Associations Between Client Characteristics,Service Utilization and Health-Related Quality of Life of Older Adults Accepting Home Care

Abstract

Introduction:

Currently, little is known about interactions between older home care (HC) clients health-related quality of life (HRQoL) and home care (HC) services accessed.

Objective:

This study aims to address the knowledge gap by evaluating changes in the HRQoL of older adult HC clients across individual characteristics and HC services accessed over approximately 1 year.

Methods:

This study conducted a retrospective, exploratory observational evaluation of changes in the HRQoL of older adult home care (HC) clients. A validated mapping process was used to estimate Health Utility Index 3 (HUI3) scores using Resident Assessment Instrument – Home Care (RAI-HC) assessments.

Results:

A total of 8743 clients with 17,486 observations were included in the study. The average baseline age was 83.1 (standard deviation (SD) 8.3) and 5847 clients (66.9%) were female. The baseline HUI3 score was 0.382 (SD 0.296); which decreased over the period of approximately 1 year to 0.298 (SD 0.315). Being older (75 years and older vs 65-74 years), having an increase in comorbidity index scores, clinical specialties supports, and non-regulated supports during the study period were found to be associated with decreased HRQoL.

Conclusion:

Healthcare service providers interested in mitigating the decline of HRQoL in older adult HC clients should focus interventions on individuals who are in older age groups, with poorer and declining health status, and have had a recent increase in HC service use.

Keywords

older adults health-related quality of life HRQoL service levels

Introduction

Home care (HC) services are critical to maintaining the health of community-residing older adults and their quality of life.^1,2 In Canada, there are no standardized national requirements for HC services, leading to variation across provinces and territories. As such, HC services including Clinical Specialties Support care (i.e., registered nurses) and Non-regulated Support care (i.e., home health aides) vary depending on the region.^1,3 As HC programs are expected to continue to grow,⁴ information on how services impact client outcomes can support planning, monitoring, and evaluation. Research is just beginning to investigate the relationship between HC client characteristics, service use, and quality of life.⁵

Little is known about the health-related quality of life of clients accessing long-term HC services. Quality of life (QOL) is a multidimensional concept defined by the World Health Organization (WHO) as an “individual’s perception of their position in life and the context of the culture and value systems in which they live and in relation to their goals, expectations, standards, and concerns.”⁶ In contrast, the Health-Related Quality of Life (HRQoL) is a narrower construct that focuses assessment on physical, psychological, and social aspects of well-being that directly or indirectly relate to health.⁷ Incorporating HRQoL measures can support the monitoring of both client health and quality of care, by assessing patient outcomes^8-10 relevant to healthcare delivery. While QOL encompasses broader life domains such as environment, financial security, and social relationships, HRQoL is more appropriate for this study because it is directly responsive to changes in health.

Challenges in measuring HRQoL in HC may help explain the existing gap in knowledge. Not all subgroups of individuals may be represented equally, as results are biased toward the healthy and cognitively intact clients who can respond to surveys.¹¹ Earlier research notes that the oldest adults may refuse or be unable to participate due to various health concerns, including impaired cognition.^12,13 One solution is proxy measurement for older adults who cannot respond, although this method is time-consuming and costly, particularly at scale. Recent work created a mapping system implementing data from the commonly used clinical tool, the Resident Assessment Instrument – Home Care (RAI-HC), to estimate outcomes for the HUI3.¹¹ This mapping process offers a practical method to better understand HRQoL among a more diverse group of older adult HC clients¹¹ who are routinely assessed using the RAI-HC annually.

Several factors are known to lower HRQoL for older adults, including older age, living alone, with a lower income, poor physical, and oral health¹⁴ and having several chronic conditions.¹⁵ Preventative home visits provided by a nurse demonstrated positive effects on older adults HRQoL.¹⁶ Beyond intervention-based changes in HRQoL within HC, little is known about interactions between clients’ characteristics, service use, and HRQoL. This study evaluated changes in HRQoL among older adult HC clients by individual characteristics and HC services accessed over approximately 1 year.

Materials and Methods

Design, Sample, and Setting

This observational retrospective exploratory study included a within-subject evaluation of HRQoL in older adults accessing long-term HC over about 1 year. Data from Alberta Health Services (AHS), a provincial healthcare authority, were previously collected for HC service provision in Edmonton Zone.

