Pain-Associated Clusters Among Nursing Home Residents and Older Adults Receiving Home Care in Germany

Journal Pre-proof Pain-Associated Clusters Among Nursing Home Residents and Older Adults Receiving Home Care in Germany Andrea Budnick, PhD, Ronny Kuhnert, PhD, Arlett Wenzel, MSc Medical Education, Mimi Tse, PhD, RN, Prof., Juliana Schneider, MPharm, Reinhold Kreutz, MD, PhD, Prof., Dagmar Dräger, PhD PII:

S0885-3924(20)30070-1

DOI:

https://doi.org/10.1016/j.jpainsymman.2020.01.018

Reference:

JPS 10374

To appear in:

Journal of Pain and Symptom Management

Received Date: 7 October 2019 Revised Date:

24 January 2020

Accepted Date: 24 January 2020

Please cite this article as: Budnick A, Kuhnert R, Wenzel A, Tse M, Schneider J, Kreutz R, Dräger D, Pain-Associated Clusters Among Nursing Home Residents and Older Adults Receiving Home Care in Germany, Journal of Pain and Symptom Management (2020), doi: https://doi.org/10.1016/ j.jpainsymman.2020.01.018. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc. on behalf of American Academy of Hospice and Palliative Medicine

Running head: PAIN-ASSOCIATED CLUSTERS

Pain-Associated Clusters Among Nursing Home Residents and Older Adults Receiving Home Care in Germany Authors: Andrea Budnick, Ronny Kuhnert, Arlett Wenzel, Mimi Tse, Juliana Schneider, Reinhold Kreutz, Dagmar Dräger

Andrea Budnick, PhD Institute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, Berlin, Germany

Ronny Kuhnert, PhD Institute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, Berlin, Germany

Arlett Wenzel, MSc Medical Education Institute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, Berlin, Germany

Prof. Mimi Tse, PhD, RN School of Nursing, Hong Kong Polytechnic University, Hong Kong, China

Juliana Schneider, MPharm Institute of Clinical Pharmacology and Toxicology, Charité - Universitätsmedizin Berlin, Berlin, Germany

Prof. Reinhold Kreutz, MD, PhD Institute of Clinical Pharmacology and Toxicology, Charité - Universitätsmedizin Berlin, Berlin, Germany

Running head: PAIN-ASSOCIATED CLUSTERS Dagmar Dräger, PhD Institute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, Berlin, Germany

Correspondence concerning this article should be addressed to Dr. Andrea Budnick, Institute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, Virchowweg 22, 10117 Berlin, Germany, Phone: +49 30 450 529 064, Fax: +49 30 450 529 972, E-mail: [email protected]

Number of tables:

4

Figures:

2

Reference count:

47

Word count abstract:

207

Word count main text (excluding tables and references):

4.524

Cluster analysis in nursing homes and home care setting

Pain-Associated Clusters Among Nursing Home Residents and Older Adults Receiving Home Care in Germany Abstract Context. There are no available data regarding pain-associated clusters among nursing home residents and older adults receiving home care with chronic pain. Objectives. To identify and describe pain-associated clusters in nursing home residents and older adults receiving home care with chronic pain, and to explore associations with clusters in both settings. Methods. We surveyed 137 nursing home residents and 205 older adults receiving home care. Clusters were identified using hierarchical agglomerative cluster analysis, utilizing Ward’s method with Squared Euclidean Distances in the proximities matrix. The clusters were characterized based on socio-demographic and clinical characteristics. Multinomial logistic regression was used to identify variables associated with different clusters. Results. In each setting, we identified three clusters: pain-relieved, pain-impaired, and suffering severe pain. In the nursing home study and the home care study, respectively, the participant distributions were 46.72% and 11.71% in the pain-relieved cluster, 22.63% and 33.66% in the pain-impaired cluster, and 30.66% and 54.63% in the severe-pain cluster. Appropriate pain medication was only detected among pain-relieved nursing home residents. Conclusion. Overall, differences in pain management exist within the two care settings presented here. There is potential for improvement in both settings. Moreover, there exists a need for clinical interventions aiming at shifting from pain-affected clusters to painrelieved status.

Key Message: We identified three clusters in each analyzed setting. In the nursing home setting, almost half of participants were in the pain-relieved cluster (46.72%). In the home care setting, the severe-pain cluster contained the highest proportion of individuals (54.63%). Appropriate chronic pain management is still not yet ubiquitous.

Key Words: Cluster, chronic pain, nursing home, home care, long-term care Running Title: Cluster analysis in nursing homes and home care setting

