Hospitalizations and Associated Costs Among Older Adults Receiving Regular Home Care Services

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Participants (n=293) were persons aged ≥65 years living in Eastern Finland and receiving regular home care services. Aims To examine factors, diagnoses, and costs associated with hospitalizations among home care clients Methods Baseline examination encompassed comprehensive information on clients’ demographic characteristics, morbidity as measured by the Charlson Comorbidity Index (CCI), current pharmacological treatments, and functional assessments. Binary logistic regression was used to assess factors predicting hospitalization and linear regression to assess factors predicting the number of inpatient days. The costs of hospitalizations were calculated using the national unit costs of health care. Results A total of 176 (60%) home care clients were hospitalized at least once during the follow-up. A higher CCI (OR 1.26, CI95% 1.10-1.45), lower BADL (0.71, 0.59-0.89) and IADL scores (0.81, 0.72-0.92), longer TUG times (1.02, 1.00-1.03), and lower HRQoL (0.25, 0.09-0.71) were associated with hospitalizations. Cognitive and functional impairment increased the number of inpatient days. The total costs of hospitalizations were €2,370,910, with primary care accounting for €1,267,519 and secondary care €1,103,391 of the costs. In the entire cohort, the costs amounted to €9,225 per person-year. Discussion Despite regular home care services, over half of the home care clients were hospitalized with substantial costs in one-year timeframe. Greater comorbidity burden, functional impairment and lower health-related quality of life predicted hospitalizations, while cognitive impairment was associated with increased inpatient days. Conclusions Besides optimal disease management, preserving functioning of the clients is a critical component of home care. Hospitalization home care older adults health care costs functioning Figures Figure 1 1. INTRODUCTION Home care clients are a growing and vulnerable patient group with multimorbidity and complex needs for health and social services. Home care is considered a possible strategy to reduce the use of other health and social care services; [ 1 , 2 ] yet unplanned hospitalizations are nevertheless common among home care clients. According to an earlier Finnish study, 43% of new home care clients were hospitalized during a one-year follow-up [ 3 , 4 ]. Higher total health care service use, comorbidity and cognitive impairment have been associated with higher hospitalization costs [ 5 , 6 ]. Hospitalization costs peak during a patient’s last year of life but may also decrease with age and shift to long-term care [ 7 ]. Hospitalizations among older people are also associated with many negative outcomes such as rapid functional decline and death [ 8 , 9 ]. Hospitalizations of older people are often due acute exacerbations of chronic diseases, infections or injuries [ 1 , 3 , 10 ]. Earlier studies among home care clients or home-dwelling older people also indicate that many health-related and social factors, such as previous coronary artery disease, congestive heart failure, Parkinson’s disease, a history of falls, cognitive impairment, housing-related problems, health-related quality of life (HRQoL) and poor self-rated health, are risk factors for hospitalizations [ 1 , 4 , 10 – 20 ]. One major risk factor for hospitalizations among older people is functional disability, which also is the main prerequisite for receiving home care services [ 14 , 17 , 21 , 22 ]. We examined hospitalizations in a cohort of older people who were receiving home care services on a regular basis. We focused on the factors that predicted hospital admissions and inpatient days within a one-year timeframe. The primary diagnoses for hospitalizations and care settings were analyzed as well. Little is known about the costs of hospitalizations among home care clients; to fill the gap, we analyzed the costs of hospital care. 2. MATERIALS AND METHODS 2.1 Study design and participants This study was a subgroup analysis of the Finnish Interprofessional Medication Assessment (FIMA) study. The FIMA RCT evaluated the effects of medication assessment compared with usual care in public home care settings in 2015–2016 [ 23 – 25 ]. The participants of the present study were established home care clients aged ≥ 65 years and residing in the city of Savonlinna, located in Eastern Finland. Of the 301 participants (59% of the total FIMA cohort) receiving regular home care services, eight individuals were excluded due to missing baseline data (n = 1), absence of follow-up information (n = 5), or death shortly after enrollment (n = 2). Preliminary analyses revealed no statistically significant unadjusted associations between randomization status (intervention vs. usual care) and hospitalization outcomes. Therefore, the intervention and control groups were pooled in the present study. In adjusted analyses, the randomization status was still used as a covariate to control its potential associations with explanatory factors within a multivariate analysis. Ethical approval for the FIMA study protocol was granted by the Research Ethics Committee of the Northern Savo Hospital District and Kuopio University Hospital on February 3, 2015 and the study has been registered with ClinicalTrials.gov (Identifier: NCT02398812, March 2015). 2.2 Data collection Baseline data collection in 2015 was carried out by interprofessional home care teams consisting of a pharmacist, a physician, and a registered nurse. [ 23 , 24 ] 23,24 The baseline data collection comprised of participants’ demographic characteristics, documented medical diagnoses, overall comorbidity burden (assessed using the modified Charlson Comorbidity Index, CCI), medication use, and functional assessments. We calculated the CCI using the following diseases with corresponding scores: metastatic or terminal cancer (score of 6), moderate or severe renal insufficiency and non-metastatic cancer (score of 2), coronary artery disease, cerebrovascular disease, dementia of any type, type 1 or 2 diabetes, chronic asthma or obstructive pulmonary disease, peripheral vascular disease, rheumatoid arthritis, or a history of gastrointestinal bleeding (score of 1). Functional assessments included mobility (Timed Up and Go test, TUG), cognitive performance (Mini-Mental State Examination, MMSE), basic (Kazt Index, BADL) and instrumental (Lawton and Brody Scale, IADL) activities of daily living, mood (Geriatric Depression Scale, GDS-15), health-related quality of life (HRQoL, measured by the EuroQol EQ-5D-3L), and self-rated health status (SRH) [ 23 , 25 ]. 2.3 Outcome measures 2.3.1 Hospitalization The maximum length of follow-up was one year after the baseline examinations, and the follow-up data were collected from the electronic medical records of the East Savo Health Care District. The numbers of hospital admissions and inpatient days were recorded. The use of hospital care was presented by setting (primary or secondary care), specialty, and the diagnostic category according to the International Classification of Diseases, Tenth Revision (ICD-10) [ 26 ]. Following the collection of clinical and hospitalization data, all personal identifiers were removed to ensure participant anonymity. This de-identification process guaranteed that neither the researchers involved in data analysis and reporting, nor the readers of published findings could identify individual participants. 2.3.2 Costs We calculated the costs of hospitalizations and divided them by person-years. These costs were analyzed across both primary and secondary care settings and by specialties and diagnostic categories. We calculated the costs using the national unit costs of social and health care in Finland compiled by the Finnish Institute for Health and Welfare [ 27 ]. Unit costs were adjusted for inflation using the public social and health care expenditure price index for the year 2023 and are presented in euros [ 28 ]. In the Finnish health care system, the majority of hospitalization costs are publicly funded through taxation, with patient fees accounting for approximately 4% of the total. For the purposes of this analysis, both tax-funded expenditures and patient fees were aggregated to represent the total cost of hospitalization. 2.4 Statistical analysis The characteristics of the patients were summarized using percentages, means, medians, standard deviations and quartiles. Mean or median values were compared between groups using a t-test, Mann-Whitney U-test and Pearson χ 2 -test. In the main analysis, we applied a two-step procedure. First, we used binary logistic regression to assess factors predicting hospitalizations (yes vs. no) within a year after baseline. Second, we used linear regression to assess factors predicting the number of inpatient days. The baseline characteristics presented in Table 1 served as potential factors. The first step included all patients (n = 293) whereas the second step included only the hospitalized ones, i.e. those who had been admitted to hospital at least once within the follow-up (n = 176). The number of inpatient days was expressed relative to the length of follow-up, i.e., transformed to a number per year for each patient regardless of the actual length of the follow-up, which for some patients was shorter than one year. In both steps, we analyzed every factor separately, adjusting for age, sex, and the intervention group (medication assessment or usual care). As an additional exploratory analysis, we constructed multivariate logistic and linear regressions based on factors related to health, functioning, and mood that showed statistical significance (p < 0.05) in the main analysis. Single diseases were not used as explanators in multivariate regressions since the number of cases per disease varied a lot from disease to disease, and for some diseases the number was very low (< 5). Concerning linear regressions, model residuals were inspected to diagnose model fit. Regarding logistic regressions, Area Under the Curve (AUC) was calculated to estimate performance, and the Hosmer and Lemeshow test was executed to evaluate the goodness-of-fit. Table 1 Baseline characteristic of the home care clients by number of inpatient days Number of inpatient days All patients Variables 0 (n = 117) ≥ 1 (n = 176) p-value n = 293 Age, mean (SD) 81.1 (6.6) 83.6 (5.9) 0.001 82.6 (6.3) Women, n (%) 79 (67.5) 128 (72.7) 0.361 207 (70.6) Living alone, n (%) 82 (70.1) 128 (72.7) 0.507 210 (73.4) Intervention, n (%) 55 (47.0) 92 (52.3) 0.405 147 (50.2) Health and diseases Excellent or good self-rated health, n (%) 24 (21.2) 49 (28.7) 0.169 73 (24.9) Charlson Comorbidity Index, mean (SD) 2.4 (1.8) 3.3 (2.0) < 0.001 2.9 (1.9) Diabetes, n (%) 39 (33.3) 66 (37.5) 0.534 105 (35.8) Dementia, n (%) 22 (18.8) 54 (30.7) 0.029 76 (25.9) Coronary artery disease, n (%) 48 (41.0) 73 (41.5) 0.518 121 (41.3) Heart failure, n (%) 27 (23.1) 73 (41.5) 0.001 100 (34.1) Cerebrovascular disease, n (%) 38 (32.5) 55 (31.3) 