Included clients accessed long-term HC service (over 3 months) and completed a RAI-HC between March 1, 2018, and February 28, 2019 (baseline), and a second assessment between March 1, 2019, and February 29, 2020 (follow-up). To control for differences in time elapsed between assessments, a selection process maximized time between sequential assessment periods. Specifically, the first assessment from the baseline period was kept and any subsequent measures from this period were dropped. During follow-up the final RAI-HC assessment was kept, and any earlier measurements were dropped. Clients may have received HC in any setting except a Long-Term Care Facility (LTCF), designated supportive living, or hospice. Included clients were aged 65 years or older as of March 1, 2018, and scheduled to receive at least 1 weekly service visit. An AHS data analyst provided data for all eligible clients. Clients were excluded if they did not have 2 RAI-HC assessments completed from the 2 points in time identified for data collection, were under 65 years of age as of March 1, 2018, or were not scheduled for at least 1 weekly visit from HC.

Data Measures Provided by AHS

Resident Assessment Instrument–Home Care (RAI-HC)

The RAI-HC is a comprehensive standardized assessment tool used internationally to evaluate care needs.^11,17,18 This assessment is used to develop a care plan for scheduled care provided by HC staff who are generally not available 24 hours a day. Adults accessing government-funded long-stay HC services in Alberta are required to have a RAI-HC completed at intake, annually from the initial assessment date and additionally if there is a significant change in health condition.¹⁹ To promote data robustness, the RAI-HC collects data by integrating information from discussions with clients, care providers, clinical evaluations, and other relevant health record information.¹⁹ The RAI-HC psychometric properties have been reported in various publications and have been found to be reliable and valid.^20-22

Pampalon Deprivation Index (PDI)

The PDI reports client demographics using a socioeconomic deprivation scale to stratify the population and identify potential impact. This index was developed through a “social component” and a “material component” stratifying the population into 5 quintiles according to a level of deprivation and can be used as a proxy measure for socioeconomic status.²³ If the number of clients in an area was too small (less than 5 to prevent possible identification), or required information was unavailable, PDI values were coded as zero.

Comorbidity Index (CI)

The CI is a demographic measure used to classify individuals with various health conditions identified as impactful on mortality in longitudinal studies.²⁴ The updated version of the Charlson Comorbidity Index presented by Quan et al.²⁵ was selected and completed with available data from the RAI-HC. Based on the RAI-HC, 6 diagnoses were compressed or excluded, and 9 impactful diagnoses were added to the CI summary measure (S1 Table). A higher CI score indicates a lower health-level.

Service Utilization

The RAI-HC collects information on the extent of service time provided to the client by a formal caregiver for direct care or care management “over the last seven days or since last assessment if less than 7 days.”²⁶ Service categories include Clinical Specialties Support care and Non-regulated Support care types listed in the RAI-HC.²⁷ Clinical Specialties Support care time (hours) was computed as the sum of physical therapy, occupational therapy, speech therapy, nursing, and social work. Non-regulated Support care time was computed as the sum of home health aids, homemaking, meals, volunteer services, and day care.

Derived Measures from AHS Data

Health Utility Index (HUI)

The HUI is a generic preference-weighted instrument used to identify HRQoL through multiple attributes.²⁸ It can be used to describe changes over time in health status classification or numerically as a utility-scoring formula.²⁸ The HUI3 functions as a scoring tool using 8 independent health attributes (vision, hearing, speech, ambulation, dexterity, emotion, cognition and pain) with 5 to 6 levels of function per attribute.²⁸ The HUI3 health attributes are then combined through the Multi-Attribute Utility Function formula to identify an overall single preference score. HUI3 has been previously validated and demonstrated to be responsive to changes in health in patients with various chronic diseases.²⁹ Minimally important change (MIC) was used to define significant change, with a difference of 0.03 in the overall HUI3 score and a difference of 0.05 for inividual attributes was considered meaningful.³⁰

Mapping RAI-HC to HUI3 (interRAI HRQoL Outcome)

Hirdes et al¹¹ created a mapping algorithm between the RAI-HC and the HUI3 to reproduce both the clinical and theoretical constructs of the HUI3 while using RAI-HC data. This process uses information collected from the RAI-HC to identify both health attributes and an overall HRQoL value, the interRAI HRQoL. Earlier research notes that data mapped from the RAI-HC to the HUI3 is sensitive to changes in clinical status and can thereby be used as a summary indicator for comparison of HRQoL over time.¹¹

To calculate HUI3 overall utility score and the utility of each attribute based on the RAI-HC items or scales, we first assigned a score at the HUI3 attribute level based on the severity of RAI-HC items or scales.¹¹ Subsequently, the corresponding utility weights were assigned to HUI3 attribute levels, and the overall HUI3 utility score was computed using the standard HUI3 formula²⁶:

\begin{array}{l} Overall HUI 3 \\ utility score = 1.371 \times (UTvision \times UThearing \times UTspeech \\ \times UTambulation \times UTdexterity \times UTemotion \\ \times UTcognition \times UTpain) - 0.37 \end{array}

Where UT is the utility score on an attribute of HUI3.