1

Cluster analysis in nursing homes and home care setting

Introduction Chronic pain is common in older adults, particularly among those requiring long-term care. Internationally, the reported pain prevalence ranges from 3.7% to 79.5% among nursing home residents,1,2 and is estimated to be 38.5% among home care recipients.3 German data indicate that approximately every other nursing home resident of ≥ 65 years of age suffers from pain,4,5 and a pain prevalence rate of 68.5% has been reported among adults (≥ 18 years of age) receiving home care.6 Unrelieved chronic pain among older adults is generally associated with reduced physical functioning and psychological parameters (e.g., impaired mobility, lower happiness, and poorer quality of life).7–9 Nursing home residents with chronic pain report varying pain intensity, and find that pain commonly interferes with function.10 Notably, longitudinal data have shown improved mobility among nursing home residents following a complex intervention aiming to improve pain management.11 In a recent study, we identified major deficits in non-pharmacological and pharmacological pain management for nursing home residents and older adults receiving home care.12-15 Prior studies clustering chronic pain patients with cancer have focused on symptom clusters.16,17,18,19,20 Although, this is not a symptom cluster paper,21,22 clustering is necessary to describe and compare older adults affected by chronic pain, to identify differences in pain management and to improve it.23 To our knowledge, there are no available data regarding pain-associated clusters (subgroups of patients with distinct pain profiles) based on items measuring pain intensity and pain interference with function among nursing home residents or older adults receiving home care. Given the high burden of pain among older adults, we hypothesized that there were likely pain-associated clusters, regarding pain intensity and pain interference with function, in both settings. Defining pain-associated clusters and the determining factors may enable better targeted health-service delivery for doctors and nurses. In the present study, we aimed to identify and compare for the first time painassociated clusters among persons ≥ 65 years old with chronic pain and capable of self-report, including both nursing home residents and older adults receiving home care. We also examined whether individuals in clusters differed within each setting in terms of sociodemographic and clinical characteristics and finally, which of these characteristics are associated with clusters (sub-groups of patients with distinct pain profiles).

2

Cluster analysis in nursing homes and home care setting

Methods Study Population Nursing Home Study (n = 137). This study was performed using baseline data, and was part of a cluster-randomized controlled trial conducted in 12 nursing homes in Berlin, Germany, which were within the same for-profit chain. Sample size was calculated by: pain prevalence of 55%, 50% of residents with an MMSE score ≥18, and a follow-up mortality rate of 10%, resulting in a necessary group size of 96. Power calculation was based on 80%, a two-sided alpha of 0.05 and an intracluster correlation coefficient (ICC) of 0.12.10 This study was registered with the German Clinical Trials Register (DRKS00004239). Nursing home residents participated in our study if they were ≥ 65 years, affected by chronic pain (≥ 3 months), requiring care according to Volume XI of the German Social Security Code (SGB), capable of self-report (MMSE ≥ 18), and had lived in the nursing home facility for ≥ 3 months.

Home Care Study (n = 205). The current observational study was based on data from the project ACHE which was designed to examine the current pain management situation among home care recipients in Berlin, Germany.19 Home care recipients participated if they were ≥ 65 years, affected by chronic pain (≥ 3 months), requiring care according to Volume XI of SGB and capable of self-report (MMSE ≥ 18). We recruited a convenience sample of home care recipients with chronic pain via home care services in Berlin. A representative sample would have been desirable. What prevented this was that a central register for care recipients from which a representative random sample for research purposes could be taken does not exist for the relevant target group. A stratified random sample, whereby in the first stage, care services and in the second stage, test persons could be randomly selected, was planned but could not be carried out, because willingness to participate voluntarily was extremely low among home care service workers due to staff shortages and permanently excessive work loads. In addition, some of the home care services indicated that in case of voluntary participation in the study, they would determine the selection of test persons and would not enable a random selection of test persons, for organizational reasons. Quota sampling, which is not a random sample but is based on defined quotas oriented on a known base population, was not possible either, because a reliable database to determine the quota of home-care recipients (≥65 years) with chronic pain does not exist.

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Cluster analysis in nursing homes and home care setting

Measures In both the nursing home and home care studies, sociodemographic characteristics were collected via self-report, including age, sex (female; male), partnership (yes; no), and level of care (minor = level 1 + level 2; serious = level 3 + level 4 + level 5). To determine the level of care, different items in six modules (1. mobility 10%, 2. either cognitive and communicative abilities or 3. behavior and psychiatric problem 15%, 4. self-care 40%, 5. dealing with requirements due to illness or therapy 20%, 6. organization of everyday life and social contacts 15%), that are all weighted differently contribute to the final score. The calculated score ranges from 0 to 100, with higher scores indicating worse condition. If the total score is > 12 an applicant receives level of care 1 indicating minor impairments of autonomy or of skills, level of care 2 means considerable, level of care 3 means serious, level of care 4 means severe and finally, level of care 5 means most severe impairments of autonomy or of skills with 90 to 100 overall points.25 We also collected information about the below-described variables in both study settings.

Pain. Self-reported pain was assessed using the German version of the Brief Pain Inventory (BPI26,27) with one modified item (BPI-NHR28). Individuals were asked to use a scale ranging from 0 (no pain) to 10 (worst pain) to rate their pain intensity over the past 24 hours at its worst, least, and average, as well as right now. Study participants were also asked to rate the extent to which their pain interfered with function (general activity; mood; walking; the ability to cope, mentally and physically, with daily stressors, events, and activities; relations with others; sleep; enjoyment of life) on a scale ranging from 0 (does not interfere) to 10 (interferes completely). A pain score of ≥ 3 was considered clinically significant.10

Cognitive Status. Participants were screened for cognitive status using the Mini Mental Status Examination (MMSE), which is a valued screening instrument due to its brevity.29,30 MMSE scores indicated no-to-mild (18–30 points), moderate (10–17 points), or severe (< 10 points) cognitive impairment. Multidimensional instruments like BPI26,27 can be used for individuals with an MMSE score of 18–30 points.

