improvement in Ambulation, Improvement in Light Meal Preparation, Improvement in UTI, Acute Care Hospitalization, and Discharge to the Community (see columns 3 and 4 of Tables 1216).
The average of the differences at each agency is greatest for Discharge to the Community (0.374 percentage points) followed by Improvement in UTI (0.287 percentage points). In the case of the UTI outcome, the average percent of patients improving at each agency was 83.7% when estimated using the current riskadjustment model and 83.9% when estimated using the alternative full model. Despite the very small size of average differences, they often are statistically significant because sample sizes tend to be large, ranging from a low of 771 agencies when comparing the riskadjusted Improvement in UTI outcomes, to 4,798 agencies in analyses of the percent of patients with an Acute Care Hospitalization.
While the magnitude of the difference between outcome estimates using the two riskadjustment approaches is important, it is the ranking of each agency relative to others that is likely to be of most concern to providers. The nexttothelast column in Tables 1216 reports estimates of Spearmans rank correlation coefficient. These correlation coefficients are presented graphically in Figure 8. A value of one would indicate that rankings are exactly the same. For most outcomes, in fact, the correlation coefficient is close to one (i.e., it is above 0.950). The two lowest correlation coefficients are 0.912 for Improvement in UTI and 0.925 for Improvement in Ambulation.
The final column of each of the agencylevel analysis tables reports the number and percent of agencies that change two or more deciles in rank when the riskadjustment method is changed. (An agency, for example, would have to decline from the top decileor top 10% in rankingto the third decile or lower to be identified as changing two or more deciles.) The outcomes with the greatest number of agencies shifting at least two deciles in rank, not surprisingly, are those with the lowest Spearmans rank correlation coefficient. Among the agencies analyzed, 20.1% shifted two or more deciles in their Improvement in UTI ranking while 17.3% changed two or more deciles in their Improvement in Ambulation ranking.
Agency quality rankings differ the most where the difference in the explanatory power of the current and alternative riskadjustment models is substantial. In the case of Improvement in Ambulation, the alternative riskadjustment model explains considerably more of the variation in the outcome than the current model. It is the reverse for the Improvement in UTI outcome where the current model includes LOS among the riskadjusters. Agency quality rankings for the utilization outcomes do not differ as much as might be expected given the exclusion of LOS from the alternative models and, as a result, the lower explanatory power of alternative versus current riskadjustment models.
A sensitivity analysis then was conducted to better understand the impact on agency quality ratings of the inclusion of outcomespecific and OASIS prior items in the alternative riskadjustment models of the OBQI quality indicators. Specifically, agencylevel analyses were repeated with only the core riskadjusters included in the alternative riskadjustment models (i.e., the final version of Model 1 for each of the 31 currently riskadjusted OBQI outcomes). The results of the sensitivity analysis are presented graphically in Figure 9 and Figure 10. The basic pattern of impacts is the same but, as expected, the difference in riskadjusted outcomes using the current and alternative approaches increases (to between one and three percentage points for most agencies on almost all outcomes). For almost a third of the outcomes the Spearman rank correlation coefficient now is in the 0.9000.950 range with the correlation coefficient for Improvement in Ambulation falling slightly below 0.900.
Finally, it is important to note that for many OBQI outcomes a relatively large number of agencies had fewer than 20 patients in the analytic sample with the potential to have the outcome. These agencies, therefore, were excluded when examining the impact of the alternative approaches to riskadjustment on the percent of patients with the outcome. The number of agencies excluded is particularly large for two outcomes. All but 14.7% of agencies were excluded when examining the impact of alternative riskadjustment approaches on estimates of Improvement in UTI and all but 19.5% were excluded when examining the impact on estimates of Improvement in Bowel Incontinence.

View full report
"qualHH.pdf" (pdf, 3.72Mb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®