Statistical Analysis

Results were reported as counts and percentages for categorical variables and mean with standard deviations (SD) for continuous variables. Descriptive and regression analyses examined baseline (Time 1), follow-up (Time 2) and change over time in the HUI3 utility score based on stable (±0.03), improved (>0.03), or declined (<−0.03) HUI3 utility score. These sub-groups were defined by the pre-set MIC for the HUI tool. Descriptive statistics summarized client characteristics, health, functional status, service provision, and HRQoL. Chi-square tests were applied to examine the overall differences in the HUI3 utility change by each covariate. Significant values were set at P < .05.

To examine how changes in the HUI3 utility were related to support care changes and client characteristics, univariate and multivariable logistic regressions compared clients with decreased HUI3 to those with improved or stable HUI3 scores over the follow-up. In regression analysis, the predictor or independent variables were support care change, age, sex, and CI change. Baseline HUI3 score, CI score, support care, and follow-up time were included as confounders. The outcome variable of HUI3 change was dichotomized as “decreased HUI3” relative to “improved and stable HUI3” over time. The odds ratio (OR) indicates the odds of having a decreased HUI3 greater than 0.03 points from baseline to follow-up, which suggests a clinically important change in the HUI according to the literature.²⁹ The univariate model estimated effects of gender, age, CI change, specialized care and non-specialized care change, and time elapsed between assessments. In the multivariable logistic regression, model 1 included gender, age, CI change, time elapsed between assessments, and baseline HUI3 and CI score. Model 2 further included changes in care services and their baseline scores. Goodness of fit of the regression model was tested using likelihood-ratio tests (LRT). Due to substantial missing PDI data (41.8%), this variable was removed from regression analysis. Because the HUI3 range was small (−0.36 for worst health and 1 for best health), and some clients had negative scores, a standardized baseline HUI3 utility score was used in the multivariable regression model for ease of interpretation. Thus, the association between baseline HUI3 score and decreased HUI3 was interpreted as the odds associated with a 1 standard deviation change from the baseline mean. Analysis was completed using STATA V.15.

Ethical Considerations

This study was approved by the Research Ethics Board at the University of Alberta (Identification Number: Pro00108790). The authors received fully anonymized archived medical record data which held no information that could identify individual participants during or after data collection. As such, the ethics committee waived the requirement for informed consent.

Results

Description of the Sample’s Characteristics and Service Utilization

The sample consisted of 8743 clients (66.9% women) and 17,486 observations. Figure 1 shows the sample selection process, and Table 1 presents the characteristics of clients. At baseline, the mean age was 83.1 years (SD 8.3), ranging from 65 to 108 years. The largest age group was 85 years and older (48.6%), followed by clients aged 75 to 84 years (33.4%). The mean (SD) CI was 3.18 (1.97) at baseline and 3.51 (2.05) at follow-up. Among the clients, 16.6% had a 1-score increase in CI, and 15.9% had a 2-score or greater increase over time. PDI scores were available in 58.2% of clients. At baseline, 69.4% of the clients did not have Clinical Specialties Support and 21.6% did not have Non-regulated Support. During the baseline year, increases in care time were observed in 29.3% of clients accessing Clinical Specialties Support care and 52.2% accessing Non-regulated Support care (Table 1).

Table 1.

Characteristics of the Clients, Health Care Services, and HUI3 Utility Scores.