Body-Mass Index. Body mass index (BMI) was calculated by dividing the participant’s weight (kg) by their height squared (m2).

4

Cluster analysis in nursing homes and home care setting

Activities of Daily Living. We assessed performance of activities of daily living (ADL31,32) using the Hamburg Classification Manual for Barthel Index (BI), which accounts for functional status and required support for activities (e.g., toilet use, transfer bed to chair and back). The calculated score ranges from 0 to 100, with higher scores indicating better physical function and independence in performing ADL. Information used to determine the BI were either proxy ratings or self-reports.

Functional Performance. In the nursing home study, we used the Timed “Up & Go” test (TUG) to assess functional status in residents who were able to walk.33 The TUG is a performance-based measure, in which the resident is observed and timed while rising from an arm chair, walking 3 meters, turning, walking back, and sitting down again. A time score of < 20 seconds is a cut-off point indicating mobility and high functional performance. A score of ≥ 20 seconds indicates impaired mobility and low functional performance.33 Residents who were unable to walk, and thus unable to perform the TUG, were considered immobile. For data analyses, we used the categories high functional performance (1) for residents scoring < 20 seconds on the TUG test, and low functional performance (2) for those scoring ≥ 20 seconds or considered immobile. In the home care study, we used the Martin Vigorimeter® to measure handgrip strength to avoid falling. Handgrip strength is an accepted proxy measure of functional performance or muscle strength.34 The Martin Vigorimeter® enables assessment of handgrip strength with minimal requirements for position, and optimal capacity for older adults to generate maximal grip in an individually comfortable position.34 It measures the pressure when individuals press a rubber bulb that is connected by a tube to a manometer. We used the medium bulb, with pressure ranging from 0.0 to 1.6 bar.35 Normal values are 0.7 to 1.25 bar for healthy women, and 0.8 to 1.3 bar for healthy men.35 For the current data analyses, we used a data-based median split to divide between high functional performance (≥ 0.43 bar) and low functional performance (≤ 0.42 bar).

Pain Medication. Data about the number of medications (prescribed and self-medication) and the appropriateness of pain medication were retrieved from the patients’ medical records. We determined whether pain medication was appropriate using the German version of the Pain Medication Appropriateness Scale (PMASD).13 PMASD values of > 67.00% indicate an appropriate pain medication strategy.36

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Cluster analysis in nursing homes and home care setting

Statistical Analysis Sociodemographic and clinical characteristics were presented as mean and standard deviation (SD) for continuous variables, and as percentage for categorical variables. We employed cluster analysis because this procedure takes a number of cases and condenses them into a smaller cluster based on similarities shared by members within a group. In a first step, we used the BPI-NHR28 to classify and compare pain-associated clusters that are based on similarity in pain intensity and pain interference with function items in a sample with 137 nursing home residents. Hierarchical agglomerative cluster analysis was performed using Ward’s method with Squared Euclidean Distances in the proximities matrix.37 We screened the dendrogram and analyzed dissimilarity measures to identify the cluster solution with three clusters. The authors provide the vertical blue line that designates the three-cluster solution (Fig. 1). The distance at which the horizontal lines (branches) join indicates similarity (a shorter branch represents greater similarity). Membership of a particular cluster is defined by tracing back down the branch to the case number.37 We also performed a subsequent multivariate analysis of variance (MANOVA) to identify the relative contribution of each variable among clusters.38 For univariate comparison between cluster characteristics, we performed analysis of variances (ANOVAs) or the chi-square test. Post hoc contrasts were calculated using the Bonferroni procedure at α < 0.05. To test the quality of the cluster model, we employed replication of the cluster solution with a different data set.39 This data set comprises a sample of 205 older adults receiving home care. Applying the same statistical procedure as described above for the sample with nursing home residents (n = 137), we generated three clusters showing similar patterns regarding pain intensity and pain interference with function among older adults receiving home care (Fig. 2). The logistic regression results are shown as odds ratios (OR) and 95% confidence intervals (CI). We used Nagelkerke R² to assess the level of explained variance. For the nursing home study, forward and backward Wald procedures applied to the set of sociodemographic and clinical characteristics the same independent variables (BI, functional performance, and PMASD) as showing significant effects on the dependent dichotomous variables (impaired by pain vs. pain-relieved and suffering sever pain vs. pain-relieved), which were thus included in the final logistic regression models. For the home care study, a backward Wald procedure calculated that the best fit included BI and functional status as independent variables on the same dependent dichotomous variables as described above, 6

Cluster analysis in nursing homes and home care setting

which were used in the final model. Analyses were performed using IBM SPSS Statistics 25.0.

Results Table 1 shows sample characteristics of nursing home residents and older adults receiving home care. Nursing home residents were very old (mean age, 83.33 years), and were predominantly female (72.30%), without a partner (88.24%), and requiring a minor level of care (74.45%). Moreover, the majority of nursing home residents had a low functional performance (84.67%), were capable of self-report (mean MMSE, 23.92; 95% CI, 23.30– 24.55), were pre-obese (mean BMI, 27.44; 95% CI, 26.54–28.35), and required assistance (mean, 61.58; 95% CI, 58.21–64.95). The mean number of medications was 9.33 (95% CI, 8.70–9.95) and the mean PMASD score was 58.50 (95% CI, 53.50–63.49). Home care recipients were also very old (mean age, 81.57 years), and the majority were female (68.78%), had no partner (76.58%), required minor care (71.71%), had a high functional performance (51.71%), were capable of self-report (mean MMSE, 26.21; 95% CI, 25.85–26.57), were obese (mean BMI, 31.81; 95% CI, 29.83–33.80), and had a mean BI of 77.60 (95% CI, 74.95–80.24). The mean number of medications was 11.05 (95% CI, 10.42–11.68) and the mean PMASD score was 45.57 (95% CI, 42.41–48.72).

