Journal of Gerontological Nursing

Feature Article 

The Association Between Chronic Condition Symptoms and Treatment Burden in a Skilled Nursing Population

Nathanial Schreiner, PhD, MBA, RN; Stacie Schreiner, MSN, RN; Barbara Daly, PhD, RN, FAAN

Abstract

The purpose of the current study was to determine the relationship between chronic condition symptoms and treatment burden in older adults transitioning from skilled nursing facilities to home. Treatment burden is defined as the burden associated with adhering to a prescribed chronic condition self-management regimen. Analysis of correlations between chronic condition symptoms and treatment burden revealed that symptoms and treatment burden are positively correlated (p < 0.05). Multivariate analysis (adjusted R2 = 0.40, F[10, 63] = 5.96, p < 0.001), controlling for other known antecedents of treatment burden, demonstrated that fatigue (standardized beta coefficient = 0.47, p < 0.001) predicted higher levels of treatment burden. Post hoc analysis revealed caregiver presence partially mediated the effect of fatigue on treatment burden, decreasing treatment burden during transition. Findings support existing transitional care literature suggesting that clinical assessment, including symptom screening, treatment of symptoms, and/or intervention reducing the impact of symptoms on patients' health and well-being, may lower treatment burden, thus improving self-management adherence. [Journal of Gerontological Nursing, 44(12), 45–52.]

Abstract

The purpose of the current study was to determine the relationship between chronic condition symptoms and treatment burden in older adults transitioning from skilled nursing facilities to home. Treatment burden is defined as the burden associated with adhering to a prescribed chronic condition self-management regimen. Analysis of correlations between chronic condition symptoms and treatment burden revealed that symptoms and treatment burden are positively correlated (p < 0.05). Multivariate analysis (adjusted R2 = 0.40, F[10, 63] = 5.96, p < 0.001), controlling for other known antecedents of treatment burden, demonstrated that fatigue (standardized beta coefficient = 0.47, p < 0.001) predicted higher levels of treatment burden. Post hoc analysis revealed caregiver presence partially mediated the effect of fatigue on treatment burden, decreasing treatment burden during transition. Findings support existing transitional care literature suggesting that clinical assessment, including symptom screening, treatment of symptoms, and/or intervention reducing the impact of symptoms on patients' health and well-being, may lower treatment burden, thus improving self-management adherence. [Journal of Gerontological Nursing, 44(12), 45–52.]

In 2015, 1.7 million older adults received post-acute care in skilled nursing facilities (SNFs) (Crosson, Christianson, & Miller, 2017). The health of these individuals is characterized by chronic multi-morbidities requiring complex medical care in acute settings, and often rehabilitation in a SNF before returning to a home setting (Centers for Medicare & Medicaid Services [CMS], 2012; Mor, Intrator, Feng, & Grabowski, 2010; Toles, Colón-Emeric, Naylor, Barroso, & Anderson, 2016). Due to complex multi-morbidities, post-discharge health trajectories in this population are often poor, with 20% of individuals discharged from SNF care readmitted to an acute care setting or seen in an emergency department (ED) within 30 days of discharge (Toles et al., 2014).

Research is emerging designed to improve poor health outcomes in older adults receiving post-acute care in SNFs (Toles, Colón-Emeric, Naylor, Asafu-Adjei, & Hanson, 2017; Toles et al., 2016), borrowing concepts and methods from inpatient transitional care research (Berkowitz et al., 2013; Hirschman, Shaid, McCauley, Pauly, & Naylor, 2015). These transitional care studies focus on coordination and continuity of care by assessing and meeting patients' discharge care needs to improve health outcomes and reduce readmission rates. An important but sometimes overlooked component of improving health outcomes in this population is the need for self-management of chronic conditions. Poor self-management of chronic conditions can result in an acute exacerbation of the condition or increase in symptoms, thus delaying recovery from the acute illness. In addition, ineffective self-management of chronic conditions has been shown to be a major factor in read-mission and ED use within 30 days of discharge (RTI International, 2016).

High levels of treatment burden are associated with ineffective self-management of chronic conditions and contribute to poor health outcomes in older adults after discharge from a SNF. Treatment burden is defined as the burden associated with adherence to a prescribed chronic condition self-management regime (Sav et al., 2013; Tran et al., 2012). Treatment burden has been found to increase with the number of chronic conditions, or multiple chronic conditions (MCC), diagnosed in a patient (Sav, Salehi, Mair, & McMillan, 2017; Schreiner & Daly, 2018). As the number of MCC increases, so does the risk for polypharmacy, including the number, frequency, and cumulative side effects of medications, as well as the number of collaborating physicians and health care providers associated with the individual's care, including associated appointments and necessary follow ups. As treatment burden increases, so does the risk of the individual's partial or full non-adherence to the prescribed treatment regimen (Demain et al., 2015; Sav et al., 2013; Sav et al., 2016). Non-adherence to a prescribed treatment regimen can result in ineffective self-management of MCC, leading to poor health outcomes such as exacerbation of a chronic condition or inadequate resolution of an acute health condition (Kymes, Pierce, Girdish, & Matlin, 2016; Viswanathan et al., 2012).