Variable	Time 1: Baseline (March 2018 to Feb 2019)	Time 2: Follow-up (March 2019 to Feb 2020)
Gender: n (%)
Male	2896 (33.12)
Female	5847 (66.88)
Age group: n (%)
65-74 years	1573 (17.99)
75-84 years	2922 (33.42)
≥85 years	4248 (48.59)
Pampalon Deprivation Index (PDI) score: n (%)
1 (least deprived)	1500 (17.16)
2	799 (9.14)
3	1131 (12.94)
4	881 (10.08)
5 (most deprived)	778 (8.90)
PDI (not known)	3654 (41.79)
CI score: mean (standard deviation, SD)	3.18 (1.97)	3.51 (2.05)
CI change group (follow-up score minus baseline score): n (%)
CI decreased and no change	5907 (67.56)
CI increased = 1	1449 (16.57)
CI increased ≥ 2	1387 (15.86)
Clinical Specialties Support care received* (hours/week): mean (SD)	0.74 (1.86)	1.20 (2.75)
Clinical Specialties Support care change (follow-up hours minus baseline hours): n (%)
No change and decreased	6179 (70.67)
Increased	2564 (29.33)
Non-regulated Support care received** (hours/week): mean (SD)	6.73 (10.19)	8.79 (11.47)
Non-regulated Support care change (follow-up hours minus baseline hours): n (%)
No change and decreased	4182 (47.83)
Increased	4561 (52.17)
HUI3 global and attributes’ scores: mean (SD)
Global HUI3 utility score	0.382 (0.296)	0.298 (0.315)
Vision	0.939 (0.093)	0.927 (0.102)
Hearing	0.946 (0.084)	0.937 (0.089)
Speech	0.969 (0.054)	0.955 (0.068)
Ambulation	0.868 (0.111)	0.825 (0.129)
Dexterity	0.994 (0.041)	0.989 (0.053)
Emotion	0.917 (0.157)	0.912 (0.161)
Cognition	0.841 (0.177)	0.800 (0.196)
Pain	0.933 (0.090)	0.935 (0.088)

Abbreviations: CI, Comorbidity Index; HUI3, Health Utility Index Mark 3.

Physiotherapy, occupational therapy, speech therapy, nursing, and social work.

Home health aids, meals, homemaking, volunteer services, day care.

Description of the Samples HRQoL

The mean HUI3 score at baseline was 0.382 (SD 0.296), ranging between −0.346 and 0.984. At follow-up, the mean HUI3 score decreased to 0.298 (SD 0.315). Baseline HUI3 scores by covariate groups are presented in S2 Table. The mean HUI3 score decreased by 0.084 (Table 1). A decrease in the mean individual HUI3 attribute categories occurred in all attributes except pain (increase by 0.002). The largest drop in mean health attribute occurred in ambulation (−0.043) and cognition (−0.041; Table 1).

Changes in HRQoL Based on Characteristics and Service Use

Table 2 presents the frequency distribution of HUI3 change groups by client characteristics, CI change, and support care. Figures 2 and 3 show the cumulative distribution of the change of global HUI3 scores by age group, and in total sample, respectively. Over time, approximately 28% of clients improved, 20% remained stable, and 52% had decreased HUI3 scores (Figures 2 and 3, and Table 2). Clients with increased hours of Clinical Specialties Support and Non-regulated Support care were more likely to have decreased HUI3 scores than clients with no change and/or decreased hours of the same care (Table 2).

Table 2.

Frequency Distribution (n (%)) of HUI3 Change Group by the Client Characteristics, and Service Use.

Variables	Improved HUI3	Stable HUI3	Decreased HUI3	P-value
Total sample	2436 (27.86)	1741(19.91)	4566 (52.22)
Gender				.906
Male	811 (28.00)	569 (19.65)	1516 (52.35)
Female	1625 (27.79)	1172 (20.04)	3050 (52.16)
Age group (baseline)				<.001
65-74 years	502 (31.91)	374 (23.78)	697 (44.31)
75-84 years	875 (29.95)	560 (19.16)	1487 (50.89)
≥85 years	1059 (24.93)	807 (19.00)	2382 (56.07)
Pampalon Deprivation Index (PDI) score				<.001
1 (least deprived)	404 (26.93)	304 (20.27)	792 (52.80)
2	210 (26.28)	176 (22.03)	413 (51.69)
3	335 (29.62)	233 (20.60)	563 (49.78)
4	259 (29.40)	202 (22.93)	420 (47.67)
5 (most deprived)	252 (32.39)	182 (23.39)	344 (44.22)
PDI (not known)	976 (26.72)	644 (17.63)	2033 (55.65)
CI change group (follow-up score minus baseline score)				<.001
CI decreased and no change	1777 (30.08)	1420 (24.04)	2710 (45.88)
CI increased = 1	364 (25.12)	199 (13.73)	886 (61.15)
CI increased ≥ 2	295 (21.27)	122 (8.80)	970 (69.94)
Clinical Specialties Support care (follow-up hours minus baseline hours)				<.001
No change and decreased	1799 (29.11)	1426 (23.08)	2954 (47.81)
Increased	637 (24.84)	315 (12.29)	1612 (62.87)
Non-regulated Support care (follow-up hours minus baseline hours)				<.001
No change and decreased	1089 (26.04)	938 (22.43)	2155 (51.53)
Increased	1347 (29.53)	803 (17.61)	2411 (52.86)

Abbreviations: CI, Comorbidity Index; HUI3, Health Utility Index Mark 3.