Please place Table 1 here.

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Cluster analysis in nursing homes and home care setting

Cluster Analysis In both the nursing home and home care studies, we identified three clusters as the best fit (Fig. 1, Fig. 2). The naming of clusters (subgroups of patients) is based on the mean value of each pain intensity and interference with function item in each cluster. MANOVA confirmed the overall significant separation of the BPI-NHR28 items included in the clustering solution for both samples (nursing home, Wilks’ λ = 0.058, F(22,284) = 35.64, P < 0.001; home care, Wilks’ λ = 0.117, F(22,384) = 33.52, P < 0.0001). We computed univariate ANOVAs, which indicated significant differences among clusters in all variables used to create the clusters (all P = 0.000). Bonferroni post-hoc analyses revealed significant differences in items among clusters. In the nursing home study, pain intensity items significantly differed among clusters (P < 0.001), with exceptions observed between the pain-impaired and severe-pain cluster for least pain (P = 0.091), average pain (P < 0.05), and right now pain (P < 0.01). With regards to pain interference with function items, we observed significant differences (P < 0.001) for all items, with exceptions observed between the pain-impaired and pain-relieved clusters for relations with others (P = 1.000), enjoyment (P = 0.262), mood, and sleep (P < 0.05). Between the painimpaired and severe-pain clusters, we found a P value of 0.002 only for the item sleep. In the home care study, post-hoc analyses revealed significant differences for all pain intensity items among all clusters (P < 0.001). For the pain interference with function items, we observed significant differences (P < 0.001) for all items among clusters, with exceptions observed between the pain-impaired and pain-relieved clusters for the items relation with others (P < 0.05) and sleep (P < 0.01).

Nursing Home Study. The first cluster, comprising pain-relieved residents according to the mean value of the BPI-NHR28 items, contained nearly half (46.72%) of this sample. The second cluster, comprising residents who reported low-to-moderate pain levels in BPI-NHR28 items, included 22.63% of the residents. The third cluster, characterizing residents suffering severe pain, comprised 30.66% of the sample; these individuals had the highest pain scores compared with the other clusters (Table 2). Home Care Study. The first cluster, characterized by pain-relieved individuals, contained 11.71% of the sample. The second cluster, including individuals with moderate pain levels, comprised 33.66% of the home care residents. The third cluster, comprising home care recipients suffering severe pain, made up 54.63% of this sample (Table 2).

Please place Table 2 here. 8

Cluster analysis in nursing homes and home care setting

Demographic and Clinical Characteristics Among Clusters and Settings Table 3 presents the three clusters for each setting according to demographic and clinical characteristics.

Nursing Home Study. All clusters included very old residents (F(2,134) = 0.187, P = 0.830) who were mostly female (χ² = 2.817, P = 0.245) and did not significantly differ in partnership (χ² = 3.166, P = 0.205) or level of care (χ² = 0.333, P = 0.846). The clusters also did not significantly differ with regards to TUG test (χ² = 3.595, P = 0.166), cognitive status (F(2,128) = 2.157, P = 0.120), or number of medications (F(2,134) = 0.867, P = 0.423). BMI differed between clusters (F(2,129) = 4.274, P = 0.016)—notably between pain-relieved residents and those suffering severe pain (P = 0.029). BI differed between clusters (F(2,133) = 3.246, P = 0.042)—most strongly between residents in the pain-relieved versus pain-impaired clusters (P = 0.038). Moreover, the clusters differed in the PMASD (F(2,119) = 12.007, P = 0.000), with pain-relieved residents more commonly receiving appropriate pain medication compared to pain-impaired (P = 0.000) and suffering severe pain residents (P = 0.001).

Home Care Study. All clusters included very old residents (F(2,202) = 0.143, P = 0.866) and were mostly female (χ² = 0.070, P = 0.966). Partnership status (χ² = 3.728, P = 0.155) and level of care (χ² = 0.705, P = 0.703) did not differ among clusters. The clusters significantly differed in functional performance (χ² = 17.014, P = 0.000), with 59.63% with lower functional performance in the severe-pain cluster (P = 0.005). We used a data-based median split to differentiate between low and high functional status (<0.43 bar and ≥0.43 bar). Although we did not include gender differences, the norm values of the Martin Vigorimeter® distinguish according to sex (rangewomen= 0.7–1.25 bar, rangemen= 0.8–1.3 bar).29 Furthermore, our gender-independent median of 0.43 bar revealed that more than half of our home care recipients exhibited a functional status below the lowest norm value for women. Expanded analysis showed median values of 0.39 bar for women, and 0.59 bar for men. The clusters did not differ in MMSE (F(2,202) = 2.377, P = 0.095), BMI (F(2,202) = 0.154, P = 0.857), BI (F(2,201) = 2.647, P = 0.073), number of medications (F(2,201) = 1.339, P = 0.264), or PMASD (F(2,197) = 0.876, P = 0.418).

