The Effects of Community-Based Visiting Care on the Quality of Life

490237WJN351010.1177/0193945913490237<italic>Western Journal of Nursing Research</italic>Lim et al. research-article2013 Article The Effects of Community-Based Visiting Care on the Quality of Life Western Journal of Nursing Research 35(10) 1280 1291 The Author(s) 2013 Reprints and permissions: sagepub.com/journalspermissions.nav DOI: 10.1177/0193945913490237 wjn.sagepub.com Ji Young Lim 1, Geun Myun Kim 2, Eun-Joo Kim 3, Kyung Won Choi 4 and Sang Suk Kim 5 Abstract This study explored factors contributing to the quality of life of communitybased home visiting care service users in Korea through a two-level multilevel model. The community health care center s organizational factors including the total number of visiting nurses and visiting nurses rehire rate were treated as covariates. For participant s individual factors (Level 1), only subjective health status and the presence of cerebral vascular disease significantly explained the quality of life. Visiting nurses demographic factors (Level 2) were not statistically significant. However, the total number of visiting nurses and visiting nurses rehire rate were significant. Therefore, to increase an elderly adult s quality of life through community-based home visiting care services, a community health care center s organizational factors should be considered in addition to patient characteristics. This result may prove useful not only for Korea but also for other countries that intend to reform their community-based home visiting care services. 1 Inha University, Incheon, Korea 2 Seoul Women s College of Nursing, Korea 3 Sangji University, Gangwon-do, Korea 4 Hanbuk University, Dongducheon-si, Korea 5 Chung-Ang University, Seoul, Korea Corresponding Author: Geun Myun Kim, Seoul Women s College of Nursing, 287-89 Hongje-dong, Seodaemun-gu, Seoul, 120-090, South Korea. Email: hellena71@hanmail.net

Lim et al. 1281 Keywords community health nursing, home care services, frail elderly, quality of life, multilevel analysis South Korea (to be referred to as Korea hereafter), as in many other countries, has focused on building community-based primary health care systems in the last decade. One such program is the nationwide community-based home visiting care service primarily for the elderly operated by the Korean Ministry of Health and Welfare (KMHW) since 2007, which targets economically vulnerable elderly. Previous studies have found that home visiting services reduced hospital readmissions (Nabagiez, Shariff, Khan, Molloy, & McGinn, 2013) and improved the cognitive functions of the elderly (Ukawa et al., 2012). Community-based home visiting care services in Korea are provided by 253 community health care centers managed by local administrative agencies. Each center has an average of 11 visiting nurses assigned by the central government, and each nurse has an assigned territory. Besides providing customized care for patients, these specially trained visiting nurses also act as case managers during the delivery of services (i.e., assess patients health needs, plan the services needed including reference to other social welfare or health resources, and evaluate the outcomes of delivered services; KMHW, 2012). All services records are logged and managed in the databases of community-based home visiting care services. A More Precise Evaluation of Quality of Life One of the goals of community-based home visiting care services, other than improving users health status to prevent hospital readmission, is to enhance users quality of life. Quality of life is an important assessment criterion of community-based home visiting care services because it refers to individuals perceptions of their position in life in the context of the culture and value system in which they live, and in relation to their goals, expectations, standards, and concerns (Lee, Chan, & Mok, 2010). Therefore, quality of life has to be evaluated precisely and systematically because this outcome has dynamic and complex interactions with internal and external environmental factors. Multiple linear regressions have traditionally been used to determine the contributing factors of quality of life (Seltzer, 1994). However, because the characteristics of hierarchical structured data such as community-based home visiting care services are ignored in a multiple linear regression model, the