Bold P-value indicates statistically significant results (P < 0.05). HUI3 stable: difference > −0.03 and <0.03; HUI3 increased if the difference ≥ 0.03, HUI3 decreased if difference ≤ −0.03).

Associations of Client Characteristics and Service Use for Individuals with Decreased HRQoL

Table 3 shows the logistic regression results for clients with decreased HUI3 utility scores compared to clients with improved or stable HUI3 scores. Age was found to be impactful in both the univariate and multivariable models, with increasing age (75-84 years and 85 years or older) demonstrating higher odds for a decreased HUI3 score compared to the age group of 65 to 74 years. Sex was not significantly related to decreased HUI3 score. An increase in the CI score during the assessment period was associated with higher odds of decline in HRQoL. Similarly, clients with an increase in Clinical Specialties Support and Non-regulated Support care over time (relative to no change and decrease in receiving the care) were significantly more likely to experience a decreased HUI3 after adjusting for their baseline scores and other covariates. The likelihood-ratio tests presented a good fit of the multivariable logistic regression models relative to the regression model without a covariate adjustment (likelihood-ratio (LR) Chi-square = 1004.86, P < .001 for Multivariable Model 2; LR Chi-square = 816.00, P < .001 Multivariable Model 1). Furthermore, the fit of multivariable model 2 was better than the multivariable model 1 (LR Chi-square (degree of freedom 4) = 188.87, P < .001).

Table 3.

Logistic Regression Models for the Odds of Decreased HUI3 Utilities Over Time (Use Change of CI and Care).

	Univariate model		Multivariable model 1^a		Multivariable model 2^b
Variables	OR (95% CI)	P-value	OR (95% CI)	P-value	OR (95% CI)	P-value
Gender
Female vs. Male	0.99 (0.91, 1.09)	.871	1.01 (0.92, 1.11)	.782	1.03 (0.94, 1.13)	.550
Age
65-74 years	1.0		1.0		1.0
75-84 years	1.30 (1.15, 1.47)	<.001	1.31 (1.15, 1.49)	<.001	1.30 (1.14, 1.48)	<.001
≥85 years	1.60 (1.43, 1.80)	<.001	1.67 (1.48, 1.89)	<.001	1.67 (1.47, 1.89)	<.001
CI change groups
No change and decreased	1.0		1.0		1.0
Increased 1 score	1.86 (1.65, 2.09)	<.001	1.95 (1.73, 2.21)	<.001	1.82 (1.61, 2.06)	<.001
Increased 2 scores or more	2.74 (2.42, 3.11)	<.001	2.95 (2.58, 3.36)	<.001	2.65 (2.32, 3.03)	<.001
Clinical Specialties Support care change
No change and decreased	1.0				1.0
Increased	1.85 (1.68, 2.03)	<.001			1.88 (1.70, 2.08)	<.001
Non-regulated Support care change
No change and decreased	1.0				1.0
Increased	1.05 (0.97, 1.15)	.213			1.13 (1.03, 1.24)	.008
Follow-up time
<365 days	1.0		1.0		1.0
≥365 days	1.00 (0.92, 1.09)	.979	1.02 (0.93, 1.11)	.695	1.04 (0.95, 1.14)	.408
HUI3 utility score* (standardized, baseline)			1.61 (1.54, 1.69)	<.001	1.75 (1.66, 1.84)	<.001
CI score (baseline)			1.04 (1.01, 1.06)	.001	1.03 (1.00, 1.05)	.031
Clinical Specialties Support care (baseline)					1.03 (1.00, 1.05)	.027
Non-regulated Support care (baseline)					1.01 (1.01, 1.02)	<.001

Abbreviations: CI, Comorbidity Index; HUI3, Health Utility Index Mark 3.

Bold P-value indicates statistically significant results (P < 0.05).

*Calculation of the standardized HUI3 utility score: the observed score subtracted the mean and divided by the standard deviation for each observation.

Multivariable model 1: adjusted for gender, age, change of CI, follow-up time, and baseline HUI3 utility and CI score.

Multivariable model 2: additional adjustment of the change of care services (as 2 groups) and their baseline scores from multivariable model 1.

Discussion

This study found that HRQoL among older adults recieving HC services was low at baseline, and it decreased significantly over 1 year for over half the population. Client characteristics were important, as individuals in the oldest age categories and those with an increase in chronic conditions during follow-up were more likely to experience a decrease in HRQoL. Service use was also associated with these changes: clients with declining HRQoL were more likely to receive increases in Clinical Specialities Support and Non-regulated Support care. The largest declines were observed in the cognition and ambulation dimensions. Overall, several factors were associated with decreased HRQoL among long-term HC clients, starting from an already modest level.