Current literature suggests many antecedent factors are associated with treatment burden. Chronic condition factors, such as number of chronic conditions (Sav et al., 2017; Schreiner & Daly, 2018), severity of the chronic condition (Sav et al., 2016; Schreiner & Daly, 2018), and diagnosis of diabetes or other endocrine-related conditions (Sav et al., 2016), increase the level of treatment burden experienced by individuals diagnosed with chronic conditions. The care-related variables of receiving home care after discharge from a SNF (Schreiner & Daly, 2018) and having a caregiver assist with self-management of chronic conditions (Sav et al., 2016) were associated with a higher level of treatment burden, although Schreiner and Daly (2018) showed that the presence of a caregiver who assisted with self-management decreased treatment burden. Disease-related symptoms, such as pain, have also been identified as antecedent factors affecting treatment burden, but this relationship has not been quantitatively measured (Sav et al., 2016). In addition, younger individuals experience higher treatment burden (Sav et al., 2016). Schreiner and Daly (2018) and Sav et al. (2016) answered important questions about which conceptually related variables are associated with treatment burden in various populations diagnosed with chronic conditions; however, many potential antecedent factors associated with treatment burden remain untested.

A vast body of literature addresses symptoms associated with chronic conditions. Symptoms such as impaired physical functioning (Stenholm et al., 2015), depression (Read, Sharpe, Modini, & Dear, 2017), anxiety (Gould, O'Hara, Goldstein, & Beaudreau, 2016), fatigue (Connolly, O'Toole, Redmond, & Smith, 2013), and pain (Scherer et al., 2016) are associated with chronic conditions. Although the relationship between chronic condition symptoms and treatment burden has not yet been examined, the existence of this relationship is highly probable. For example, an individual with impaired physical functioning may find it burdensome to accomplish basic chronic condition–related self-management tasks, such as adhering to an exercise regimen or managing fluid and nutritional intake. Confirmation of the hypothesis that chronic condition symptoms are related to treatment burden will have implications for care of individuals transitioning from SNFs to home. Measuring and identifying this potential antecedent relationship can inform suggestions for potential interventions to effectively reduce treatment burden in this population. For instance, if a clinician can adequately reduce symptoms such as pain associated with chronic conditions, he/she may decrease the amount of treatment burden experienced by the patient. By decreasing treatment burden, that patient may be more adherent to the prescribed treatment regimen, thus effectively managing the chronic condition, improving health outcomes, and reducing 30-day readmission and ED visits.

Purpose

The purpose of the current study was to examine the relationship between chronic condition symptoms and treatment burden in older adults transitioning from a SNF to home. Researchers hypothesized that chronic condition symptoms were positively correlated with treatment burden. The aim of the current study was to examine the relationship between chronic condition symptoms (i.e., physical function, fatigue, depression, anxiety, pain interference, and pain intensity) and treatment burden, controlling for known antecedent factors (i.e., severity of MCC, number of home care visits, and presence/absence of a caregiver).

Method

Design and Sample

The current study was conducted using a prospective correlational, cross-sectional design that examined the relationship between chronic condition–related symptoms and treatment burden 30 days after participants were discharged from a SNF to home. The study was conducted over a period of approximately 5 months, from September 2016 through January 2017. Participants included 74 adult men and women recruited via convenience sampling. Participants were identified and approached for consent into the study if they were: (a) ages ≥18, and (b) diagnosed with two or more chronic conditions as defined by the CMS (2017). Participants were excluded from enrollment in the study if they scored <8 points on the Brief Interview of Mental Status examination, indicating severe cognitive impairment (Chodosh et al., 2008).

Setting and Ethical Approval

Researchers conducted the study at a 100-bed, privately owned SNF in Northeast Ohio. The participating SNF's population was representative of other Northeast Ohio–area SNFs, comprising a medically complex, multimorbid patient population discharged from an acute hospital setting, with individuals who required additional skilled care, such as physical/occupational therapy or complex wound care prior to discharge to a home setting. Researchers obtained Institutional Review Board approval from the respective university prior to identification, recruitment, and enrollment of participants.