0.898 93 (31.7) Peripheral vascular disease, n (%) 7 (6.0) 24 (13.6) 0.035 31 (10.6) COPD or asthma, n (%) 16 (13.7) 31 (17.6) 0.419 47 (16.0) Non-metastatic cancer, n (%) 7 (6.0) 22 (12.9) 0.073 29 (9.9) Metastatic or terminal cancer, n (%) < 5 < 5 0.306 4 (1.4) Previous gastrointestinal bleeding, n (%) < 5 < 5 0.394 5 (1.7) Rheumatoid arthritis, n (%) 7 (6.0) 13 (7.6) 0.814 20 (6.8) Glomerulal filtration rate < 50 ml/min, n (%) 21 (17.9) 58 (33.0) 0.005 79 (27.0) Medication Number of all medicines, median (IQR) 14 (11.0, 17.0) 15 (12.0, 18.0) 0.297 14.0 (12.0, 18.0) ≥ 10 medicines in use, n (%) 103 (88.0) 153 (86.9) 0.859 256 (87.4) Class D medicine in the Meds75+, n (%) 35 (29.9) 38 (21.6) 0.129 73 (24.9) Functioning and mood MMSE, median (IQR) 23 (20.0, 26.0) 23 (20.0, 25.0) 0.079 23.0 (20.0, 26.0) BADL, median (IQR) 5 (5.0, 6.0) 5 (4.0, 6.0) < 0.001 5.0 (4.0, 6.0) IADL, median (IQR) 5 (3.0, 6.0) 4 (2.0, 6.0) < 0.001 4.0 (2.0, 6.0) TUG, second, median (IQR) 18.6 (12.8, 26.9) 22.3 (16.0, 33.9) 0.015 20.4 (14.7, 29.9) GDS-15, median (IQR) 4 (2.25,7.0) 5 (3.0, 7.5) 0.258 4.0 (3.0, 7.0) Health related quality of life EQ5D, median (IQR) 0.69 (0.59, 0.73) 0.59 (0.52, 0.73) 0.006 0.6 (0.5, 0.7) Quality of life VAS 0-100, median (IQR) 60.0 (50.0, 73.0) 55.00 (48.5, 69.5) 0.008 55.0 (49.0, 70.0) Received medication assessment intervention, COPD Chronic obstructive Pulmonary Disease, Meds75 + database of medication for older adults, class D: avoid use in older adults, MMSE Mini-Mental State of Examination, BADL Basic Activities of Daily Living, IADL Instrumental Activities of Daily Living, TUG Timed Up and Go test, GDS-15 Geriatric Depression Scale, EQ-5D EuroQol instrument with 5 dimensions and 3 levels for measuring health-related quality of life, P-value threshold for statistical significance: < 0.05. The significance of the results is presented as p-values, and values < 0.05 are considered statistically significant. All statistical analyses were performed using IBM SPSS software version 29. 3. RESULTS At the baseline (Table 1 ), the mean age of all the clients (n = 293) was 82.6 years (range 65–95 years). The majority were women and lived alone. The most common chronic conditions were coronary artery disease (41%), diabetes (36%), heart failure (34%), cerebrovascular disease (32%), renal failure (27%) and dementia (26%). The mean CCI of all clients was 2.9. Most clients had difficulties in performing daily activities; half of the home care clients needed help with at least one basic and four instrumental activities of daily living. The MMSE scores were indicative of mild or more advanced dementia in a half of the home care clients (median MMSE score 23.0). The median time to complete the TUG test was 20.4 seconds. A total of 176 (60%) home care clients were hospitalized at least once during the follow-up. The home care clients with hospitalizations were older, had a higher CCI and suffered more often from dementia, peripheral vascular disease or heart or renal failure at the baseline of the study compared with home care clients without hospitalizations. They also had more difficulties in BADLs and IADLs, worse TUG times, and lower HRQoL than the home care clients without hospitalizations. 3.1 Factors predicting hospitalizations Logistic regressions showed that a history of heart failure was associated with an almost two-fold risk of hospitalization (Table 2 ). The other factors statistically significantly associated with hospitalizations were CCI, peripheral vascular disease, renal failure, BADL and IADL scores, TUG times, and HRQoL. Table 2 Factors associated with hospitalization. Logistic regression in which each variable was analyzed separately. All the analyses were adjusted for age, sex and intervention group Variables B p-value OR (CI95%) Living alone, n (%) -0.24 0.396 0.79 (0.46–1.37) Health and diseases Excellent or good self-reported health -0.39 0.184 0.68 (0.38–1.21) Charlson Comorbidity Index 0.23 0.001 1.26 (1.10–1.45) Diabetes 0.29 0.256 1.34 (0.81–2.23) Dementia 0.55 0.063 1.73 (0.97–3.07) Coronary artery disease -0.05 0.849 0.95 (0.59–1.55) Heart failure 0.72 0.009 2.06 (1.20–3.53) Cerebrovascular disease 0.04 0.897 1.04 (0.61–1.75) Peripheral vascular disease 1.01 0.027 2.74 (1.12–6.73) COPD or asthma 0.38 0.266 1.47 (0.75–2.87) Non-metastatic cancer -1.49 0.207 0.23 (0.02–2.27) Metastatic or terminal cancer 0.70 0.124 2.02 (0.82–4.96) Previous gastrointestinal bleeding -0.53 0.572 0.59 (0.09–3.71) Rheumatoid arthritis 0.17 0.736 1.18 (0.44–3.16) Glomerulal filtration rate < 50 ml/min -0.62 0.041 0.54 (0.30–0.98) Medication All medicines 0.02 0.311 1.03 (0.98–1.08) Class D medicine in the Meds75+ -0.51 0.068 0.60 (0.82–2.16) ≥ 10 medicines in use -0.08 0.825 0.92 (0.45–1.90) Functioning and mood MMSE -0.03 0.267 0.97 (0.92–1.02) BADL -0.33 0.003 0.72 (0.59–0.89) IADL -0.21 < 0.001 0.81 (0.72–0.92) TUG 0.02 0.047 1.02 (1.00–1.03) GDS-15 0.04 0.280 1.04 (0.97–1.13) Health related quality of life EQ5D -1.39 0.009 0.25 (0.09–0.71) Quality of life VAS 0-100 -0.02 0.014 0.98 (0.97–1.00) COPD Chronic obstructive Pulmonary Disease, Meds75 + database of medication for older adults, class D: avoid use in older adults, MMSE Mini-Mental State of Examination, BADL Basic Activities of Daily Living, IADL Instrumental Activities of Daily Living, TUG Timed Up and Go test, GDS-15 Geriatric Depression Scale, EQ-5D EuroQol instrument with 5 dimensions and 3 levels for measuring health-related quality of life, P-value threshold for statistical significance: < 0.05. In the multivariate logistic regression, CCI (p = 0.008) predicted hospitalizations whereas age (p = 0.101), sex (p = 0.329), intervention (p = 0.253), BADL (p = 0.571), IADL (p = 0.225), TUG (p = 0.293), EQ-5D-3L (p = 0.372) did not. The model correctly classified 66% of clients who were hospitalized but only 46% of those who were not (AUC = 68.2). The Hosmer and Lemeshow test indicated a good fit of the model (p = 0.700). 3.2 Factors predicting inpatient days We performed a linear regression analysis on clients who were hospitalized for at least one day during the study period (n = 176). A history of diabetes or heart failure and MMSE, BADL and IADL scores were associated with the cumulative number of inpatient days (Table 3 ). A one-point increase in the MMSE was associated with approximately reduction of 0.5 inpatient days, whereas a one-point increase in BADL and IADL scores was associated with nearly one-day decrease in inpatient days. Table 3 Baseline characteristics associated with the cumulative number of inpatient days per person-year. Linear regression in which each variable was analyzed separately. All the analyses were adjusted for age, sex and intervention group Variables B (CI 95%) p-value Living alone, n (%) -1.59 (-4.05–0.86) 0.202 Health and diseases Excellent or good self-reported health 1.17 (-1.18–3.52) 0.326 Charlson Comorbidity Index -0.16 (-0.70–0.37) 0.553 Diabetes -2.54 [-4.64–(-0.43)] 0.019 Dementia 3.52 (1.33–5.70) 0.002 Coronary artery disease -1.19 (-3.29–0.92) 0.267 Heart failure -2.52 [-4.66–(-0.38)] 0.021 Cerebrovascular disease -0.61 (-2.89–1.67) 0.599 Peripheral vascular disease -0.17 (-3.25–2.90) 0.911 COPD or asthma -2.04 (-4.78–0.69) 0.143 Non-metastatic cancer 0.23 (-2.98–3.44) 0.889 Metastatic or terminal cancer -5.40 (-19.33–8.53) 0.445 Previous gastrointestinal bleeding -2.99 (-13.05–7.06) 0.557 Rheumatoid arthritis 1.27 (-2.79–5.33) 0.538 Glomerulal filtration rate < 50 ml/min -0.16 (-0.70–0.37) 0.553 Medication All medicines -0.23 [-0.43 – (-0.02)] 0.029 Class D medicine in the Meds75+ -0.09 (-2.62–2.44) 0.943 ≥ 10 medicines in use -1.30 (-4.38–1.79) 0.409 Functioning and mood MMSE -0.38 [-0.59–(-0.16)] < 0.001 BADL -0.92 [-1.67–(-0.16)] 0.018 IADL -0.83 [-1.31–(-0.35)] < 0.001 TUG 0.02 (-0.03–0.07) 0.390 GDS-15 0.12 (-0.21–0.46) 0.464 Health related quality of life EQ5D -0.819 (-4.95–3.31) 0.696 Quality of life VAS 0-100 -0.04 (-0.09–0.03) 0.250 COPD Chronic obstructive Pulmonary Disease, Meds75 + database of medication for older adults, class D: avoid use in older adults, MMSE Mini-Mental State of Examination, BADL Basic Activities of Daily Living, IADL Instrumental Activities of Daily Living, TUG Timed Up and Go test, GDS-15 Geriatric Depression Scale, EQ-5D EuroQol instrument with 5 dimensions and 3 levels for measuring health-related quality of life, P-value threshold for statistical significance: < 0,05. In the multivariate linear regression, MMSE [B -0.28, CI95% -0.52 – (–0.04), p-value 0.024] predicted the number of hospital days per person-year but intervention, age, sex, CCI, BADL and IADL did not (p > 0.05). The total model was statistically significant (p = 0.009), although it explained only 7% of variation in the data, and model diagnostics raised some concerns regarding residual randomness as well as outcome skewness. 3.3 Reasons for and costs of hospitalizations During the follow-up period, the home care clients were hospitalized for 4,697 days; 3,489 in primary care and 1,208 in secondary care hospitals (Table 4 ). There were 19.3 hospital days per person-year in the entire cohort, 13.6 in primary care and 5.7 in secondary care. Table 4 Days and costs of hospitalizations n (%) Days/person-year Total costs (€) Cost/person-year (€) Total 4,697 (100) 19.28 2,370,910 9,225 Primary care 3,489 (74.3) 13.61 1,267,519 4,932 Secondary care 1,208 (25.7) 5.71 1,103,391 4,293 Main specialties: Internal medicine 503 (41.6) 1.96 350,669 1,368 Surgery 500 (41.4) 1.95 679,435 2,616 Pulmonary disease 120 (9.93) 0.47 66,271 258 Neurology 52 (4.30) 0.20 24,919 97 In secondary care, most patients were treated in internal medicine (42%) or surgery wards (41%). The most common diagnostic categories for all inpatient days were diseases of the circulatory system (ICD-10 I00-I99, 25% of the days), injuries, poisonings and other consequences of external causes (S00-T98,16%), as well as diseases of the nervous system (G00-G99,15%). The total cost of hospitalizations was €2,370,910, with primary care accounting for €1,267,519 and secondary care for €1,103,391 of the cost. In the entire cohort, the costs were €9,225 per person-year, €4,932 from primary care and €4,293 from secondary care hospitalizations. The costs of primary and secondary care hospitalizations were nearly similar, even though the number of inpatient days in primary care was three times higher than in secondary care. Internal medicine and surgery costs were €1,368 and €2,616 per person-year, respectively. 4. DISCUSSION 4.1 Key findings In this prospective study, hospitalizations among home care clients were associated with a greater comorbidity burden, a history of peripheral vascular disease or heart or renal failure, and worse mobility. Better functioning in basic and instrumental activities of daily living and a higher quality of life decreased the risk of hospitalization. The primary reasons for hospitalizations most often fell into the diagnostic categories of circulatory system diseases, injuries and nervous system diseases. The home care clients were hospitalized on average for 19 days per year and hospitalization costs per client exceeded €9,000 per year. The number of primary care inpatient days was nearly three times higher than that of secondary care, yet the hospitalization costs were nearly the same. Rehabilitation of older patients is mainly conducted in primary care hospitals, and this on its part might explain extended stays. On the other hand, all the patients cannot be discharged back to their own homes, and they usually have to wait for a nursing home place in primary care wards. 