Please place Table 3 here.

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Cluster analysis in nursing homes and home care setting

Logistic Regression Analyses The final model for nursing home residents showed that being in the pain-impaired cluster was significantly associated with BI (P = 0.020) and PMASD (P = 0.000), but not with functional performance. A one-point increase of BI score was associated with an increased risk of being in the pain-impaired cluster (OR: 1.04; CI: [1.01, 1.08]). A one-point increase in PMASD score was associated with a decreased risk of being impaired by pain (OR: 0.96; CI: [0.94, 0.98]). Being in the severe-pain cluster was significantly associated with functional performance (P = 0.033) and PMASD (P = 0.003), but not with BI. Mobile nursing home residents had a lower risk of being in the severe-pain cluster (OR: 0.08; CI: [0.01, 0.81]). A one-point increase of PMASD score was associated with decreased risk of being in severe-pain (OR: 0.97; CI: [0.95, 0.99]). The final model for older adults receiving home care revealed that functional performance was not associated with pain-associated cluster. Nevertheless, BI was associated with being in the pain-impaired cluster (P = 0.026) and with being in the severe-pain cluster (P = 0.051). A one-point increase of BI score was associated with slight increases in the risk of being in the pain-impaired cluster (OR: 1.03; CI: [1.00,1.05]) or in the severe-pain cluster (OR: 1.02; CI: [1.00,1.04]).

Please place Table 4 here.

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Cluster analysis in nursing homes and home care setting

Discussion In the present study, we identified and described for the first time pain-associated clusters among older adults with chronic pain, who were residing in nursing homes (n = 137) or receiving home care (n = 205), in terms of socio-demographic and clinical characteristics. We additionally analyzed which of these characteristics are associated with clusters in each care setting. Both study populations included older adults who were similar regarding age, sex, partnership, and level of care. According to the German official statistics on long-term nursing care,39 the proportion of female residents in nursing homes in 2018 was 70.00% and in the home care setting 63.00% and thus similar to our samples (72.30% and 67.78%). We must point out that both samples excluded cognitively impaired older adults, thus omitting individuals requiring a fairly serious level of care. With respect to level of care, our data for the nursing home sample (minor level: 74.45%; serious level: 22.63%) and for the home care sample (minor level: 71.71%; serious level: 25.85%) diverge considerably compared with the current long-term care statistics (nursing home, minor level: 25.10%; serious level: 74.90% and home care, minor level: 55.20%; serious level: 44.80%).40 For both data sets, three clusters provided the best fit. Cluster validity of the nursing home sample was supported by replication of the cluster solution with a different data set, a sample with older adults receiving home care. This is considered a robust way to validate cluster accuracy.39 However, there remains a need to perform further longitudinal research examining cluster stability over time. In the nursing home study (n = 137), the following clusters were detected: painrelieved residents (46.72%), pain-impaired residents (22.63%), and residents suffering severe pain

(30.66%). The pain-relieved nursing home residents generally had low functional

performance (82.81%), no cognitive impairment, and an acceptable BMI41. A meta-analysis examined BMI and all-cause mortality among older adults, and reported an increased risk of mortality for those with BMI values of < 23.0 and > 33.0.41 Compared with the other clusters, pain-relieved residents scored lowest in BI. The low BI score could be attributed to side effects of pain medication, e.g., dizziness.42 Previous findings show that half of all opioid prescriptions are given for long-term therapy in the nursing home setting.13 Pain is another possible cause of low BI. However, improved functioning among nursing home residents without severe cognitive impairment (BI and functional performance measured by TUG) could be associated with a lower PMASD score and concomitant reduced side effects of pain medication (due to shortage), but with a correspondingly higher risk of being impaired by pain. Moreover, pain-relieved residents were the only group with appropriate pain medication 11

Cluster analysis in nursing homes and home care setting

on average according to PMASD, while the number of medications did not significantly differ among clusters. Notably, residents showing better functional performance had a lower risk of being in the severe-pain cluster. This is in accordance with previous findings indicating that painrelieved residents show better functional mobility over time.11 A positive aspect of the nursing home was that pain medication was appropriate in at least one cluster. PMASD was identified as a variable influencing pain-associated clusters in the logistic regression model. Overall, the continuous treatment provided by nursing professionals and doctors in the nursing homes likely contributed to the fact that half of the residents in our study were pain-relieved, although there remains potential for optimization in pain management.4,10,12,13,16 We also identified three pain-associated clusters among older adults receiving home care (n = 205). In this setting, pain-relieved older adults constituted the smallest cluster (11.71%), while 33.66% were pain-impaired, and 54.63% were suffering severe pain. Compared to the other clusters in the home care setting, the pain-relieved cluster included individuals with rather high functional performance (56.52%). Overall, our genderindependent median of 0.43 bar supports the expectation that older adults generally exhibit a lower functional status compared to healthy younger adults.43 Moreover, the severe-pain cluster demonstrated an average BMI that is associated with mortality risk among older adults.41 With regards to BI, pain-relieved individuals scored lowest in cluster comparison. Our findings in the home care setting indicated that individuals scoring higher in BI had a slightly higher risk of being impaired by pain. Inappropriate pain medication was observed across clusters in the home care setting, such that PMASD was not identified as a variable influencing pain-associated clusters in the logistic regression model. Our prior findings in the home care setting confirm deficits in outpatient non-pharmacological and pharmacological pain management.14,15,44 It is remarkable that pain-relieved residents (46.72%) constituted almost half of the nursing home population, while the severe-pain cluster included the highest proportion of individuals in the home care setting (54.63%). Nursing home residents should have 24 hour / 7 day a week supervision. As such, better pain control in the nursing home would be expected compared to the home care setting. However, we observed deficits in non-pharmacological and pharmacological pain treatment in German nursing homes.4,12,13 Moreover, about threequarters of German people requiring care receive home care (2.59 million).45 As older adults receiving home care are in better health compared to nursing home residents45 and live in their familiar residential environment (approx. 68% are cared for by informal caregiver and approx. 12