The mapping of the RAI-HC scores to HUI3 using a previously validated algorithm¹¹ provides a promising option to estimate HRQoL for older adult HC clients. The observed HUI3 outcomes (0.382 at baseline and 0.298 at follow-up) were comparable to those reported in other Ontario research on long-stay HC clients (mean HUI3 score of 0.34).¹¹ Considering information completeness and accuracy, this method appears feasible and valid for collecting HRQoL data for an HC population that is often underrepresented in research.

This study found that clients in older age groups were more likely to have a decrease in HUI3 score. Earlier research has also reported modestly lower HRQoL scores amongst HC clients aged 85 years and older when compared to younger HC clients.¹¹ One possibility is that the older age category may experience greater age-related accumulation of health deficits. Research on frailty has suggested that it can be understood as a state of poor health due to an ongoing accumulation of deficits across functional or health attributes.³¹ Although heterogeneity in aging and frailty is relevant to HC client research, it was not specifically evaluated in this study.

Our findings also align with previous Canadian research showing that an increase in the number of chronic conditions is associated with lower HRQoL.³² In this study, an increase in the CI score during the follow-up period was strongly associated with a decline in the HUI3 outcome, even after adjusting for baseline CI score, and other covariates. This suggests that individuals may be able to adapt to existing health diagnoses over time, but that the onset of new chronic conditions continues to have a negative effect on HRQoL.

This study also identified an association between service use and HRQoL. Specifically, clients who experienced a decline in HRQoL were more likely to receive increased Clinical Specialties. As HC services are provided based on unmet need, these results may suggest that the staff coordinating services are able to identify clients who have experienced impactful changes in HRQoL and increase services accordingly.

The high proportion of HC clients experiencing HRQoL decline highlights the need for targeted interventions. Earlier research has shown that interventions that can improve HRQoL, such as regular monthly visits from nurses completing comprehensive in-home assessments.³³ Further studies examining effective interventions to address HRQoL of older adults receiving home healthcare support, would be beneficial.

Limitations

This study has several limitations. As this study relied on administrative health data, the range of predictor variables was limited, restricting the ability to examine additional confounders such as socioeconomic variables. Disease information was limited to the 28 categories in the RAI-HC. In addition, only clients with 2 sequential RAI-HC assessments were included, which may have introduced selection bias toward more stable clients. Clients who died, were hospitalized, or moved to other care settings were excluded, as they would not have had a second RAI-HC completed.

Other limitations include the inability to verify data accuracy in anonymized AHS records, potential linkage errors, and the lack of detail on the specific care provided during home visits. The fixed study period (2018-2020) may also have captured secular trends, such as seasonal variation in service use. Finally, because the data came from 1 metropolitan area (the city of Edmonton and surrounding areas) in a single Canadian province, results should be generalized with caution.

Future Research

Future studies could extend this work by including rural communities and other areas across Canada, as well as examining differences among individuals who identify as immigrants, refugees, or indigenous. Longitudinal analysis with more than 2 time points and advanced modeling approaches (such as multilevel or latent growth modeling) would also provide deeper insight into HRQoL trajectories.

Conclusion

This longitudinal study of administrative health data among HC clients identified several factors associated with declines in HRQoL, including older age (e.g., aged 75 years and older), increasing numbers of health conditions, and greater use of Clinical Specialties Support and Non-regulated Support services. HUI3 estimates derived from the RAI-HC data offer a practical way to monitor trends in client HRQoL and inform HC management decisions. These findings suggest that healthcare providers should pay particular attention to older clients, those with poorer and declining health status, and those with recent increases in HC service needs in order to help mitigate HRQoL decline.

Supplemental Material

sj-pdf-1-hhc-10.1177_10848223261472423 – Supplemental material for Associations Between Client Characteristics, Service Utilization and Health-Related Quality of Life of Older Adults Accepting Home Care

Supplemental material, sj-pdf-1-hhc-10.1177_10848223261472423 for Associations Between Client Characteristics, Service Utilization and Health-Related Quality of Life of Older Adults Accepting Home Care by Julie Flemming, Xiu Yun Wu, Nguyen Xuan Thanh, Shanthi Johnson and Arto Ohinmaa in Home Health Care Management & Practice

Supplemental Material

sj-pdf-2-hhc-10.1177_10848223261472423 – Supplemental material for Associations Between Client Characteristics, Service Utilization and Health-Related Quality of Life of Older Adults Accepting Home Care

Supplemental material, sj-pdf-2-hhc-10.1177_10848223261472423 for Associations Between Client Characteristics, Service Utilization and Health-Related Quality of Life of Older Adults Accepting Home Care by Julie Flemming, Xiu Yun Wu, Nguyen Xuan Thanh, Shanthi Johnson and Arto Ohinmaa in Home Health Care Management & Practice

Footnotes

Acknowledgements

We would like to thank our ALA colleagues, Colleen Berean, Melissa Jameson, and Alexei Potapov, for their support in accessing data and for providing valuable insights in completing this project.