4.2 Comparison with previous studies In our study, 60% of home care clients were hospitalized within a one-year time frame. This is more than in an earlier Finnish study, where 43% of home care clients were hospitalized during a one-year follow-up [ 3 , 4 ]. This may be due to study designs, the reference study included new home care clients only and in addition our inclusion criteria selected to identify home care clients with medication-related problems. In this home care cohort, a higher comorbidity burden and a history of heart or renal failure and peripheral vascular disease increased the risk of hospitalization. A large register-based study conducted by the inter-RAI network reported that coronary artery disease, heart failure, cancer, emphysema and renal failure increased the six-month hospitalization risk among older home care clients in Canada, Finland and the U.S. [ 1 ]. Furthermore, in a large register-based U.S. cohort of new Medicare Home Health (MHH) clients aged ≥ 65 years, a primary diagnosis of heart disease was associated with a higher risk of hospitalization within two months [ 17 ]. In an earlier MHH cohort, congestive heart failure increased the hospitalization risk by 42% when it was identified as the primary diagnosis for receiving home care services [ 29 ]. Similar to our findings, an Israeli study of 1,932 home care clients aged ≥ 70 years reported that a higher comorbidity burden was a risk factor for hospitalizations within the subsequent year [ 30 ]. An Italian study of older home care clients (n = 1,291) also reported that comorbidity raised the one-year hospitalization risk [ 10 ]. In this study, higher HRQoL decreased the risk of hospitalization. In contrast, there were no differences in self-rated health between those who were not hospitalized and those who were not. Previously, HRQoL and self-rated health have been associated with emergency department (ED) visits and hospitalizations among home care clients [ 4 , 33 ]. A Finnish study showed that poor self-rated health was an independent risk factor for unplanned hospitalizations among new home care clients [ 4 ]. An Australian study of 1,999 chronically ill patients (mean age 63 years) reported that after one and two years, lower HRQoL was associated with an approximately two-fold increase in the number of ED visits and hospitalizations [ 20 ]. Lower HRQoL also increased the risk of unplanned hospital readmissions in chronically ill home-dwelling older Australians [ 19 ]. Earlier U.S. studies found that depressive symptoms were associated with hospitalizations among older home care clients [ 16 , 17 ] whereas in our study, GDS-15 scores showed no association with hospital use. In the U.S. studies, depressive symptoms were assessed using a two-item questionnaire: whether or not the client had a depressed mood (feeling sad or tearful) or by using the two-item Patient Health Questionnaire. The difference between study results may be due to different measurement methods. In the present study, longer TUG times and lower BADL and IADL scores had correlative associations with hospitalizations. In addition, lower BADL, IADL and MMSE scores were also associated with a higher number of inpatient days. An earlier Finnish study found that cognitive impairment increased the risk of hospitalization among new home care clients aged ≥ 65 years [ 3 ]. Impaired mobility itself and as a manifestation of frailty syndrome may increase vulnerability during acute illnesses and delay recovery [ 31 ]. Injuries were the second leading cause of hospitalizations in our study, and most injuries of older people are fall-related [ 32 ]. A Swedish study of 1,402 older people showed that ADL dependency increased hospital admissions in six-year follow-up [ 14 ]. Functional dependency also increased the use and costs of other health care services, as reported in a U.S. study of community-dwelling older adults [ 21 ]. The excess health care costs were approximately $ 10,000 per dependent person over a period of two years compared to those who remained independent. In our study, diabetes, heart failure, and greater number of medicines were associated with fewer inpatient days. This finding differs from earlier studies. It might have been that these clients had a higher baseline frequency of home care visits, which could have facilitated a more rapid discharge from the hospital. In this study, based on multivariate logistic regression, the CCI had direct association with hospitalizations and in multivariate linear regression MMSE was associated directly with inpatient days. In our dataset, functional impairment may exert its effect on the outcomes indirectly through the CCI, and the MMSE scores may provide a more accurate representation of cognitive performance than dementia diagnoses. Overall, baseline characteristics explained only a small share of inpatient days in the present home care cohort. There might be organizational factors, for example a lack of care services required after discharge, that affect the duration of hospitalizations more than the patient-related factors. It would be important to identify these factors as well. The annual costs of hospitalizations were on average €9,250 per home care client. The costs were not evenly distributed in the cohort, as 42% of the home care clients were not hospitalized during the follow-up. In addition, the primary care and secondary care costs were almost the same, even though only a quarter of the inpatient days were from secondary care. The average cost of one inpatient day was €363 and €913 in primary and secondary care hospitals, respectively. Treatment costs in English geriatric wards and Finnish primary care wards were comparable in 2010–2011 (€238 vs. €213 per patient per day) [ 18 ]. The most common reasons for hospitalizations were treatment of circulatory system diseases, injuries and nervous system diseases. A recent Finnish study reported that infectious diseases were the most common reason for hospitalizations among home care clients [ 3 ]. In the present analysis, infections were classified under several ICD-10 categories (for example pleuropulmonary infections under category J and urinary tract infections under N). This might explain the difference. In Finland, home care and nursing services for older people were previously arranged by municipalities, but since 2023 they have been provided by wellbeing services counties. The population structure of Finland is aging rapidly. Home care can be considered as a strategy to reduce the use of more expensive care services such as residential, nursing home or hospital care. A meta-analysis of 20 studies assessing the impact of home care on the use of inpatient care reported that home care reduced hospital days [ 34 ]. Home care nurses can identify symptoms of acute illnesses and exacerbations of chronic conditions in their early stages; in addition, some of these conditions can be managed at home as well. However, this requires the availability of skilled nursing staff and sufficient financing, both of which seem to be challenges for aging societies, including Finland. The proportion and more recently also the absolute number of older Finns receiving regular home care services have decreased [ 32 ]. 4.3 Strengths and limitations One of the strengths of the present study was that the study was carried out in the real-life context. Data collection was conducted using validated instruments appropriate for older people. The home care clients were interviewed and examined instead of leaning solely on the register-based data. The records of hospitalizations can be assumed to be highly accurate, because the data collection is obligatory and regulated by law and it serves also as a basis for invoicing. Nonetheless, certain limitations must be acknowledged. Predicting hospitalization and the duration of inpatient care among home care clients remains challenging. This unpredictability may stem not only from sudden and acute changes in health status, but also from structural and functional complexities of the health and social care services. The relatively small sample size may have reduced the statistical power of the study. On the other hand, statistically significant findings observed in a small sample are likely to reflect true effects. The dataset was derived from a single geographic region in Finland. Nevertheless, due to the implementation of national quality guidelines for home care, we consider the findings to be generalizable to the wider Finnish home care context and are potentially applicable to other Nordic countries with comparable service structures. 4.4 Conclusions A higher comorbidity burden, a history of peripheral vascular disease or heart or renal failure, functional impairment and worse mobility were predictors of hospitalizations. Better functioning in basic and instrumental activities of daily living and higher health-related quality of life decreased the hospitalization risk of home care clients. Functional and cognitive impairments were associated with more inpatient days. In addition to optimal disease management, preserving functional capacity of home care clients is a critical component of comprehensive care. Annual hospitalization costs per home care client exceeded €9,000. The costs of primary and secondary care hospitalizations were nearly similar, even though the number of inpatient days in primary care was three times higher than that in secondary care. The most common primary reasons for hospitalizations were circulatory system diseases and injuries, which could serve as primary targets in the prevention of hospitalizations. Declarations Declaration of interest The authors declare no conflicts of interest that may inappropriately influence this work. Data availability statements The datasets generated and/or analyzed during the current study are not publicly available due to participants not consenting to sharing datasets publicly; however, they are available from the corresponding author on reasonable request. Funding Sources This work is supported by the Primary Health Care Unit of Kuopio University Hospital; The Finnish Medical Foundation; the Ministry of Social Affairs and Health, Finland; the State Research Funding (SRF) for university-level health research, Kuopio University Hospital, Wellbeing Services County of North Savo; and Outpatient Research Foundation, Finland. The funders did not take part in the study design, data collection and analysis or preparation and publishing of the manuscript. CRediT authorship contributions statement Eeva Björkstedt: Writing –original draft, review and editing, Investigation, Conceptualization, Formal analysis, Data curation, Funding acquisition, Eija Lönnroos : Writing –review and editing, Investigation, Conceptualization, Funding acquisition, Supervision, Methodology, Validation Pekka Mäntyselkä: Writing –review and editing, Investigation, Conceptualization, Funding acquisition, Supervision, Methodology, Validation, Project Administration Johanna Jyrkkä : Writing –review and editing, Investigation, Conceptualization, Methodology, Validation, Project Administration Ari Voutilainen : Writing –review and editing, Conceptualization, Data curation, Formal analysis, Methodology, Validation Virva Hyttinen-Huotari: Writing –review and editing, Conceptualization, Methodology, Supervision, Validation Kati Päivärinta : Writing –review and editing, Investigation, Conceptualization, Funding acquisition All authors have approved the final version of the manuscript Acknowledgements We appreciate the work done by the health care personnel in conducting this study in Savonlinna, Finland. The authors would also like to thank home care patients who participated in the FIMA study. References Morris JN, Howard EP, Steel K et al (2014) Predicting risk of hospital and emergency department use for home care elderly persons through a secondary analysis of cross-national data. BMC Health Serv Res 14 Nov 14(1):519. 