1282 Western Journal of Nursing Research 35(10) assumption of independency required for the multiple linear regression model is violated (Adewale et al., 2007). Therefore, this method of analysis could provide biased estimates and inaccurate standard errors, and a more appropriate model is needed. One such model is the multilevel regression model (Kreft & De Leeuw, 1998; Raudenbush & Bryk, 2002), which could handle the above problems and explain the effects of organizational, contextual, and individual factors on program outcome (Von Korff, Koepsell, Curry, & Diehr, 1992). This model could also process data that are clustered and have a hierarchical structure. Therefore, the multilevel regression model was recommended to analyze factors that predict the quality of life (Adewale et al., 2007; Cho, 2003; Park & Lake, 2005). Purpose The purpose of this study was to explore factors contributing to the quality of life of community-based home visiting care service users, such as users individual factors, visiting nurses demographic factors, and community health center s organizational factors, with a multilevel regression model; the specific research questions can be found below. The results of this study have implications for the management model of community-based home visiting care services, which could affect users quality of life. Our results would also be useful to other countries that intend to reform their community-based home visiting care services. Research Questions Research Question 1: How do home visiting care users individual factors affect their quality of life in terms of community-based home visiting care services? Research Question 2: How do visiting nurses demographic factors affect users quality of life? Research Question 3: How do the community health care center s organizational factors affect home visiting care users quality of life? Method Sample This study used the data in the databases of community-based home visiting care services provided by three community health care centers in Korea. We

Lim et al. 1283 used a descriptive method with secondary data analysis. The participants of this study were 611 users and 20 visiting nurses registered with communitybased home visiting care services provided by three community health care centers from one city in Korea. The data were collected from March to December 2008 from the databases of community-based home visiting care services of three community health care centers in Korea. The database contained users demographic information and complete home visiting care service records. Visiting nurses demographic factors and community health care center s organizational factors were gathered from each community center s official 2008 annual report. All data with missing values were excluded from the analysis. Measures Visiting Care Users Individual Factors. The following individual factors were assessed: gender, age, economic status, smoking and drinking habits, subjective health status, and presence of hypertension, diabetes mellitus, cerebral vascular disease, cancer, and/or rheumatoid arthritis. Gender, smoking, and drinking habits were measured dichotomously while age was measured as a continuous variable. Economic status was classified as low, middle, or high. Subjective health status was measured on a 5-point Likert-type scale ranging from 1 (very bad) to 5 (very good) and users could check multiple boxes for presence of disease. Visiting Nurses Demographic Factors. Visiting nurses demographic factors, which consisted of gender, age, and duration of visiting care experience, were measured. However, because all the visiting nurses were women, gender was excluded in the final analysis. Community Health Care Center s Organizational Factors. Two variables, that is, the total number of visiting nurses and visiting nurses rehire rate were measured. In this study, rehire rate refers to the rehiring of the same visiting nurse. These two variables were selected because these were predetermined outcome variables of community-based home visiting care services evaluation by KMHW (2012). Quality of Life. Quality of life was measured via the Quality of Life Short Form-8 (QOL SF-8) developed by Turner-Bowker, Bayliss, Ware, and Kosinski (2003). This scale has been used since 2007 by the KMHW to assess community-based visiting care programs. This scale is composed of eight items divided into three categories: patient-reported physical, mental/

1284 Western Journal of Nursing Research 35(10) emotional, and social aspects of health. This study used the Korean version developed by Chin, Lee, and Chang (2004). The items regarding general health and bodily pain were measured on a 6-point Likert-type scale and the other six items, which include physical functioning, role limitations due to physical health problems, energy/fatigue, social functioning, role limitations due to emotional problems, and psychosocial stress and well-being, were measured on a 5-point Likert-type scale. Higher scores indicate better quality of life. The Cronbach s alpha coefficient obtained for this scale by Chin et al. (2004) was.91 and was.87 when used by Han, Song, and Lim (2010) to measure the effects of a group training program on cognitive enhancement in community-dwelling elderly in Korea. The Cronbach s alpha coefficient obtained in this study was.89. Analysis Statistical analysis was conducted with Stata statistical software program version 11.0 (StataCorp, TX). We used frequency, percentile mean, and SD as descriptive statistics of users and visiting nurses. To examine the factors that affect quality of life, a two-level multilevel model was used. In this model, Level 1 consisted of users who directly received community-based home visiting care services and Level 2 consisted of visiting nurses who provided community-based home visiting care services in a specially assigned territory. The community health care center s organizational factors were treated as covariates. In this study, the ideal sample size was calculated via the formula N effective = m [1 + (n 1) (1 ρ)], for multilevel analysis suggested by Twisk (2006). In this formula, m refers to the number of clusters, n refers to the number of observations for each cluster, and ρ refers to the intraclass correlation coefficient (ICC). The required sample size was 440 according to this formula. Therefore, our actual sample size of 611 visiting care users was more than adequate for a multilevel model analysis in this study. Results General Characteristics The individual factors of home visiting care users are presented in Table 1. There were 427 (69.89%) women and 184 (30.11%) men. The average age was 68.79 years (SD = 13.85). Two hundred and sixty-five (43.37%) home visiting care users perceived themselves to have low economic status. There were 188 (30.77%) smokers and 208 (34.04%) drinkers. The average