Data Collection

Facility staff identified potential participants for the study and provided a list to the principal investigator (PI; N.S.). The PI approached, consented, and collected data from all participants. Data were collected in the SNF at enrollment and via telephone 30 days after the participant was discharged home. The PI was solely responsible for all data collection from all participants. Data collection in the facility included demographic questionnaires. Data collection at home via telephone was approximately 20 minutes and included the Patient-Reported Outcomes Measurement Information System (PROMIS®) short-form questionnaires for physical function, fatigue, depression, anxiety, pain interference, and pain intensity (Cella et al., 2010; Deyo et al., 2015) and the Treatment Burden Questionnaire-15 (TBQ-15; Tran et al., 2014). Researchers also collected measures of other known antecedent treatment burden factors (i.e., severity of MCC, number of home care visits, presence/absence of a caregiver) at the 30-day point to control for the influence of these factors in statistical analysis (Sav et al., 2017; Schreiner & Daly, 2018).

Prior to the study, researchers conducted a sensitivity analysis using G*Power software (Faul, Erdfelder, Buchner, & Lang, 2009) for the proposed multivariate analysis. Inputting parameters of an alpha of 0.05, a power of 0.8, 10 independent variables, and a sample size of 74 participants, a medium effect size of 0.25 was needed to detect statistical significance.

Measures

Treatment Burden. The previously validated TBQ-15 was used to measure participant treatment burden (Tran et al., 2014). Each question posed by the questionnaire inquires about burden related to performing specific aspects of self-management, such as medication administration, dietary changes/restrictions, or arranging/getting to scheduled physician appointments. The questionnaire prompts the participant to rank the level of treatment burden for each of the 15 questions, with responses ranging from 0 (no burden) to 10 (very high burden). After all questions are answered, scores are summed and range from 0 to 150, with higher scores indicating greater treatment burden. Construct validity has been evaluated via correlations with medication adherence and quality of life, using Morisky's Medication Adherence Scale and the PatientsLikeMe Quality of Life Scale, respectively (Tran et al., 2014). Compared to participants with moderate or high medication adherence, participants with low medication adherence had significantly higher levels of treatment burden (mean [SD] TBQ score: 61.8 [30.5] vs. 37.7 [27.5]; p < 0.0001). Global treatment burden was inversely correlated with quality of life; individuals with high treatment burden reported a lower quality of life (r = −0.50; p < 0.0001). Reliability for the TBQ-15 was determined via a 1-month test–retest with an intra-class correlation of 0.77 (Tran et al., 2014).

Symptoms. Researchers used the four-item, short-form PROMIS instrument (Cella et al., 2010) to measure participant physical function, fatigue, depression, anxiety, pain interference, and pain intensity. PROMIS measures are a set of standardized, subjective instruments that evaluate physical, mental, and social health in adults. Researchers chose to use the four-item PROMIS instrument to measure chronic condition symptoms, as these measures are widely used in scientific research and display strong psychometric properties. In addition, using the four-item variant of the PROMIS instrument reduces survey fatigue in participants. For each variable, scores range from 1 (low severity) to 5 (high severity); scores are summed, resulting in a total range of 4 to 20, with higher scores indicating greater symptom severity. Unlike measurements for other symptoms, researchers used a single-item scale to measure pain intensity, which was scored on a 0 to 10 scale, with higher scores indicating greater pain. Construct validity of the PROMIS scale has been established through significant positive correlations, with increasing claims for workers' compensation, falls, and Pain Catastrophizing Scale (Deyo et al., 2015). Reliability has been tested via internal consistency between items, with Cronbach's alphas ranging from 0.85 (anxiety) to 0.94 (fatigue) (Deyo et al., 2015) and 0.89 (anxiety) to 0.93 (depression) (Kroenke, Yu, Wu, Kean, & Monahan, 2014).

Severity of MCC. The severity of MCC was measured using self-report questions for each diagnosed CMS– defined chronic condition: “What is the impact of the chronic condition (0 [no impact], 10 [high impact]) on your health and overall well-being?” Researchers summed the total score for all chronic conditions and treated the summed score as a continuous independent control variable for analysis. This index was developed by the PI to capture the impact of all chronic conditions on a participant's health and well-being, as not all chronic conditions are equal in severity.

Home Care Visits. Home care visits were defined as provider-ordered in-home nursing care following discharge to home. Orders for home care were verified via the facility's electronic medical record system. During data collection, researchers asked the participant how many visits he/she had during the 30-day transition period home. The total number of home care visits indicated by the participant was treated as a continuous independent control variable for statistical analysis.