10.1186/s12913-014-0519-z Landi F, Onder G, Russo A et al (2001) A new model of integrated home care for the elderly: impact on hospital use. J Clin Epidemiol 9(54):968–970. 10.1016/S0895-4356(01)00366-3 Rönneikkö JK, Jämsen ER, Mäkelä M et al (2018) Reasons for home care clients’ unplanned hospital admissions and their associations with patient characteristics. Arch Gerontol Geriatr 78:114–126. 10.1016/j.archger.2018.06.008 Rönneikkö JK, Mäkelä M, Jämsen ER, Huhtala H, Finne-Soveri H, Noro A (2017) ym. Predictors for Unplanned Hospitalization of New Home Care Clients. 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Int J Qual Health Care 25(5):542–554. 10.1093/intqhc/mzt056 Doran D, Hirdes JP, Blais R et al (2013) Adverse Events Associated with Hospitalization or Detected through the RAI-HC Assessment among Canadian Home Care Clients. Healthc Policy Aug 9(1):76–88 Landi F, Onder G, Cesari M et al (2004) Comorbidity and social factors predicted hospitalization in frail elderly patients. J Clin Epidemiol 57832–836. 10.1016/j.clinepi.2004.01.013 Gjestsen MT, Brønnick K, Testad I (2018) Characteristics and predictors for hospitalizations of home-dwelling older persons receiving community care: a cohort study from Norway. BMC Geriatr 18(1):203. 10.1186/s12877-018-0887-z Fortinsky RH, Madigan EA, Sheehan TJ et al (2006) Risk Factors for Hospitalization Among Medicare Home Care Patients. West J Nurs Res 28(8):902–917. 10.1177/0193945906286810 Inacio MC, Jorissen RN, Khadka J et al (2021) Predictors of short-term hospitalization and emergency department presentations in aged care. J Am Geriatr Soc 69(11):3142–3156. 10.1111/jgs.17317 Sandberg M, Kristensson J, Midlöv P et al (2012) Prevalence and predictors of healthcare utilization among older people (60+): Focusing on ADL dependency and risk of depression. Arch Gerontol Geriatr 54(3):e349–e363. 10.1016/j.archger.2012.02.006 Boult C, Dowd B, McCaffrey D et al (1993) Screening Elders for Risk of Hospital Admission. J Am Geriatr Soc. ;41(8):811–7. 10.1111/j.1532-5415 . 1993.tb06175.x Fortinsky RH, Madigan EA, Sheehan TJ et al (2014) Risk Factors for Hospitalization in a National Sample of Medicare Home Health Care Patients. J Appl Gerontol 33(4):474–493. 10.1177/0733464812454007 Lohman MC, Scherer EA, Whiteman KL et al (2018) Factors Associated With Accelerated Hospitalization and Re-hospitalization Among Medicare Home Health Patients. 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Arch Intern Med 161(21):2602–2607. 10.1001/archinte.161.21.2602 Sears NA, Blais R, Spinks M et al (2017) Associations between patient factors and adverse events in the home care setting: a secondary data analysis of two canadian adverse event studies. BMC Health Serv Res 17(1):400. 10.1186/s12913-017-2351-8 Auvinen K, Räisänen J, Merikoski M et al (2019) The Finnish Interprofessional Medication Assessment (FIMA): baseline findings from home care setting. Aging Clin Exp Res 31(10):1471–1479. 10.1007/s40520-018-1085-8 Auvinen K, Räisänen J, Voutilainen A et al (2021) Interprofessional Medication Assessment has Effects on the Quality of Medication Among Home Care Patients: Randomized Controlled Intervention Study. J Am Med Dir Assoc 22(1):74–81. 10.1016/j.jamda.2020.07.007 Björkstedt E, Voutilainen A, Auvinen K et al (2023) The role of functioning in predicting nursing home placement or death among older home care patients. Scand J Prim Health Care 41(4):478–485. 10.1080/02813432.2023.2274333 ICD-10 Version (2019) https://icd.who.int/browse10/2019/en . Accessed Oct 11, 2024 Finnish Institute for Health and Welfare (THL) Terveyden- ja sosiaalihuollon yksikkökustannukset Suomessa vuonna 2017. https://www.julkari.fi/handle/10024/142882 . Accessed on Jun 18, 2024 Statistics Finland - Prices and Costs (2024) - Price index of public expenditure https://stat.fi/til/jmhi/index_en.html Accessed on Oct 6 Fortinsky RH, Madigan EA, Sheehan TJ et al (2014) Risk Factors for Hospitalization in a National Sample of Medicare Home Health Care Patients. J Appl Gerontol 33(4):474–493. 10.1177/0733464812454007 Inouye SK, Zhang Y, Jones RN et al (2008) Risk Factors for Hospitalization Among Community-Dwelling Primary Care Older Patients: Development and Validation of a Predictive Model. Med Care 46(7):726. 10.1097/MLR.0b013e3181649426 Clegg A, Young J, Iliffe S et al (2013) Frailty in Older People. Lancet 381(9868):752–762. 10.1016/S0140-6736(12)62167-9 Finnish Institute for Health and Welfare (THL) (2025) Finland https://thl.fi/en/topics/management-of-health-and-wellbeing-promotion/safety-promotion/injuries-among-older-people . Accessed on Jan 8 Björkstedt E, Voutilainen A, Hyttinen-Huotari V et al (2025) Predictors, Diagnoses, and Costs of Emergency Department Visits among Home Care Clients. J Am Med Dir Assoc 26(1):105308. 10.1016/j.jamda.2024.105308 Hughes SL, Ulasevich A, Weaver FM, Henderson W, Manheim L, Kubal JD (eds) (1997) ym. Impact of home care on hospital days: a meta-analysis. Health Serv Res. ;32(4):415–32 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8388855","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571423929,"identity":"3f47c764-946d-4720-890f-5487ed6e35e2","order_by":0,"name":"Eeva 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1","display":"","copyAsset":false,"role":"figure","size":28325,"visible":true,"origin":"","legend":"\u003cp\u003eDays and costs of hospitalizations in primary and secondary care among 293 home care clients\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8388855/v1/ac76b4fbc0f69901b24294a8.png"},{"id":100366094,"identity":"3e607be0-80de-465e-94ef-10c2e72b5e84","added_by":"auto","created_at":"2026-01-16 07:55:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1211154,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8388855/v1/375bffe1-ab2f-47dd-b9e6-ce3a78203971.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHospitalizations and Associated Costs Among Older Adults Receiving Regular Home Care Services\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eHome care clients are a growing and vulnerable patient group with multimorbidity and complex needs for health and social services. Home care is considered a possible strategy to reduce the use of other health and social care services; [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] yet unplanned hospitalizations are nevertheless common among home care clients. According to an earlier Finnish study, 43% of new home care clients were hospitalized during a one-year follow-up [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHigher total health care service use, comorbidity and cognitive impairment have been associated with higher hospitalization costs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Hospitalization costs peak during a patient\u0026rsquo;s last year of life but may also decrease with age and shift to long-term care [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hospitalizations among older people are also associated with many negative outcomes such as rapid functional decline and death [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHospitalizations of older people are often due acute exacerbations of chronic diseases, infections or injuries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Earlier studies among home care clients or home-dwelling older people also indicate that many health-related and social factors, such as previous coronary artery disease, congestive heart failure, Parkinson\u0026rsquo;s disease, a history of falls, cognitive impairment, housing-related problems, health-related quality of life (HRQoL) and poor self-rated health, are risk factors for hospitalizations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. One major risk factor for hospitalizations among older people is functional disability, which also is the main prerequisite for receiving home care services [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe examined hospitalizations in a cohort of older people who were receiving home care services on a regular basis. We focused on the factors that predicted hospital admissions and inpatient days within a one-year timeframe. The primary diagnoses for hospitalizations and care settings were analyzed as well. Little is known about the costs of hospitalizations among home care clients; to fill the gap, we analyzed the costs of hospital care.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and participants\u003c/h2\u003e \u003cp\u003eThis study was a subgroup analysis of the Finnish Interprofessional Medication Assessment (FIMA) study. The FIMA RCT evaluated the effects of medication assessment compared with usual care in public home care settings in 2015\u0026ndash;2016 [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe participants of the present study were established home care clients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years and residing in the city of Savonlinna, located in Eastern Finland. Of the 301 participants (59% of the total FIMA cohort) receiving regular home care services, eight individuals were excluded due to missing baseline data (n\u0026thinsp;=\u0026thinsp;1), absence of follow-up information (n\u0026thinsp;=\u0026thinsp;5), or death shortly after enrollment (n\u0026thinsp;=\u0026thinsp;2).\u003c/p\u003e \u003cp\u003ePreliminary analyses revealed no statistically significant unadjusted associations between randomization status (intervention vs. usual care) and hospitalization outcomes. Therefore, the intervention and control groups were pooled in the present study. In adjusted analyses, the randomization status was still used as a covariate to control its potential associations with explanatory factors within a multivariate analysis.