Lim et al. 1285 Table 1. General Characteristics of Home Visiting Care Users and Visiting Nurses. Category Variable n (%) Subjects (n = 611) Gender Male 184 (30.11) Female 427 (69.89) Economic status Poor 265 (43.37) Low 186 (30.44) Middle 160 (26.19) Smoking No 423 (69.23) Yes 188 (30.77) Drinking No 403 (65.96) Yes 208 (34.04) Hypertension No 292 (47.79) Yes 319 (52.21) Diabetes mellitus No 492 (80.52) Yes 119 (19.48) Cerebral Vascular Accident No 551 (90.18) Yes 60 (9.82) Cancer No 566 (92.64) Yes 45 (7.36) Rheumatoid arthritis No 367 (60.07) Yes 244 (39.93) Gender Visiting nurses (n = 20) Male 0 (0.00) Female 20 (100.00) subjective health status score was 2.21 points (SD = 0.73). Among the diseases listed, hypertension and rheumatoid arthritis were the most prevalent, affecting 319 (52.21%) and 244 (39.93%) of home visiting care users respectively. The demographic characteristics of the visiting nurses are also presented in Table 1. All of the visiting nurses were women. The average

1286 Western Journal of Nursing Research 35(10) Table 2. Quality of Life of Home Visiting Care Users (N = 611). Items M (SD) General health 3.44 (0.77) Bodily pain 4.69 (1.15) Physical functioning 4.06 (0.86) Role limitations due to physical health problems 4.04 (0.82) Energy/fatigue 3.51 (0.85) Social functioning 4.39 (0.81) Role limitations due to emotional problems 4.21 (0.87) Psychosocial stress and well-being 4.37 (0.79) Total 32.74 (5.45) age was 41.26 (SD = 6.67) years and the average duration of visiting care experience was 8.01 (SD = 1.12) months. Regarding community health care center s organizational factors, the total number of visiting nurses in each community health center was 16, 23, and 33, and the visiting nurses rehire rate was 0, 40, and 75%, respectively. Statistics on home visiting care users quality of life are provided in Table 2. The mean total quality of life score was 32.74 points (SD = 5.45). Generally, the scores on all items were over 4 points; however, two items, general health and energy/fatigue, revealed average scores below 3 points (M = 3.44, SD = 0.77; M = 3.51, SD = 0.85, respectively). Multilevel Model The results of the multilevel regression analysis are shown in Table 3. The Wald chi-square statistic of the random effect model was 179.01 and was statistically significant (p <.001). Between-group variance was 8.75 in the intercept-only model but was 6.64 in the random effect model. ICC was 0.36 in the intercept-only model and 0.30 in the random effect model. From these findings, we can conclude that the random effect model of the multilevel model is more relevant to this study. Regarding users individual factors (Level 1), only subjective health status (coefficient = 2.66, p <.001) and presence of cerebral vascular disease (coefficient = 1.91, p =.001) significantly explained quality of life. None of the demographic factors pertaining to visiting nurses (Level 2) was significant. On the other hand, the total number of visiting nurses in the health care center (coefficient = 21.97, p =.050) and visiting nurses rehire rate (coefficient = 60.75, p =.050) significantly affected home visiting care users quality of life.