Caregiver. Caregiver was defined as an individual (e.g., spouse, family member, friend, employee) who provided assistance in self-management of MCCs after the participant returned home from the SNF. For data collection and analysis, researchers treated this independent variable as dichotomous (i.e., no caregiver[s] assisting with self-management or caregiver[s] assisting with self-management).

Demographics

The PI collected patient demographics during the baseline visit via interview and confirmed responses with an electronic medical record review. Age, gender, race, education, income, and number of MCC were collected.

Data Analysis

Data were analyzed using SPSS version 24. Researchers evaluated frequency distributions and univariate statistics to ensure data met statistical assumptions of the planned analyses. Statistical assumptions of linear regression and multi-collinearity were tested before conducting multivariate statistical analysis. Researchers set a priori statistical significance at 0.05.

Reporting of descriptive, univariate statistics included: (a) range, mean, and standard deviation of continuous variables, and (b) number and percentage of categorical and dichotomous variables. Bivariate correlational analysis between chronic condition symptoms (i.e., impaired physical functioning, depression, anxiety, fatigue, pain interference, and pain intensity) and treatment burden was conducted using Pearson's r test reporting r statistics and p values. Multivariate analysis of independent variables (i.e., impaired physical functioning, depression, anxiety, fatigue, pain interference, and pain intensity controlling for the number of MCC) on the dependent variable of treatment burden was conducted using linear regression reporting the overall model fit (F test, degrees of freedom, adjusted R2, p value), and unstandardized/standardized beta coefficients, t score, and p value for each independent variable analyzed in the model. The total number of MCC was significantly associated with treatment burden in a previous analysis by the current authors using this dataset; thus, the relationship between the number of MCC and treatment burden was controlled for by adding the summed total of MCC in the analysis as a continuous variable in multivariate analysis.

Results

Study demographics are displayed in Table 1. No significant relationships between age, gender, race, education, income, and treatment burden were found in the sample; thus, researchers did not control for these characteristics in multivariate analysis.

Study Sample Variable Univariate Statistics (N = 74)

Table 1:

Study Sample Variable Univariate Statistics (N = 74)

Researchers first examined the relationship between chronic condition symptoms and treatment burden by analyzing bivariate correlations (Table 2). All chronic condition symptoms were positively associated with treatment burden (i.e., the more severe the reported symptom, the higher the level of treatment burden). Fatigue (r = 0.64, p ≤ 0.001) and pain interference (r = 0.38, p ≤ 0.001) were symptoms most strongly correlated with treatment burden 30 days following discharge from SNF to home.

Correlations Between Chronic Condition Symptoms and Treatment Burden

Table 2:

Correlations Between Chronic Condition Symptoms and Treatment Burden

In a multivariate analysis (Table 3) including the six chronic condition symptoms as independent variables, 39% of variance in treatment burden was explained (adjusted R2= 0.39, F[6,67] = 8.64, p < 0.001 with the symptom of fatigue [standardized beta coefficient = 0.55; p < 0.001]) predicting treatment burden. Researchers then controlled for other known antecedent factors of treatment burden (number of MCC, severity of MCC, presence of a caregiver, and number of home care visits). In multivariate analysis, the model was significant (adjusted R2 = 0.40, F[10,63] = 5.96, p < 0.001). Fatigue still predicted treatment burden (standardized beta coefficient = 0.47; p < 0.001). The presence of a caregiver assisting with chronic condition self-management tasks also remained significant in multivariate analysis (standardized beta coefficient = −0.21; p = 0.04).

Multivariate Analysis of Independent Chronic Condition Symptoms Variables

Table 3:

Multivariate Analysis of Independent Chronic Condition Symptoms Variables

Post hoc analysis of findings revealed that the presence of a caregiver in the multivariate model partially mediated the relationship between fatigue and treatment burden (standardized beta coefficient = 0.47; p < 0.001), reducing the impact of fatigue on treatment burden during the 30-day transition period. When comparing individuals with caregivers and individuals without caregivers, researchers found that fatigue severity was lower in participants with caregivers (11.53) versus those without caregivers (14.04). In addition, fatigue was not a significant predictor of treatment burden in individuals with caregivers (standardized beta coefficient = 0.33, p = 0.08) compared to participants without caregivers (standardized beta coefficient = 0.63, p < 0.001).

Discussion

Study results confirmed the associations between chronic condition symptoms and treatment burden, with more severe symptoms directly correlating with higher levels of treatment burden during the 30-day transition period. In addition, results of the multivariate analysis demonstrated that higher levels of fatigue predicted greater treatment burden and the presence of a caregiver partially mediated the effect of fatigue on treatment burden. Initial findings support current clinical practice of assessment, education, and allocation of resources outlined in current transitional care literature; however, further testing is needed to determine whether these actions are effective in decreasing treatment burden and improving clinical outcomes.