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e for the FIMA study protocol was granted by the Research Ethics Committee of the Northern Savo Hospital District and Kuopio University Hospital on February 3, 2015 and the study has been registered with ClinicalTrials.gov (Identifier: NCT02398812, March 2015).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eBaseline data collection in 2015 was carried out by interprofessional home care teams consisting of a pharmacist, a physician, and a registered nurse. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003csup\u003e23,24\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe baseline data collection comprised of participants\u0026rsquo; demographic characteristics, documented medical diagnoses, overall comorbidity burden (assessed using the modified Charlson Comorbidity Index, CCI), medication use, and functional assessments. We calculated the CCI using the following diseases with corresponding scores: metastatic or terminal cancer (score of 6), moderate or severe renal insufficiency and non-metastatic cancer (score of 2), coronary artery disease, cerebrovascular disease, dementia of any type, type 1 or 2 diabetes, chronic asthma or obstructive pulmonary disease, peripheral vascular disease, rheumatoid arthritis, or a history of gastrointestinal bleeding (score of 1). Functional assessments included mobility (Timed Up and Go test, TUG), cognitive performance (Mini-Mental State Examination, MMSE), basic (Kazt Index, BADL) and instrumental (Lawton and Brody Scale, IADL) activities of daily living, mood (Geriatric Depression Scale, GDS-15), health-related quality of life (HRQoL, measured by the EuroQol EQ-5D-3L), and self-rated health status (SRH) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcome measures\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Hospitalization\u003c/h2\u003e \u003cp\u003eThe maximum length of follow-up was one year after the baseline examinations, and the follow-up data were collected from the electronic medical records of the East Savo Health Care District. The numbers of hospital admissions and inpatient days were recorded. The use of hospital care was presented by setting (primary or secondary care), specialty, and the diagnostic category according to the International Classification of Diseases, Tenth Revision (ICD-10) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFollowing the collection of clinical and hospitalization data, all personal identifiers were removed to ensure participant anonymity. This de-identification process guaranteed that neither the researchers involved in data analysis and reporting, nor the readers of published findings could identify individual participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Costs\u003c/h2\u003e \u003cp\u003eWe calculated the costs of hospitalizations and divided them by person-years. These costs were analyzed across both primary and secondary care settings and by specialties and diagnostic categories. We calculated the costs using the national unit costs of social and health care in Finland compiled by the Finnish Institute for Health and Welfare [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnit costs were adjusted for inflation using the public social and health care expenditure price index for the year 2023 and are presented in euros [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In the Finnish health care system, the majority of hospitalization costs are publicly funded through taxation, with patient fees accounting for approximately 4% of the total. For the purposes of this analysis, both tax-funded expenditures and patient fees were aggregated to represent the total cost of hospitalization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe characteristics of the patients were summarized using percentages, means, medians, standard deviations and quartiles. Mean or median values were compared between groups using a t-test, Mann-Whitney U-test and Pearson χ\u003csup\u003e2\u003c/sup\u003e -test.\u003c/p\u003e \u003cp\u003eIn the main analysis, we applied a two-step procedure. First, we used binary logistic regression to assess factors predicting hospitalizations (yes vs. no) within a year after baseline. Second, we used linear regression to assess factors predicting the number of inpatient days. The baseline characteristics presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e served as potential factors. The first step included all patients (n\u0026thinsp;=\u0026thinsp;293) whereas the second step included only the hospitalized ones, i.e. those who had been admitted to hospital at least once within the follow-up (n\u0026thinsp;=\u0026thinsp;176). The number of inpatient days was expressed relative to the length of follow-up, i.e., transformed to a number per year for each patient regardless of the actual length of the follow-up, which for some patients was shorter than one year. In both steps, we analyzed every factor separately, adjusting for age, sex, and the intervention group (medication assessment or usual care). As an additional exploratory analysis, we constructed multivariate logistic and linear regressions based on factors related to health, functioning, and mood that showed statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the main analysis. Single diseases were not used as explanators in multivariate regressions since the number of cases per disease varied a lot from disease to disease, and for some diseases the number was very low (\u0026lt;\u0026thinsp;5). Concerning linear regressions, model residuals were inspected to diagnose model fit. Regarding logistic regressions, Area Under the Curve (AUC) was calculated to estimate performance, and the Hosmer and Lemeshow test was executed to evaluate the goodness-of-fit.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristic of the home care clients by number of inpatient days\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003einpatient days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 (n\u0026thinsp;=\u0026thinsp;176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.1 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.6 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.6 (6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e207 (70.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (70.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e210 (73.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147 (50.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth and diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent or good self-rated health, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (24.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson Comorbidity Index, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105 (35.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76 (25.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121 (41.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100 (34.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93 (31.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD or asthma, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-metastatic cancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (9.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic or terminal cancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious gastrointestinal bleeding, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatoid arthritis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerulal filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;50 ml/min, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 (27.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of all medicines, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (11.0, 17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (12.0, 18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.0 (12.0, 18.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10 medicines in use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (86.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e256 (87.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass D medicine in the Meds75+, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (24.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFunctioning and mood\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (20.0, 26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (20.0, 25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.0 (20.0, 26.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBADL, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0 (4.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0 (2.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG, second, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6 (12.8, 26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.3 (16.0, 33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.4 (14.7, 29.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDS-15, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.25,7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.0, 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0 (3.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth related quality of life\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ5D, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.59, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59 (0.52, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 (0.5, 0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of life VAS 0-100, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.0 (50.0, 73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.00 (48.5, 69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.0 (49.0, 70.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eReceived medication assessment intervention, COPD Chronic obstructive Pulmonary Disease, Meds75\u0026thinsp;+\u0026thinsp;database of medication for older adults, class D: avoid use in older adults, MMSE Mini-Mental State of Examination, BADL Basic Activities of Daily Living, IADL Instrumental Activities of Daily Living, TUG Timed Up and Go test, GDS-15 Geriatric Depression Scale, EQ-5D EuroQol instrument with 5 dimensions and 3 levels for measuring health-related quality of life, P-value threshold for statistical significance: \u0026lt; 0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe significance of the results is presented as p-values, and values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are considered statistically significant. All statistical analyses were performed using IBM SPSS software version 29.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eAt the baseline (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the mean age of all the clients (n\u0026thinsp;=\u0026thinsp;293) was 82.6 years (range 65\u0026ndash;95 years). The majority were women and lived alone. The most common chronic conditions were coronary artery disease (41%), diabetes (36%), heart failure (34%), cerebrovascular disease (32%), renal failure (27%) and dementia (26%).