Table 3. Parameter Estimates in Multilevel Models. Intercept-only model Random effect model Fixed part Variable Coefficient SE p Coefficient SE p Subject Gender 0.41 0.50.409 Age (year) 0.44 0.26.866 Economic status 0.18 0.01.199 Smoking 0.80 0.50.114 Drinking 0.51 0.42.221 Health status 2.66 0.23 <.001 Hypertension 0.23 0.36.520 Diabetes mellitus 0.06 0.42.873 CVA 1.91 0.56.001 Cancer 1.10 0.63.083 Rheumatoid arthritis 0.15 0.39.692 Nurse Age (year) 0.24 0.10.810 Amount of visiting care experience (month) 0.61 0.57.286 Organization Total number of visiting nurses 21.97 11.33.050 Visiting nurses rehire rate 60.75 31.09.050 Intercept random part 31.68 0.70 <.000 260.74 147.73.078 σ 1 8.75 3.11 6.64 2.71 σ 2 19.05 1.13 15.38 0.90 ICC 0.36 0.30 Chi-square 211.47 <.001 116.02 <.001 Note: ICC = intraclass correlation coefficient. 1287

1288 Western Journal of Nursing Research 35(10) Discussion Improving the quality of life is one of the national health policy goals of Korea as mentioned in the Health Plan 2020, which is a formal governmental policy report implemented in 2011 and will continue until 2020. Because communitybased home visiting care service is one of the public health care services implemented to accomplish this goal, enhancing the quality of life of the elderly is therefore a very critical outcome of this service program. As a result, a more refined approach is needed to determine the factors that affect users quality of life to construct a more effective program structure and delivery system. Many previous studies, systematic reviews, and commissioned reports have emphasized the importance of various factors in improving the quality of life of community-dwelling older people (Han et al., 2010; Lee et al., 2010), but few studies have identified and assessed organizational characteristics as an affecting factor. However, organizational factors of a community health care center could affect visiting nurses workload, which in turn affects nurses job satisfaction, job stress, and performance, and consequently care users quality of life. Therefore, organizational factors could be instrumental to better understand the effects of community-based home visiting care services. Most previous studies used multiple linear regression to analyze the effects of community-based home visiting care services based on a presumption of average effect. Other studies suggested that methods that do not consider the hierarchical structure of data could obtain different results due to errors in measuring the effect of services (Lake, 2006; Simon, Müller, & Hasselhorn, 2010; Von Korff et al., 1992). This could explain why the present study, which used multilevel modeling instead of multiple linear regression, obtained results different from those of previous studies that investigated the determinants of quality of life (Mowad, 2004; Paskulin & Molzahn, 2007). In this study, the quality of life of care users registered to communitybased home visiting care services could be explained by subjective health status and presence of cerebral vascular disease; users who had higher subjective health status perceived themselves to have better quality of life compared with those with lower subjective health status. Users with cerebral vascular disease had lower quality of life than other users. However, users quality of life was not significantly related to visiting nurses demographic factors. This could be because the services provided by visiting nurses were highly standardized by work manuals or guidelines. Therefore, service contents and/or quality could be similar across nurses. On the other hand, our study found that the total number of visiting nurses and the rehire rate of visiting nurses, which could affect the workload of visiting nurses, significantly affected users quality of life. Therefore, the

Lim et al. 1289 organizational management policy of community health care centers should be reevaluated and adjusted if they are seeking to improve the quality of life of home visiting care users because organizational factors such as the total number of visiting nurses or the visiting nurses rehire rate can be adjusted and changed to raise the quality of life of the elderly and to maximize the effect of services. In other words, the community health care center s organizational factors are causes that we can control, and they need to be considered when implementing the management policies of community-based home visiting care services. However, although we found important findings that have implications in the field of home visiting nursing care, there are several limitations to this study. First, users were selected from the databases of community-based home visiting care services of three community health care centers in Korea without random selection. Therefore, the generalizability of the findings to other populations of community-dwelling elderly may be limited. Second, the measurement variables were not ideally selected according to the study s purposes because the data were extracted from databases constructed by the KMHW. Therefore, further studies with refined experimental research designs are needed. In conclusion, multilevel analysis is a more appropriate method of planning and assessing future home visiting care. In addition, elderly participant s quality of life was affected by not only their individual factors but also by the community health care center s organizational factors. This means that interventions to enhance elderly adults quality of life should focus on changing the management structure of community-based home visiting care services in addition to considering user characteristics such as subjective health status or current disease. We suggest that to increase an elderly adult s quality of life through community-based home visiting care services, and to make these services more effective, a community health care center s organizational factors such as its employment policy should be reviewed and changed while still applying modified interventions according to an elderly adult s personal characteristics such as health status perception or current disease types. Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article. Funding The authors received no financial support for the research, authorship, and publication of this article.