For SNF providers (e.g., physicians, nurse practitioners, nurses, social workers), understanding that a patient who displays high levels of MCC–associated symptoms is at risk for potential non-adherence to part or all needed management of MCC when discharged home allows the provider to mitigate these effects prior to discharge. Addressing MCC–related symptoms should be a priority for providers during a patient's SNF stay, and effective interventions, pharmacological and nonpharmacological, should be incorporated into the patient's discharge plan. Toles et al. (2016) demonstrated that individualized, early transitional care planning improves health outcomes in patients discharged from SNF to home. Improved pain management, mental health assessments, and additional in-home resources can increase the probability of effective self-management. For example, more frequent and/or longer duration of home care, in-home provider follow up from a physician or nurse practitioner, or community resources (e.g., assistance with obtaining medical transport to appointments) may help ensure that patients with significant MCC symptoms have the in-home assistance and support needed for effective management of MCC during the transition period home.

The multivariate analysis finding of fatigue predicting treatment burden has substantial implications for clinical practice. Studies have found that a decline in activities of daily living (ADL), a decrease in mobility, or an increase in care needs increase the risk of readmission in patient populations discharged from hospital to home (Berian, Mohanty, Ko, Rosenthal, & Robinson, 2016; Hoyer et al., 2014). A priority of SNF care is rehabilitation through physical therapy and/or occupational therapy modalities. However, although a patient may be able to meet therapy goals during his/her SNF stay, the increase in fatigue due to increased/unforeseen demands after returning home, when the readily available services of the SNF are no longer available, may impair the patient's ability to adequately self-manage chronic condition(s), thereby increasing risk of adverse outcomes. Examining the impact of patient fatigue on mobility and ADL performance after transition home may provide further direction for approaches to improve health outcomes during the transition period.

The finding of the positive influence of caregiver presence on decreasing transitional fatigue and mediating the effect of this fatigue on treatment burden highlights the importance of SNF providers assessing availability of caregivers and interfacing with these individuals prior to discharge to home to include them in the plan of care. This finding also demonstrates that the presence of the caregiver assisting in MCC self-management is an essential mechanism for reducing treatment burden, even when controlling for other associated factors. Previous studies of treatment burden have highlighted the role of caregivers in assisting with self-management of MCC (Koch, Wakefield, & Wakefield, 2015) and decreasing treatment burden in the MCC population (Sav et al., 2013; Schreiner & Daly, 2018).

The findings also have implications for transitional care/home care providers. Studies have shown that home health visits within 1 week of discharge can reduce the risk for readmission after SNF discharge (Carnahan, Slaven, Callahan, Tu, & Torke, 2017). The results suggest that transitional/home care providers should specifically assess severity of MCC symptoms and treatment burden. This assessment could identify additional resources needed during the transitional period to improve MCC self-management and reduce the risk for readmission.

Results also point to the need for further examination of the relationship between fatigue and pain and impaired physical function in the role of chronic conditions self-management. For instance, higher levels of fatigue and pain are associated with impaired physical functioning in a multitude of chronic conditions (Cook et al., 2016), and poor physical function is related to risk of readmission after discharge to home (Falvey et al., 2016). The current researchers found that fatigue, pain, and impaired physical function are positively associated with treatment burden; thus, as symptom severity increases, the level of treatment burden also increases. In addition, these three symptoms may have deleterious effects on other aspects of self-management, such as self-efficacy. Further exploration of these variables in future studies may provide valuable insight regarding ways to improve chronic condition self-management through improved symptom management.

Limitations

The current study was not without limitations. Researchers had a limited sample size and thus were underpowered for multivariate analysis, although the effect size in the sample was large enough to detect statistical significance. The cross-sectional design and convenience sampling in a single SNF limited generalizability to disparate populations, particularly individuals with lower incomes. In addition, the cross-sectional design limited researchers' ability to test for causation; a longitudinal design with a cross-legged structural equation model analysis should be incorporated in further testing of findings. Use of additional, objective measures of functioning (e.g., 6-minute walk tests, ADL/instrumental ADL scoring, SNF physical therapy scores) should be correlated with subjective reports of physical functioning and be tested as independent variables in future models exploring MCC symptoms and treatment burden. Comparing levels of fatigue pre- and post-discharge and related measures of physical functioning/mobility should also be addressed in future study designs.