ORCID iDs

Julie Flemming

Xiu Yun Wu

Nguyen Xuan Thanh

Ethical Considerations

The University of Alberta Research Ethics Board has approved (Identification Number: Pro00108790) the study. Approval was also received from AHS, the organization that provides publicly funded home care services. Information provided to the research team was anonymized prior to release from AHS. Client consent was not required for use of this data.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: No financial relationships exist that may lead to a conflict of interest. Julie Flemming received financial contributions from the Network of Alberta Health Economists Postdoctoral Fellowship, supported financially and in-kind by Alberta Health and administered by the Institute of Health Economics ().

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Julie Flemming is an employee of the Home, and Community Care program, Assisted Living Alberta (formerly within Alberta Health Services).

Data Availability Statement

The data that support the findings of this study are not publicly available.

Supplemental Material

Supplemental material for this article is available online.

References

Gilmour

Formal home care use in Canada. Health Rep. 2018;29(9):3-9. https://www150.statcan.gc.ca/n1/pub/82-003-x/2018009/article/00001-eng.htm

Markle-Reid

Browne

Weir

Gafni

Roberts

Henderson

Seniors at risk: the association between the six-month use of publicly funded home support services and quality of life and use of health services for older people. Can J Aging. 2008;27(2):207-224. doi:10.3138/cja.27.2.207

Coyte

McKeever

Home care in Canada: passing the buck. Can J Nurs Res. 2001;33(2):11-25. https://pubmed.ncbi.nlm.nih.gov/11928333/

Mukamel

Fortinsky

White

Harrington

White

Ngo-Metzger

The policy implications of the cost structure of home health agencies. Medicare Medicaid Res Rev. 2014;4(1):mmrr2014.004.01.a03. doi:10.5600/mmrr2014-004-01-a03

Trukeschitz

Hajji

Kieninger

Malley

Linnosmaa

Forder

Investigating factors influencing quality-of-life effects of home care services in Austria, England and Finland: A comparative analysis. J Eur Soc Policy. 2021;31(2):192-208. doi:10.1177/0958928720974189

WHO. The World Health Organization Quality of Life assessment (WHOQOL): position paper from the World Health Organization. Soc Sci Med. 1995;41(10):1403-1409. doi:10.1016/0277-9536(95)00112-k

Hand

Measuring health-related quality of life in adults with chronic conditions in primary care settings: critical review of concepts and 3 tools. Can Fam Physician. 2016;62(7):e375-e383.

Chen

Kochen

MM.

A systematic review: how to choose appropriate health-related quality of life (HRQOL) measures in routine general practice?

J Zhejiang Univ Sci B. 2005;6(9):936-940. doi:10.1631/jzus.2005.B0936

Chan

Slaughter

Jones

Wagg

AS.

Greater independence in activities of daily living is associated with higher health-related quality of life scores in nursing home residents with dementia. Healthcare. 2015;3(3):503-518. doi:10.3390/healthcare3030503

10.

Al Sayah

Lahtinen

Bonsel

Ohinmaa

Johnson

JA.

A multi-level approach for the use of routinely collected patient-reported outcome measures (PROMs) data in healthcare systems. J Patient Rep Outcomes. 2021;5(Suppl 2):98. doi:10.1186/s41687-021-00375-1

11.

Hirdes

Bernier

Garner

Finès

Jantzi

Measuring health related quality of life (HRQoL) in community and facility-based care settings with the interRAI assessment instruments: development of a crosswalk to HUI3. Qual Life Res. 2018;27(5):1295-1309. doi:10.1007/s11136-018-1800-0

12.

Zubritsky

Abbott

Hirschman

Bowles

Foust

Naylor

MD.

Health-related quality of life: expanding a conceptual framework to include older adults who receive long-term services and supports. Gerontologist. 2013;53(2):205-210. doi:10.1093/geront/gns093

13.

Murphy

Schwerin

Eyerman

Kennet

Barriers to survey participation among older adults in the national survey on drug use and Health: the importance of establishing trust. Surv Pract. 2008;1(2):1-6. doi:10.29115/sp-2008-0006

14.