\u003c/p\u003e \u003cp\u003eThe mean CCI of all clients was 2.9. Most clients had difficulties in performing daily activities; half of the home care clients needed help with at least one basic and four instrumental activities of daily living. The MMSE scores were indicative of mild or more advanced dementia in a half of the home care clients (median MMSE score 23.0). The median time to complete the TUG test was 20.4 seconds.\u003c/p\u003e \u003cp\u003eA total of 176 (60%) home care clients were hospitalized at least once during the follow-up. The home care clients with hospitalizations were older, had a higher CCI and suffered more often from dementia, peripheral vascular disease or heart or renal failure at the baseline of the study compared with home care clients without hospitalizations. They also had more difficulties in BADLs and IADLs, worse TUG times, and lower HRQoL than the home care clients without hospitalizations.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Factors predicting hospitalizations\u003c/h2\u003e \u003cp\u003eLogistic regressions showed that a history of heart failure was associated with an almost two-fold risk of hospitalization (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The other factors statistically significantly associated with hospitalizations were CCI, peripheral vascular disease, renal failure, BADL and IADL scores, TUG times, and HRQoL.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with hospitalization. Logistic regression in which each variable was analyzed separately. All the analyses were adjusted for age, sex and intervention group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79 (0.46\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth and diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent or good self-reported health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.38\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.26 (1.10\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34 (0.81\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.73 (0.97\u0026ndash;3.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.59\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.06 (1.20\u0026ndash;3.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.61\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.74 (1.12\u0026ndash;6.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD or asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47 (0.75\u0026ndash;2.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-metastatic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.02\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic or terminal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02 (0.82\u0026ndash;4.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious gastrointestinal bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59 (0.09\u0026ndash;3.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18 (0.44\u0026ndash;3.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerulal filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;50 ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.30\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll medicines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.98\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass D medicine in the Meds75+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.82\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10 medicines in use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.45\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFunctioning and mood\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.92\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.59\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81 (0.72\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.00\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDS-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.97\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth related quality of life\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ5D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25 (0.09\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of life VAS 0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCOPD Chronic obstructive Pulmonary Disease, Meds75\u0026thinsp;+\u0026thinsp;database of medication for older adults, class D: avoid use in older adults, MMSE Mini-Mental State of Examination, BADL Basic Activities of Daily Living, IADL Instrumental Activities of Daily Living, TUG Timed Up and Go test, GDS-15 Geriatric Depression Scale, EQ-5D EuroQol instrument with 5 dimensions and 3 levels for measuring health-related quality of life, P-value threshold for statistical significance: \u0026lt; 0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the multivariate logistic regression, CCI (p\u0026thinsp;=\u0026thinsp;0.008) predicted hospitalizations whereas age (p\u0026thinsp;=\u0026thinsp;0.101), sex (p\u0026thinsp;=\u0026thinsp;0.329), intervention (p\u0026thinsp;=\u0026thinsp;0.253), BADL (p\u0026thinsp;=\u0026thinsp;0.571), IADL (p\u0026thinsp;=\u0026thinsp;0.225), TUG (p\u0026thinsp;=\u0026thinsp;0.293), EQ-5D-3L (p\u0026thinsp;=\u0026thinsp;0.372) did not. The model correctly classified 66% of clients who were hospitalized but only 46% of those who were not (AUC\u0026thinsp;=\u0026thinsp;68.2). The Hosmer and Lemeshow test indicated a good fit of the model (p\u0026thinsp;=\u0026thinsp;0.700).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Factors predicting inpatient days\u003c/h2\u003e \u003cp\u003eWe performed a linear regression analysis on clients who were hospitalized for at least one day during the study period (n\u0026thinsp;=\u0026thinsp;176). A history of diabetes or heart failure and MMSE, BADL and IADL scores were associated with the cumulative number of inpatient days (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A one-point increase in the MMSE was associated with approximately reduction of 0.5 inpatient days, whereas a one-point increase in BADL and IADL scores was associated with nearly one-day decrease in inpatient days.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics associated with the cumulative number of inpatient days per person-year. Linear regression in which each variable was analyzed separately. All the analyses were adjusted for age, sex and intervention group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.59 (-4.05\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth and diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent or good self-reported health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (-1.18\u0026ndash;3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.16 (-0.70\u0026ndash;0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.54 [-4.64\u0026ndash;(-0.43)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52 (1.33\u0026ndash;5.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.19 (-3.29\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.52 [-4.66\u0026ndash;(-0.38)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.61 (-2.89\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.17 (-3.25\u0026ndash;2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD or asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.04 (-4.78\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-metastatic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23 (-2.98\u0026ndash;3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic or terminal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.40 (-19.33\u0026ndash;8.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious gastrointestinal bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.99 (-13.05\u0026ndash;7.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27 (-2.79\u0026ndash;5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerulal filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;50 ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.16 (-0.70\u0026ndash;0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll medicines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.23 [-0.43 \u0026ndash; (-0.02)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass D medicine in the Meds75+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.09 (-2.62\u0026ndash;2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10 medicines in use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.30 (-4.38\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFunctioning and mood\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.38 [-0.59\u0026ndash;(-0.16)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.92 [-1.67\u0026ndash;(-0.16)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.83 [-1.31\u0026ndash;(-0.35)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02 (-0.03\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDS-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12 (-0.21\u0026ndash;0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth related quality of life\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ5D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.819 (-4.95\u0026ndash;3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of life VAS 0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.04 (-0.09\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCOPD Chronic obstructive Pulmonary Disease, Meds75\u0026thinsp;+\u0026thinsp;database of medication for older adults, class D: avoid use in older adults, MMSE Mini-Mental State of Examination, BADL Basic Activities of Daily Living, IADL Instrumental Activities of Daily Living, TUG Timed Up and Go test, GDS-15 Geriatric Depression Scale, EQ-5D EuroQol instrument with 5 dimensions and 3 levels for measuring health-related quality of life, P-value threshold for statistical significance: \u0026lt; 0,05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the multivariate linear regression, MMSE [B -0.28, CI95% -0.52 \u0026ndash; (\u0026ndash;0.04), p-value 0.024] predicted the number of hospital days per person-year but intervention, age, sex, CCI, BADL and IADL did not (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The total model was statistically significant (p\u0026thinsp;=\u0026thinsp;0.009), although it explained only 7% of variation in the data, and model diagnostics raised some concerns regarding residual randomness as well as outcome skewness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Reasons for and costs of hospitalizations\u003c/h2\u003e \u003cp\u003eDuring the follow-up period, the home care clients were hospitalized for 4,697 days; 3,489 in primary care and 1,208 in secondary care hospitals (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There were 19.3 hospital days per person-year in the entire cohort, 13.6 in primary care and 5.7 in secondary care.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDays and costs of hospitalizations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDays/person-year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal costs (\u0026euro;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCost/person-year (\u0026euro;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4,697 (100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e19.