Cluster analysis in nursing homes and home care setting

1/3 are cared for by informal caregiver and professional nursing care services) better pain management would have been definitely conceiveable. Moreover, a previous study has shown a reduction of approx. 2/10 (on a scale from 0 to 10 for average pain) to be clinically relevant.46 In that sense, our findings for nursing home residents and home care recipients indicate that a switch to a cluster with less pain, e.g. from the severe-pain to the impaired cluster, would be a clinically relevant improvement. Nevertheless, further research is necessary in both settings to prove the clinical relevance if individuals from our target group switch from one cluster to another with less pain. However, cluster findings in both settings confirm that it is possible for older adults with chronic pain to receive fair treatment by applying assessment and appropriate use of treatment modalities.47 Altogether, the clinical characteristics among clusters indicate the need for continuous pain management, i.e., regular monitoring by physicians and nurses in both settings. Our present study had several strengths and limitations. Strengths of the hierarchical cluster analysis are: no need to define the number of clusters in advance, and suitability for datasets with compact spherical clusters that are well-separated. A disadvantage of this analysis method is that the researcher decides when to stop the clustering process.37 Moreover, both study samples excluded cognitively impaired older adults (MMSE ≤ 17), those individuals that

might have more specific care needs than the broad group of

participants considered in both studies. Both nursing home sample and the home care sample comprise data from a large urban area. Accordingly, the outcomes may not be generalized to all nursing home residents or home care recipients in Germany. Moreover, functional performance was assessed by either TUG test27 or Martin Vigorimeter®.34 A comparison of these measures is lacking, and should be analyzed in future studies. Furthermore, it would be interesting to study the application of non-pharmacological treatment in both settings. However, our previous studies in nursing homes have demonstrated that such treatments are rarely administered to residents12 or home care recipients.14 Additionally, it was beyond the scope of this study to examine the impact of nurses, doctors, and informal caregivers on pain management in both settings.

Conclusion Overall, differences in pain management exist within the two care settings presented here. There is potential for improvement in both settings. Moreover, there exists a need for clinical interventions aiming at shifting from pain-affected clusters to pain-relieved status. 13

Cluster analysis in nursing homes and home care setting

This approach is intended to take account of older adults with chronic pain and in need of care in both settings and to compare care structures, in order to achieve the best possible care for this target group in both settings. Disclosures and Acknowledgements The nursing home study was funded by the Federal Ministry of Education and Research of Germany (01ET1001A). It complied with the Declaration of Helsinki, and was approved by the ethics committee (EA2/150/11) of Charité – University Medicine, Berlin. The home care study was funded by the National Association of Statutory Health Insurance Funds of Germany (GKV-27.03.2017). This study complied with the Declaration of Helsinki, and was approved by the ethics committee (EA1/368/14) of Charité – University Medicine, Berlin. The sponsors had no role in the design, methods, data collection, analysis, or preparation of this manuscript. The authors declare no conflicts of interest.

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17

Table 1 Sociodemographic and Clinical Characteristics Nursing Home

Home Care

n

n

mean [95% CI] or (%) 83.33 [81.98–84.68]

mean [95% CI] or (%)

Age in years

137

205

81.57 [80.54–82.60]

Sex

137

Female

99

72.30%

141

68.78%

Male

38

27.74%

64

31.22%

Partnership

136

Yes

16

11.77%

48

23.42%

No

120

88.24%

157

76.58%

Level of Care

133

Minor level

102

74.45%

147

71.71%

Serious level

31

22.63%

53

25.85%

Functional Performance

137#

High

21

15.33%

106

51.71%

Low

116

84.67%

98

47.81%

MMSE

131

23.92 [23.30–24.55]

205

26.21 [25.85–26.57]

BMI

132

27.44 [26.54–28.35]

205

31.81 [29.83–33.80]

BI

136

61.58 [58.21–64.95]

204

77.60 [74.95–80.24]

Number of Medication

137

9.33 [8.70–9.95]

204

11.05 [10.42–11.68]

PMASD

122

58.50 [53.50–63.49]

200

45.57 [42.41–48.72]

205

205

200

204§

n = number; % = percent; CI = confidence interval; #Timed-up & go Test used; §Martin Vigorimeter® used; MMSE = Mini Mental Status Examination; BMI = Body Mass Index; BI = Barthel Index; PMASD = Pain Medication Appropriateness Scale (German version).