Conclusion

The current study addressed a substantial gap in the treatment burden/transitional care literature regarding the association of chronic condition–related symptoms and treatment burden during the transition of older adults from SNF to home. Findings demonstrate that worsening condition symptom severity is associated with higher levels of treatment burden, which may lead to patient non-adherence to the prescribed treatment plan, ineffective self-management of MCC, and poor health outcomes, such as high 30-day readmission rates. Findings indicate an opportunity for clinicians to screen for MCC symptoms and treatment burden pre– and post–SNF discharge to home. Interventions such as adjustment to medications, patient/caregiver education, or allocation of additional resources to reduce symptom severity can then be implemented. By reducing symptom severity, clinicians may reduce treatment burden and improve adherence to the prescribed treatment/transitional care plan, thus improving health outcomes.

References

  • Berian, J.R., Mohanty, S., Ko, C.Y., Rosenthal, R.A. & Robinson, T.N. (2016). Association of loss of independence with readmission and death after discharge in older patients after surgical procedures. JAMA Surgery, 151, 1–7. doi:10.1001/jamasurg.2016.1689 [CrossRef]
  • Berkowitz, R.E., Fang, Z., Helfand, B.K., Jones, R.N., Schreiber, R. & Paasche-Orlow, M.K. (2013). Project ReEngineered Discharge (RED) lowers hospital readmissions of patients discharged from a skilled nursing facility. Journal of the American Medical Directors Association, 14, 736–740. doi:10.1016/j.jamda.2013.03.004 [CrossRef]
  • Carnahan, J.L., Slaven, J.E., Callahan, C.M., Tu, W. & Torke, A.M. (2017). Transitions from skilled nursing facility to home: The relationship of early outpatient care to hospital readmission. Journal of the American Medical Directors Association, 18, 853–859. doi:10.1016/j.jamda.2017.05.007 [CrossRef]
  • Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S. & Hays, R. (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63, 1179–1194. doi:10.1016/j.jclinepi.2010.04.011 [CrossRef]
  • Centers for Medicare & Medicaid Services. (2012). Chronic conditions among Medicare beneficiaries: Chart book: 2012 edition. Retrieved from http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Chronic-Conditions/Downloads/2012Chartbook.pdf
  • Centers for Medicare & Medicaid Services. (2017). Chronic conditions data warehouse. Retrieved from https://www.ccwdata.org/web/guest/condition-categories
  • Chodosh, J., Edelen, M.O., Buchanan, J.L., Yosef, J.A., Ouslander, J.G., Berlowitz, D.R. & Saliba, D. (2008). Nursing home assessment of cognitive impairment: Development and testing of a brief instrument of mental status. Journal of the American Geriatrics Society, 56, 2069–2075. doi:10.1111/j.1532-5415.2008.01944.x [CrossRef]
  • Connolly, D., O'Toole, L., Redmond, P. & Smith, S.M. (2013). Managing fatigue in patients with chronic conditions in primary care. Family Practice, 30, 123–124. doi:10.1093/fampra/cmt005 [CrossRef]
  • Cook, K.F., Jensen, S.E., Schalet, B.D., Beaumont, J.L., Amtmann, D., Czajkowski, S. & Cella, D. (2016). PROMIS measures of pain, fatigue, negative affect, physical function, and social function demonstrated clinical validity across a range of chronic conditions. Journal of Clinical Epidemiology, 73, 89–102. doi:10.1016/j.jclinepi.2015.08.038 [CrossRef]
  • Crosson, F., Christianson, J. & Miller, M. (2017, March). Report to the Congress: Medicare payment policy. Retrieved from http://www.medpac.gov/docs/default-source/reports/mar17_medpac_ch8.pdf?sfvrsn=0
  • Demain, S., Gonçalves, A.C., Areia, C., Oliveira, R., Marcos, A.J., Marques, A. & Hunt, K. (2015). Living with, managing and minimising treatment burden in long term conditions: A systematic review of qualitative research. PLoS One, 10(5), e0125457. doi:10.1371/journal.pone.0125457 [CrossRef]
  • Deyo, R.A., Ramsey, K., Buckley, D.I., Michaels, L., Kobus, A., Eckstrom, E. & Morris, C. (2015). Performance of a Patient Reported Outcomes Measurement Information System (PROMIS) short form in older adults with chronic musculoskeletal pain. Pain Medicine, 17, 312–324. doi:10.1093/pm/pnv046 [CrossRef]
  • Falvey, J.R., Burke, R.E., Malone, D., Ridgeway, K.J., McManus, B.M. & Stevens-Lapsley, J.E. (2016). Role of physical therapists in reducing hospital readmissions: Optimizing outcomes for older adults during care transitions from hospital to community. Physical Therapy, 96, 1125–1134. doi:10.2522/ptj.20150526 [CrossRef]
  • Faul, F., Erdfelder, E., Buchner, A. & Lang, A.G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160. doi:10.3758/BRM.41.4.1149 [CrossRef]
  • Gould, C.E., O'Hara, R., Goldstein, M.K. & Beaudreau, S.A. (2016). Multimorbidity is associated with anxiety in older adults in the Health and Retirement Study. International Journal of Geriatric Psychiatry, 31, 1105–1115. doi:10.1002/gps.4532 [CrossRef]
  • Hirschman, K.B., Shaid, E., McCauley, K., Pauly, M.V. & Naylor, M.D. (2015). Continuity of care: The transitional care model. Online Journal of Issues in Nursing, 20(3), 1.