1290 Western Journal of Nursing Research 35(10) References Adewale, A. J., Hayduk, L., Estabrooks, C. A., Cummings, G. G., Midodzi, W. K., & Derksen, L. (2007). Understanding hierarchical linear models: Applications in nursing research. Nursing Research, 56(Suppl. 4), S40-S46. Chin, Y., Lee, I., & Chang, H. (2004). Analysis of the effects and nursing intervention of home health care in public health centers. Journal of Korean Academy of Community Health Nursing, 15, 353-363. Cho, S. H. (2003). Using multilevel analysis in patient and organizational outcomes research. Nursing Research, 52, 61-65. Han, Y. R., Song, M. S., & Lim, J. Y. (2010). The effects of a cognitive enhancement group training program for community-dwelling elders. Journal of Korean Academy of Nursing, 40, 724-735. Korean Ministry of Health & Welfare. (2012). Guidelines for community-based home visiting care services. Seoul, South Korea: Author. Kreft, I. G. G., & De Leeuw, J. (1998). Introducing multilevel modeling. Thousand Oaks, CA: Sage. Lake, E. T. (2006). Multilevel models in health outcomes research: Part I: Theory, design and measurement. Applied Nursing Research, 19, 51-53. Lee, Y. Y., Chan, M. F., & Mok, E. (2010). Effectiveness of music intervention on the quality of life of older people. Journal of Advanced Nursing, 66, 2677-2687. Mowad, L. (2004). Correlates of quality of life in older adult veterans. Western Journal of Nursing Research, 26, 293-306. Nabagiez, J. P., Shariff, M. A., Khan, M. A., Molloy, W. J., & McGinn, J. T., Jr. (2013). Physician assistant home visit program to reduce hospital readmissions. Journal of Thoracic and Cardiovascular Surgery, 145, 225-231. Park, S., & Lake, E. T. (2005). Multilevel modeling of a clustered continuous outcome: Nurses work hours and burnout. Nursing Research, 54, 406-413. Paskulin, L. M. G., & Molzahn, A. (2007). Quality of life of older adults in Canada and Brazil. Western Journal of Nursing Research, 29, 10-26. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. (2nd ed.). Thousand Oaks, CA: Sage. Seltzer, M. H. (1994). Studying variation in program success: A multilevel modeling approach. Evaluation Review, 18, 342-361. Simon, M., Müller, B. H., & Hasselhorn, H. M. (2010). Leaving the organization or the profession A multilevel analysis of nurses intentions. Journal of Advanced Nursing, 66, 616-626. Turner-Bowker, D. M., Bayliss, M. S., Ware, J. E., Jr., & Kosinski, M. (2003). Usefulness of the ST-8 health survey for comparing the impact of migraine and other conditions. Quality of Life Research, 12, 1003-1012. Twisk, J. W. R. (2006). Applied multilevel analysis: A practical guide for medical researchers. Cambridge, UK: Cambridge University Press. Ukawa, S., Yuasa, M., Ikeno, T., Yoshioka, E., Satoh, H., Murata, W., & Kishi, R. (2012). Randomised controlled pilot study in Japan comparing a home visit

Lim et al. 1291 program using a functional improvement tool with a home visit with conversation alone. Australasian Journal on Ageing, 31, 187-189. Von Korff, M., Koepsell, T., Curry, S., & Diehr, P. (1992). Multi-level analysis in epidemiologic research on health behaviors and outcomes. American Journal of Epidemiology, 135, 1077-1082.