Table 2 Pain Intensity and Pain Interference with Function by Pain-Associated Clusters in the Nursing Home and Home Care Setting Nursing Home (n=137) Relieved (46.72%) (Rel.) Pain Intensity Mean [95% CI]

Impaired (22.63%) (Imp.) Mean [95% CI]

Severe Pain (30.66%) (Sev.) Mean [95% CI]

Test

P-Value

Post Hoc Contrasts#

Pain worst

0.00 [0.00–0.00]

4.74 [4.10–5.38]

6.10 [5.41–6.78]

F(2,134) = 249.8

0.000

0.000

Pain least

0.00 [0.00–0.00]

1.61 [1.05–2.18]

2.31 [1.67–2.95]

F(2,134) = 40.6

0.000

Imp.
Pain average

0.00 [0.00–0.00]

3.32 [2.78–3.86]

4.14 [3.55–4.73]

F(2,134) = 159.0

0.000

Imp.
Pain right now

0.00 [0.00–0.00]

1.87 [1.09–2.65]

3.36 [2.60–4.12]

F(2,134) = 51.8

0.000

Imp.
Interference with Function Activity

0.00 [0.00–0.00]

1.90 [1.13–2.67]

6.29 [5.58–6.99]

F(2,134) = 198.3

0.000

0.000

Mood

0.00 [0.00–0.00]

1.53 [0.50–1.62]

4.48 [3.49–5.46]

F(2,134) = 72.3

0.000

Rel.
Walking

0.00 [0.00–0.00]

2.32 [1.38–3.27]

6.95 [6.08–7.82]

F(2,134) = 159.5

0.000

0.000

The ability to cope […]a 0.00 [0.00–0.00]

1.39 [0.73–2.05]

6.45 [5.69–7.21]

F(2,134) = 214.7

0.000

0.000

Relations with others

0.00 [0.00–0.00]

0.16 [-0.05–0.38]

2.29 [1.50–3.07]

F(2,134) = 36.1

0.000

Rel.
Sleep

0.00 [0.00–0.00]

1.42 [0.63–2.20]

3.07 [2.09–4.05]

F(2,134) = 29.8

0.000

Rel.
Enjoyment

0.00 [0.00–0.00]

0.68 [0.16–1.19]

3.88 [2.94–4.82]

F(2,134) = 61.8

0.000

Rel.
Home Care (n=205)

Relieved (11.71%) (Rel.) Mean [95% CI]

Impaired (33.66%) (Imp.) Mean [95% CI]

Severe Pain (54.63%) (Sev.) Mean [95% CI]

Test

P-Value

Post Hoc Contrasts#

Pain Intensity

Pain worst

0.00 [0.00–0.00]

6.06 [5.58-6.54]

7.83 [7.49-8.16]

F(2,202) = 202.7

0.000

0.000

Pain least

0.00 [0.00–0.00]

2.42 [2.02-2.82]

3.74 [3.29-4.20]

F(2,202) = 49.5

0.000

0.000

Pain average

0.00 [0.00–0.00]

4.59 [4.20-4.98]

6.03 [5.67-6.39]

F(2,202) = 145.2

0.000

0.000

Pain right now

0.00 [0.00–0.00]

2.72 [2.19-3.26]

4.67 [4.10-5.24]

F(2,202) = 39.0

0.000

0.000

Interference with Function Activity

0.00 [0.00–0.00]

4.93 [4.39-5.47]

7.70 [7.31-8.10]

F(2,202) = 164.1

0.000

0.000

Mood

0.00 [0.00–0.00]

3.05 [2.55-3.54]

7.27 [6.83-7.70]

F(2,202) = 129.9

0.000

0.000

Walking

0.00 [0.00–0.00]

5.49 [4.84-6.14]

8.06 [7.65-8.47]

F(2,202) = 151.8

0.000

0.000

The ability to cope […]a 0.00 [0.00–0.00]

4.71 [4.09-5.33]

7.80 [7.43-8.16]

F(2,202) = 145.1

0.000

0.000

Relations with others

0.00 [0.00–0.00]

1.94 [1.40-2.48]

5.39 [4.76-6.01]

F(2,202) = 44.7

0.000

Rel.
Sleep

0.00 [0.00–0.00]

2.94 [2.32-3.56]

5.16 [4.53-5.79]

F(2,202) = 63.5

0.000

Rel.
Enjoyment

0.00 [0.00–0.00]

2.47 [1.99-2.95]

7.12 [6.67-7.58]

F(2,202) = 124.0

0.000

0.000 #

n = number; % = percent; CI = confidence interval; a The ability to cope mentally and physically with daily stressors, events, and activities. = Post hoc tests revealed significant differences between each cluster combination on level P = 0.000; exceptions are present exactly.