  • Hoyer, E.H., Needham, D.M., Atanelov, L., Knox, B., Friedman, M. & Brotman, D.J. (2014). Association of impaired functional status at hospital discharge and subsequent rehospitalization. Journal of Hospital Medicine, 9, 277–282. doi:10.1002/jhm.2152 [CrossRef]
  • Koch, G., Wakefield, B.J. & Wakefield, D.S. (2015). Barriers and facilitators to managing multiple chronic conditions: A systematic literature review. Western Journal of Nursing Research, 37, 498–516. doi:10.1177/0193945914549058 [CrossRef]
  • Kroenke, K., Yu, Z., Wu, J., Kean, J. & Monahan, P.O. (2014). Operating characteristics of PROMIS four-item depression and anxiety scales in primary care patients with chronic pain. Pain Medicine, 15, 1892–1901. doi:10.1111/pme.12537 [CrossRef]
  • Kymes, S.M., Pierce, R.L., Girdish, C. & Matlin, O.S. (2016). Association among change in medical costs, level of comorbidity, and change in adherence behavior. American Journal of Managed Care, 22, e295–e301.
  • Mor, V., Intrator, O., Feng, Z. & Grabowski, D.C. (2010). The revolving door of rehospitalization from skilled nursing facilities. Health Affairs, 29, 57–64. doi:10.1377/hlthaff.2009.0629 [CrossRef]
  • Read, J.R., Sharpe, L., Modini, M. & Dear, B.F. (2017). Multimorbidity and depression: A systematic review and meta-analysis. Journal of Affective Disorders, 221, 36–46. doi:10.1016/j.jad.2017.06.009 [CrossRef]
  • RTI International.(2016, July). Measure specifications for measures adopted in the FY2017SNF QRP final rule. Retrieved from https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Downloads/Measure-Specifications-for-FY17-SNFQRP-Final-Rule.pdf
  • Sav, A., King, M.A., Whitty, J.A., Kendall, E., McMillan, S.S., Kelly, F. & Wheller, A.J. (2013). Burden of treatment for chronic illness: A concept analysis and review of the literature. Health Expectations: An International Journal of Public Participation in Health Care and Health Policy, 18, 312–324. doi:10.1111/hex.12046 [CrossRef]
  • Sav, A., Salehi, A., Mair, F.S. & McMillan, S.S. (2017). Measuring the burden of treatment for chronic disease: Implications of a scoping review of the literature. BMC Medical Research Methodology, 17, 140. doi:10.1186/s12874-017-0411-8 [CrossRef]
  • Sav, A., Whitty, J.A., McMillan, S.S., Kendall, E., Kelly, F., King, M.A. & Wheeler, A.J. (2016). Treatment burden and chronic illness: Who is at most risk?The Patient, 9, 559–569. doi:10.1007/s40271-016-0175-y [CrossRef]
  • Scherer, M., Hansen, H., Gensichen, J., Mergenthal, K., Riedel-Heller, S., Weyerer, S. & Schäfer, I. (2016). Association between multimorbidity patterns and chronic pain in elderly primary care patients: A cross-sectional observational study. BMC Family Practice, 17, 68. doi:10.1186/s12875-016-0468-1 [CrossRef]
  • Schreiner, N. & Daly, B. (2018). A pilot study exploring treatment burden in a skilled nursing population. Rehabilitation Nursing. Advance online publication. doi:10.1097/rnj.0000000000000169 [CrossRef]
  • Stenholm, S., Westerlund, H., Head, J., Hyde, M., Kawachi, I., Pentti, J. & Vahtera, J. (2015). Comorbidity and functional trajectories from midlife to old age: The health and retirement study. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 70, 332–338. doi:10.1093/gerona/glu113 [CrossRef]
  • Toles, M., Anderson, R.A., Massing, M., Naylor, M.D., Jackson, E., Peacock-Hinton, S. & Colón-Emeric, C. (2014). Restarting the cycle: Incidence and predictors of first acute care use after nursing home discharge. Journal of the American Geriatrics Society, 62, 79–85. doi:10.1111/jgs.12602 [CrossRef]
  • Toles, M., Colón-Emeric, C., Naylor, M.D., Asafu-Adjei, J. & Hanson, L.C. (2017). Connect-home: Transitional care of skilled nursing facility patients and their caregivers. Journal of the American Geriatrics Society, 65, 2322–2328. doi:10.1111/jgs.15015 [CrossRef]
  • Toles, M., Colón-Emeric, C., Naylor, M.D., Barroso, J. & Anderson, R.A. (2016). Transitional care in skilled nursing facilities: A multiple case study. BMC Health Services Research, 16, 186. doi:10.1186/s12913-016-1427-1 [CrossRef]
  • Tran, V.T., Harrington, M., Montori, V.M., Barnes, C., Wicks, P. & Ravaud, P. (2014). Adaptation and validation of the Treatment Burden Questionnaire (TBQ) in English using an internet platform. BMC Medicine, 12, 109. doi:10.1186/1741-7015-12-109 [CrossRef]
  • Tran, V.-T., Montori, V.M., Eton, D.T., Baruch, D., Falissard, B. & Ravaud, P. (2012). Development and description of measurement properties of an instrument to assess treatment burden among patients with multiple chronic conditions. BMC Medicine, 10(68). doi:10.1186/1741-7015-10-68 [CrossRef]
  • Viswanathan, M, Golin, C.E., Jones, C.D., Ashok, M., Blalock, S.J., Wines, R.C. & Lohr, K.N. (2012). Interventions to improve adherence to self-administered medications for chronic diseases in the United States: A systematic review. Annals of Internal Medicine, 157, 785–795. doi:10.7326/0003-4819-157-11-201212040-00538 [CrossRef]