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2,370,910\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e9,225\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3,489 (74.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,267,519\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4,932\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecondary care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,208 (25.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,103,391\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4,293\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain specialties:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e503 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e350,669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e679,435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (9.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24,919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn secondary care, most patients were treated in internal medicine (42%) or surgery wards (41%). The most common diagnostic categories for all inpatient days were diseases of the circulatory system (ICD-10 I00-I99, 25% of the days), injuries, poisonings and other consequences of external causes (S00-T98,16%), as well as diseases of the nervous system (G00-G99,15%).\u003c/p\u003e \u003cp\u003eThe total cost of hospitalizations was \u0026euro;2,370,910, with primary care accounting for \u0026euro;1,267,519 and secondary care for \u0026euro;1,103,391 of the cost. In the entire cohort, the costs were \u0026euro;9,225 per person-year, \u0026euro;4,932 from primary care and \u0026euro;4,293 from secondary care hospitalizations. The costs of primary and secondary care hospitalizations were nearly similar, even though the number of inpatient days in primary care was three times higher than in secondary care. Internal medicine and surgery costs were \u0026euro;1,368 and \u0026euro;2,616 per person-year, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Key findings\u003c/h2\u003e \u003cp\u003eIn this prospective study, hospitalizations among home care clients were associated with a greater comorbidity burden, a history of peripheral vascular disease or heart or renal failure, and worse mobility. Better functioning in basic and instrumental activities of daily living and a higher quality of life decreased the risk of hospitalization. The primary reasons for hospitalizations most often fell into the diagnostic categories of circulatory system diseases, injuries and nervous system diseases. The home care clients were hospitalized on average for 19 days per year and hospitalization costs per client exceeded \u0026euro;9,000 per year. The number of primary care inpatient days was nearly three times higher than that of secondary care, yet the hospitalization costs were nearly the same. Rehabilitation of older patients is mainly conducted in primary care hospitals, and this on its part might explain extended stays. On the other hand, all the patients cannot be discharged back to their own homes, and they usually have to wait for a nursing home place in primary care wards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Comparison with previous studies\u003c/h2\u003e \u003cp\u003eIn our study, 60% of home care clients were hospitalized within a one-year time frame. This is more than in an earlier Finnish study, where 43% of home care clients were hospitalized during a one-year follow-up [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This may be due to study designs, the reference study included new home care clients only and in addition our inclusion criteria selected to identify home care clients with medication-related problems.\u003c/p\u003e \u003cp\u003eIn this home care cohort, a higher comorbidity burden and a history of heart or renal failure and peripheral vascular disease increased the risk of hospitalization. A large register-based study conducted by the inter-RAI network reported that coronary artery disease, heart failure, cancer, emphysema and renal failure increased the six-month hospitalization risk among older home care clients in Canada, Finland and the U.S. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Furthermore, in a large register-based U.S. cohort of new Medicare Home Health (MHH) clients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, a primary diagnosis of heart disease was associated with a higher risk of hospitalization within two months [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In an earlier MHH cohort, congestive heart failure increased the hospitalization risk by 42% when it was identified as the primary diagnosis for receiving home care services [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similar to our findings, an Israeli study of 1,932 home care clients aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years reported that a higher comorbidity burden was a risk factor for hospitalizations within the subsequent year [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. An Italian study of older home care clients (n\u0026thinsp;=\u0026thinsp;1,291) also reported that comorbidity raised the one-year hospitalization risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, higher HRQoL decreased the risk of hospitalization. In contrast, there were no differences in self-rated health between those who were not hospitalized and those who were not. Previously, HRQoL and self-rated health have been associated with emergency department (ED) visits and hospitalizations among home care clients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A Finnish study showed that poor self-rated health was an independent risk factor for unplanned hospitalizations among new home care clients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. An Australian study of 1,999 chronically ill patients (mean age 63 years) reported that after one and two years, lower HRQoL was associated with an approximately two-fold increase in the number of ED visits and hospitalizations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Lower HRQoL also increased the risk of unplanned hospital readmissions in chronically ill home-dwelling older Australians [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarlier U.S. studies found that depressive symptoms were associated with hospitalizations among older home care clients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] whereas in our study, GDS-15 scores showed no association with hospital use. In the U.S. studies, depressive symptoms were assessed using a two-item questionnaire: whether or not the client had a depressed mood (feeling sad or tearful) or by using the two-item Patient Health Questionnaire. The difference between study results may be due to different measurement methods.\u003c/p\u003e \u003cp\u003eIn the present study, longer TUG times and lower BADL and IADL scores had correlative associations with hospitalizations. In addition, lower BADL, IADL and MMSE scores were also associated with a higher number of inpatient days. An earlier Finnish study found that cognitive impairment increased the risk of hospitalization among new home care clients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Impaired mobility itself and as a manifestation of frailty syndrome may increase vulnerability during acute illnesses and delay recovery [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Injuries were the second leading cause of hospitalizations in our study, and most injuries of older people are fall-related [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A Swedish study of 1,402 older people showed that ADL dependency increased hospital admissions in six-year follow-up [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Functional dependency also increased the use and costs of other health care services, as reported in a U.S. study of community-dwelling older adults [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The excess health care costs were approximately \u003cspan\u003e$\u003c/span\u003e10,000 per dependent person over a period of two years compared to those who remained independent.\u003c/p\u003e \u003cp\u003eIn our study, diabetes, heart failure, and greater number of medicines were associated with fewer inpatient days. This finding differs from earlier studies. It might have been that these clients had a higher baseline frequency of home care visits, which could have facilitated a more rapid discharge from the hospital.\u003c/p\u003e \u003cp\u003eIn this study, based on multivariate logistic regression, the CCI had direct association with hospitalizations and in multivariate linear regression MMSE was associated directly with inpatient days. In our dataset, functional impairment may exert its effect on the outcomes indirectly through the CCI, and the MMSE scores may provide a more accurate representation of cognitive performance than dementia diagnoses.\u003c/p\u003e \u003cp\u003eOverall, baseline characteristics explained only a small share of inpatient days in the present home care cohort. There might be organizational factors, for example a lack of care services required after discharge, that affect the duration of hospitalizations more than the patient-related factors. It would be important to identify these factors as well.\u003c/p\u003e \u003cp\u003eThe annual costs of hospitalizations were on average \u0026euro;9,250 per home care client. The costs were not evenly distributed in the cohort, as 42% of the home care clients were not hospitalized during the follow-up. In addition, the primary care and secondary care costs were almost the same, even though only a quarter of the inpatient days were from secondary care. The average cost of one inpatient day was \u0026euro;363 and \u0026euro;913 in primary and secondary care hospitals, respectively. Treatment costs in English geriatric wards and Finnish primary care wards were comparable in 2010\u0026ndash;2011 (\u0026euro;238 vs. \u0026euro;213 per patient per day) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most common reasons for hospitalizations were treatment of circulatory system diseases, injuries and nervous system diseases. A recent Finnish study reported that infectious diseases were the most common reason for hospitalizations among home care clients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the present analysis, infections were classified under several ICD-10 categories (for example pleuropulmonary infections under category J and urinary tract infections under N). This might explain the difference.