Malicka

Skośkiewicz-Malinowska

Kaczmarek

The impact of socioeconomic status, general health and oral health on health-related quality of life, oral health-related quality of life and mental health among Polish older adults. BMC Geriatr. 2022;22(1):2. doi:10.1186/s12877-021-02716-7

15.

Pan

Cong

Zhou

, et al. Evaluating health-related quality of life impact of chronic conditions among older adults from a rural town in Suzhou, China. Arch Gerontol Geriatr. 2018;76:6-11. doi:10.1016/j.archger.2018.01.008

16.

Blotenberg

Seeling

Büscher

The health-related quality of life of older people through preventive home visits: a quantitative longitudinal study. Scand J Caring Sci. 2023;37(3):698-709. doi:10.1111/scs.13150

17.

Wagner

Schaffert

Möckli

Zúñiga

Dratva

Home care quality indicators based on the Resident Assessment Instrument-Home Care (RAI-HC): a systematic review. BMC Health Serv Res. 2020;20(1):366. doi:10.1186/s12913-020-05238-x

18.

Parsons

Senior

Mei-Hu Chen

, et al. Assessment without action; a randomised evaluation of theinterRAI home care compared to a national assessment tool on identification of needs and service provision for older people in New Zealand. Health Soc Care Community. 2013;21(5):536-544. doi:10.1111/hsc.12045

19.

Introduction to the RAI–HC–10001. April 17, 2021, 2021. Accessed April 17, 2021. https://mylearninglink.albertahealthservices.ca/data/SCORM/3715/index_scorm.html. Alberta.

20.

Morris

Fries

Steel

, et al. Comprehensive clinical assessment in community setting: applicability of the MDS-HC. J Am Geriatr Soc. 1997;45(8):1017-1024. doi:10.1111/j.1532-5415.1997.tb02975.x

21.

Morris

Carpenter

Berg

Jones

RN.

Outcome measures for use with home care clients. Can J Aging. 2000;19(S2):87-105. doi:10.1017/s071498080001391x

22.

Hirdes

Fries

Morris

, et al. Integrated health information systems based on the RAI/MDS series of instruments. Health Manage Forum. 1999;12(4):30-40. doi:10.1016/S0840-4704(10)60164-0

23.

Alberta Health Services. How to use the Pampalon Deprivation Index in Alberta . Alberta Health Services Insite, 2016:1-31. Accessed July 26, 2026. https://insite.albertahealthservices.ca/Main/assets/tms/phsi/tms-phsi-ri-pampalon-user-guide-for-alberta.pdf

24.

Charlson

Pompei

Ales

MacKenzie

CR.

A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. doi:10.1016/0021-9681(87)90171-8

25.

Quan

Couris

, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173(6):676-682. doi:10.1093/aje/kwq433

26.

Canadian Institute for Health Information. RAI-Home Care (RAI-HC)© User’s Manual. Canadian Version, Ottawa, ON. CIHI; 2010:1-151.

27.

Minimum Data Set Home Care (MDS-HC) Canadian Version. interRAI Cooperation; Alberta Health Services–Insite. Updated 2010. Accessed May 30, 2021. Alberta Health Services. https://insite.albertahealthservices.ca/sh/Page9466.aspx#wb-auto-2

28.

Torrance

GW.

Preferences for health outcomes and cost-utility analysis. Am J Manag Care. 1997;3 Suppl:S8-20. https://pubmed.ncbi.nlm.nih.gov/10180342/

29.

Kavirajan

Hays

Vassar

Vickrey

BG.

Responsiveness and construct validity of the health utilities index in patients with dementia. Med Care. 2009;47(6):651-661. doi:10.1097/MLR.0b013e31819241b9

30.

Horsman

Furlong

Feeny

Torrance

The Health Utilities Index (HUI): concepts, measurement properties and applications. Health Qual Life Outcomes. 2003;1:54. doi:10.1186/1477-7525-1-54

31.

Howlett

Rutenberg

Rockwood

The degree of frailty as a translational measure of health in aging. Nature Aging. 2021;1:651-665. doi:10.1038/s43587-021-00099-3

32.

Mondor

Maxwell

Bronskill

Gruneir

Wodchis

WP.

The relative impact of chronic conditions and multimorbidity on health-related quality of life in Ontario long-stay home care clients. Qual Life Res. 2016;25(10):2619-2632. doi:10.1007/s11136-016-1281-y

33.

Markle-Reid

Browne

Gafni

Nurse-led health promotion interventions improve quality of life in frail older home care clients: lessons learned from three randomized trials in Ontario, Canada. J Eval Clin Pract. 2013;19(1):118-131. doi:10.1111/j.1365-2753.2011.01782.x

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.07 MB

0.63 MB