Study Sample Variable Univariate Statistics (N = 74)

VariableMean (SD) (Range)
Age75.40 (10.26) (48 to 96)
Number of chronic conditions4.30 (1.53) (2 to 8)
Severity of multiple chronic conditions17.65 (12.14) (0 to 50)
Home care visit(s)7.62 (8.48) (0 to 30)
Physical function15.15 (5.07) (4 to 20)
Anxiety8.09 (4.01) (4 to 20)
Depression7.36 (3.77) (4 to 20)
Fatigue12.42 (5.08) (4 to 20)
Pain interference11.57 (5.56) (4 to 20)
Pain intensity5.45 (3.22) (0 to 10)
Treatment burden37.01 (24.45) (0 to 100)
n (%)
Gender
  Female55 (74.3)
  Male19 (25.7)
Race
  White68 (91.9)
  Black6 (8.1)
Educational level
  Completed grades 1 to 1116 (21.6)
  Completed high school26 (35.1)
  Completed some college19 (25.7)
  Completed college/graduate degree13 (17.6)
Income (U.S. dollars)
  ≤5,0002 (2.7)
  5,001 to 10,0005 (6.8)
  10,001 to 25,00058 (78.4)
  25,001 to 50,0006 (8.1)
  >50,0013 (4.1)
Caregiver assisting self-management at home
  Yes48 (64.9)
  No26 (35.1)

Correlations Between Chronic Condition Symptoms and Treatment Burden

Variable123456
1. Treatment burden
2. Physical function0.32**
3. Anxiety0.28**0.08
4. Depression0.29**0.140.55***
5. Fatigue0.64***0.48***0.35**0.29**
6. Pain interference0.38***0.43***0.21*0.19*0.46***
7. Pain intensity0.35**0.30**0.090.100.38***0.65***

Multivariate Analysis of Independent Chronic Condition Symptoms Variables

VariableUnstandardized Beta CoefficientsStandardized Beta Coefficientst Statisticp Value
(Constant)−7.26−0.860.39
Physical function−0.06−0.01−0.120.91
Anxiety0.070.010.100.92
Depression0.700.110.970.34
Fatigue2.660.554.68<0.01
Pain interference0.200.050.340.74
Pain intensity0.780.100.840.40
Authors

Dr. Schreiner is Post-Doctoral Fellow, Frances Payne Bolton School of Nursing, and Dr. Daly is Professor, Case Western Reserve University, and Clinical Ethics, University Hospitals Case Medical Center, Cleveland; and Ms. Schreiner is Program Director, Geriatric Rehabilitation Units, Summa Health, Akron, Ohio.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Nathanial Schreiner, PhD, MBA, RN, Post-Doctoral Fellow, Frances Payne Bolton School of Nursing, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106; e-mail: njs90@case.edu.

Received: June 07, 2018
Accepted: September 24, 2018

10.3928/00989134-20181019-01

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