\u003c/p\u003e \u003cp\u003eIn Finland, home care and nursing services for older people were previously arranged by municipalities, but since 2023 they have been provided by wellbeing services counties. The population structure of Finland is aging rapidly. Home care can be considered as a strategy to reduce the use of more expensive care services such as residential, nursing home or hospital care. A meta-analysis of 20 studies assessing the impact of home care on the use of inpatient care reported that home care reduced hospital days [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Home care nurses can identify symptoms of acute illnesses and exacerbations of chronic conditions in their early stages; in addition, some of these conditions can be managed at home as well. However, this requires the availability of skilled nursing staff and sufficient financing, both of which seem to be challenges for aging societies, including Finland. The proportion and more recently also the absolute number of older Finns receiving regular home care services have decreased [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Strengths and limitations\u003c/h2\u003e \u003cp\u003eOne of the strengths of the present study was that the study was carried out in the real-life context. Data collection was conducted using validated instruments appropriate for older people. The home care clients were interviewed and examined instead of leaning solely on the register-based data. The records of hospitalizations can be assumed to be highly accurate, because the data collection is obligatory and regulated by law and it serves also as a basis for invoicing.\u003c/p\u003e \u003cp\u003eNonetheless, certain limitations must be acknowledged. Predicting hospitalization and the duration of inpatient care among home care clients remains challenging. This unpredictability may stem not only from sudden and acute changes in health status, but also from structural and functional complexities of the health and social care services. The relatively small sample size may have reduced the statistical power of the study. On the other hand, statistically significant findings observed in a small sample are likely to reflect true effects. The dataset was derived from a single geographic region in Finland. Nevertheless, due to the implementation of national quality guidelines for home care, we consider the findings to be generalizable to the wider Finnish home care context and are potentially applicable to other Nordic countries with comparable service structures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Conclusions\u003c/h2\u003e \u003cp\u003eA higher comorbidity burden, a history of peripheral vascular disease or heart or renal failure, functional impairment and worse mobility were predictors of hospitalizations. Better functioning in basic and instrumental activities of daily living and higher health-related quality of life decreased the hospitalization risk of home care clients. Functional and cognitive impairments were associated with more inpatient days. In addition to optimal disease management, preserving functional capacity of home care clients is a critical component of comprehensive care.\u003c/p\u003e \u003cp\u003eAnnual hospitalization costs per home care client exceeded \u0026euro;9,000. The costs of primary and secondary care hospitalizations were nearly similar, even though the number of inpatient days in primary care was three times higher than that in secondary care.\u003c/p\u003e \u003cp\u003eThe most common primary reasons for hospitalizations were circulatory system diseases and injuries, which could serve as primary targets in the prevention of hospitalizations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest that may inappropriately influence this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to participants not consenting to sharing datasets publicly; however, they are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by the Primary Health Care Unit of Kuopio University Hospital; The Finnish Medical Foundation; the Ministry of Social Affairs and Health, Finland; the State Research Funding (SRF) for university-level health research, Kuopio University Hospital, Wellbeing Services County of North Savo; and Outpatient Research Foundation, Finland. The funders did not take part in the study design, data collection and analysis or preparation and publishing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEeva Bj\u0026ouml;rkstedt:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash;original draft, review and editing, Investigation, Conceptualization, Formal analysis, Data curation, Funding acquisition,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEija L\u0026ouml;nnroos\u003c/strong\u003e: Writing \u0026ndash;review and editing, Investigation, Conceptualization, Funding acquisition, Supervision, Methodology, Validation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePekka M\u0026auml;ntyselk\u0026auml;:\u003c/strong\u003e Writing \u0026ndash;review and editing, Investigation, Conceptualization, Funding acquisition, Supervision, Methodology, Validation, Project Administration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJohanna Jyrkk\u0026auml;\u003c/strong\u003e: Writing \u0026ndash;review and editing, Investigation, Conceptualization, Methodology, Validation, Project Administration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAri Voutilainen\u003c/strong\u003e: Writing \u0026ndash;review and editing, Conceptualization, Data curation, Formal analysis, Methodology, Validation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVirva Hyttinen-Huotari:\u003c/strong\u003e Writing \u0026ndash;review and editing, Conceptualization, Methodology, Supervision, Validation \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKati P\u0026auml;iv\u0026auml;rinta\u003c/strong\u003e: Writing \u0026ndash;review and editing, Investigation, Conceptualization, Funding acquisition\u003c/p\u003e\n\u003cp\u003eAll authors have approved the final version of the manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the work done by the health care personnel in conducting this study in Savonlinna, Finland. The authors would also like to thank home care patients who participated in the FIMA study. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMorris JN, Howard EP, Steel K et al (2014) Predicting risk of hospital and emergency department use for home care elderly persons through a secondary analysis of cross-national data. BMC Health Serv Res 14 Nov 14(1):519. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12913-014-0519-z\u003c/span\u003e\u003cspan address=\"10.1186/s12913-014-0519-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandi F, Onder G, Russo A et al (2001) A new model of integrated home care for the elderly: impact on hospital use. 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Accessed on Jan 8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBj\u0026ouml;rkstedt E, Voutilainen A, Hyttinen-Huotari V et al (2025) Predictors, Diagnoses, and Costs of Emergency Department Visits among Home Care Clients. J Am Med Dir Assoc 26(1):105308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jamda.2024.105308\u003c/span\u003e\u003cspan address=\"10.1016/j.jamda.2024.105308\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughes SL, Ulasevich A, Weaver FM, Henderson W, Manheim L, Kubal JD (eds) (1997) ym. Impact of home care on hospital days: a meta-analysis. Health Serv Res. ;32(4):415\u0026ndash;32\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hospitalization, home care, older adults, health care costs, functioning","lastPublishedDoi":"10.21203/rs.3.rs-8388855/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8388855/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a prospective one-year follow-up study. Participants (n=293) were persons aged ≥65 years living in Eastern Finland and receiving regular home care services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine factors, diagnoses, and costs associated with hospitalizations among home care clients\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline examination encompassed comprehensive information on clients’ demographic characteristics, morbidity as measured by the Charlson Comorbidity Index (CCI), current pharmacological treatments, and functional assessments.\u003c/p\u003e\n\u003cp\u003eBinary logistic regression was used to assess factors predicting hospitalization and linear regression to assess factors predicting the number of inpatient days. The costs of hospitalizations were calculated using the national unit costs of health care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 176 (60%) home care clients were hospitalized at least once during the follow-up. A higher CCI (OR 1.26, CI95% 1.10-1.45), lower BADL (0.71, 0.59-0.89) and IADL scores (0.81, 0.72-0.92), longer TUG times (1.02, 1.00-1.03), and lower HRQoL (0.25, 0.09-0.71) were associated with hospitalizations. Cognitive and functional impairment increased the number of inpatient days. The total costs of hospitalizations were €2,370,910, with primary care accounting for €1,267,519 and secondary care €1,103,391 of the costs. In the entire cohort, the costs amounted to €9,225 per person-year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite regular home care services, over half of the home care clients were hospitalized with substantial costs in one-year timeframe. Greater comorbidity burden, functional impairment and lower health-related quality of life predicted hospitalizations, while cognitive impairment was associated with increased inpatient days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBesides optimal disease management, preserving functioning of the clients is a critical component of home care.\u003c/p\u003e","manuscriptTitle":"Hospitalizations and Associated Costs Among Older Adults Receiving Regular Home Care Services","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 09:22:07","doi":"10.21203/rs.3.rs-8388855/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-24T15:59:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T08:45:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T09:39:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37257375694709520803065161714412499034","date":"2026-02-15T09:34:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T07:19:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78066032309486684694094343332771873265","date":"2026-02-11T00:48:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302523508438448114203676295150809222424","date":"2026-02-01T16:27:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T10:45:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-23T15:21:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-18T03:32:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aging Clinical and Experimental Research","date":"2025-12-17T19:47:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f5657ee7-c2e4-4531-8b4c-c359711bded7","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T19:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 09:22:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8388855","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8388855","identity":"rs-8388855","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}