Table 3 Characteristics of Nursing Home Residents and Older Adults Receiving Home Care by Pain-Associated Clusters Nursing Home Relieved

Impaired

Severe Pain

Test

P-Value

Post Hoc Contrasts

F(2,134) = 0.187

0.830

>0.05

χ² = 2.817

0.245

-

χ² = 3.166

0.205

-

χ² = 0.333

0.846

-

χ² = 3.595

0.166

-

mean [95% CI] mean [95% CI] mean [95% CI]

Age in years

n

or (%)

or (%)

or (%)

137

83.69

82.61

83.31

[81.60–85.78]

[79.79–85.44]

[80.90–85.72]

Sex

137

Female

99

65.63%

80.65%

76.19%

Male

38

34.38%

19.36%

23.81%

Partnership

136

Yes

16

7.94%

9.68%

19.05%

No

120

92.06%

90.32%

80.95%

Level of Care

133

Minor level

102

74.61%

77.42%

79.49%

Serious level

31

25.40%

22.58%

20.51%

Functional Performance High

137# 17.19%

22.58%

7.14%

Low MMSE

BMI

BI

131

132

136

Number of Medication 137

PMASD

122

Home care

82.81%

77.42%

92.86%

23.23

24.52

24.54

[22.25–24.21]

[23.30–25.74]

[23.45–25.62]

26.64

26.38

29.35

[25.47–27.81]

[24.45–28.30]

[27.52–31.18]

58.36

69.19

60.85

[53.23–63.49]

[64.16–74.23]

[54.04–67.67]

8.94

9.35

9.90

[8.03–9.85]

[7.90–10.81]

[8.78–11.02]

72.02

45.57

52.26

[64.89–79.16]

[35.36–55.78]

[44.51–60.00]

Relieved

Impaired

Severe Pain

F(2,128) = 2.157

0.120

> 0.05

F(2,129) = 4.274

0.016

Rel.
F(2,133) = 3.246

0.042

Rel.
F(2,134) = 0.867

0.423

> 0.05

F(2,119) = 12.007

0.000

Rel.>Imp., 0.000 Rel.>Tor., 0.001

Test

P-Value

Post Hoc Contrasts#

F(2,202) = 0.143

0.866

> 0.05

χ² = 0.070

0.966

-

χ² = 3.728

0.155

-

mean [95% CI] mean [95% CI] mean [95% CI]

Age in years

n

or (%)

or (%)

or (%)

205

82.13

81.75

81.33

[78.37–85.88]

[80.07–83.44]

[79.93–82.73]

Sex

205

Female

141

66.67%

69.57%

68.75%

Male

64

33.33%

30.44%

31.25%

Partnership

205

Yes

48

8.33%

27.54%

24.11%

No

157

91.67%

72.46%

75.89%

Level of Care

200

Minor level

147

66.67%

73.44%

75.00%

Serious level

53

33.33%

26.56%

25.00%

χ² = 0.705

0.703

-

0.000

χ² = 17.014

0.000

-

0.095

F(2,202) = 2.377

0.095

> 0.05

0.857

F(2,202) = 0.154

0.857

> 0.05

0.073

F(2,202) = 2.647

0.073

> 0.05

0.264

F(2,201) = 1.339

0.264

> 0.05

0.418

F(2,197) = 0.876

0.418

> 0.05

Functional Performance

200§

High

56.52%

72.06%

40.37%

Low

43.47%

27.94%

59.63%

25.13

26.39

26.33

[23.68–26.57]

[25.79–27.00]

[25.87–26.79]

31.11

30.15

32.98

[27.95–34.27]

[23.97–36.34]

29.78–36.17]

71.46

81.23

76.67

[60.87–82.05]

[77.45–85.01]

[73.02–80.31]

10.29

10.55

11.52

[8.94–11.64]

[9.46–11.64]

[10.62–12.43]

49.79

43.04

46.25

[39.64–59.93]

[38.20–47.87]

[41.71–50.80]

MMSE

BMI

BI

205

205

204

Number of Medication 204

PMASD

200

n = Number; % = percent; CI = confidence interval; #Timed-up & go Test used; §Martin Vigorimeter® used; MMSE = Mini Mental Status Examination; BMI = Body Mass Index; BI = Barthel Index; PMASD = Pain Medication Appropriateness Scale (German version).

Table 4 Factors Associated with Clusters - Comparing Impaired and Severe Pain Clusters to Pain-Relieved Cluster in Nursing Homes and Home Care Setting Nursing Home (n=110) Impaired

Home Care (n=194) P

Severe Pain

P

Nagelkerke R² = 33.2

P

Severe Pain

P

Nagelkerke R² = 11.8

OR [95% CI]

BI

Impaired

OR [95% CI]

OR [95% CI]

OR [95% CI]

1.04 [1.01,1.08]

0.020

1.02 [1.00,1.05]

0.103

1.03 [1.00,1.05]

0.026

1.02 [1.00,1.04]

0.051

High

0.71 [0.16,3.20]#

0.659

0.08 [0.01,0.81]#

0.033

1.78 [0.64,4.89]§

0.267

0.51 [0.20,1.30]§

0.159

Low

Ref.

PMASD

0.96 [0.94,0.98]

Functional Performance

Ref. 0.000

0.97 [0.95,0.99]

0.003

Ref.

Ref.

--

--

n = Number; OR = odds ration; % = percent; CI = confidence interval; BI = Barthel Index; #Timed-up & go Test used; §Martin Vigorimeter® used; Ref. = reference group; PMASD = Pain Medication Appropriateness Scale.

Fig. 1: Dendogram created using Ward’s method and the squared Euclidean distance using the items of the BPI-NHR28 for the nursing home sample (n = 137)

Fig. 2: Dendogram created using Ward’s method and the squared Euclidean distance using the items of the BPI-NHR28 for the home care sample (n = 205)