Identifying and Characterizing Subgroups of Medically Complex Older Patients in Community-based Intermediate Care: A Latent Class Analysis of Danish Municipal Temporary Stay Patients

Identifying and Characterizing Subgroups of Medically Complex Older Patients in Community-based Intermediate Care: A Latent Class Analysis of Danish Municipal Temporary Stay Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying and Characterizing Subgroups of Medically Complex Older Patients in Community-based Intermediate Care: A Latent Class Analysis of Danish Municipal Temporary Stay Patients Mahan Rajaeigolsefidi, Rebecca Futtrup Gantriis, Kasper Edwards, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6744694/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2026 Read the published version in BMC Health Services Research → Version 1 posted 12 You are reading this latest preprint version Abstract Background: In Denmark, municipal temporary stays (TS) have been established in all municipalities as intermediate care structures to receive mostly older patients who require short-term care that cannot be provided at their own residence. These patients are admitted to TS facilities, usually but not necessarily after hospitalization. This study aims to identify subgroups of medically complex TS patients by analyzing demographic and clinical variables. Methods: We identified a cohort of 11,284 patients with at least one temporary stay across 14 Danish municipalities during 2016-2023. Demographic and clinical information were obtained from Danish administrative and health registries. We employed latent class analysis to identify subgroups of patients. We characterized the subgroups by statistical analysis of patient characteristics and outcomes across subgroups and established patient profiles. Results: We uncovered five patient subgroups: SG1: middle-old multimorbid patients (38%) , SG2: oldest-old women with low multimorbidity (18%) , SG3: younger-old men with low polypharmacy (18%) , SG4: middle-old patients with low hospitalization burden (11%) , SG5: younger-old men with extreme chronic conditions (15%) . Two subgroups – SG1 and SG5 – demonstrated higher 30-day mortality, with the latter having the highest 30-day hospital admission rates. Two other subgroups – SG2 and SG3 – had longer temporary stays and longer survival times. SG4 patients had the shortest temporary stays, low 30-day mortality, and the lowest 30-day hospital admissions. The distribution of morbidities and prescribed drugs across subgroups showed distinct patterns that underscored the different care needs. Conclusion: TS patients are heterogeneous and have complex care needs. We identified five patient groups and analyzed their characteristics, revealing distinct patterns in demographics, history of morbidities, prescribed medication, health care use, and patient outcomes. Our findings suggest that TS patients may benefit from comprehensive geriatric assessment at key transition points and stratified care planning. Municipal care Intermediate Care Temporary stay Older patients Complex patient profile Stratified care Latent class analysis Subgroup analysis Denmark Figures Figure 1 Figure 2 Background As the world’s population is aging, the World Health Organization emphasizes the importance of developing services that provide older-person-centered and integrated care situated as close as possible to where the older people live [ 1 ]. In Denmark, municipal temporary stays (TS) have been established in all municipalities as intermediate care structures to receive mostly older patients, usually, but not necessarily after a period of hospitalization, who require short-term care that cannot be provided at home [ 2 – 4 ]. The TS facilities, like most intermediate care structures, are expected to provide continuity of care and reduce hospital (re)admissions in the face of accelerated hospital discharge [ 2 , 3 , 5 – 9 ]. There are no centralized guidelines for TS operations, and the municipalities independently organize their TS structure and care delivery. TS facilities are not obligated to have physicians on board, and the responsibility for treatment lies with the patients’ general practitioner or the hospital from which they were transferred [ 5 ]. The landscape of intermediate care functions across Western European countries is fragmented. Despite their increasing emergence throughout the past 30 years, there is no consensus on the clinical gap that intermediate care functions fill and how they are to be organized purposefully for the various and complex needs that may adhere to the plethora of patients they serve [ 10 ]. Consequently, data-driven insights about TS patients are also very scarce. A recent study used a cohort of 11,424 TS patients across 14 Danish municipalities to describe their medication use patterns. This study reported high levels of polypharmacy, noticeable prevalence of risk medications, and high diversity of medications used by TS patients [ 11 ]. In another study on the same cohort, baseline patient characteristics and care trajectories, such as morbidity, mortality, and hospital readmissions, were analyzed, and complex patient profiles were reported [ 4 ]. So far, these two were the only quantitative studies conducted on a large cohort of Danish TS patients from multiple municipalities, and both studies uncovered high levels of heterogeneity in a wide range of clinical variables. High heterogeneity among patients may pose challenges to the delivery of effective and coordinated care, especially in intermediate care facilities with generally lower specialization levels and limited resources, such as Danish TS facilities. The mix of multimorbidity, polypharmacy, and social vulnerability, especially when combined with the variability in organizational structures and the absence of standardized protocols, increases the risk of adverse outcomes such as mortality or increased health care use [ 12 – 15 ]. Additionally, a heterogeneous patient population requires a broad range of specialized care and facilities may lack the appropriate staffing mix, leading to inefficiencies and potential decline in care quality [ 16 – 18 ]. These challenges highlight the importance of systematic characterization of patient profiles to provide more insight into the patients’ needs and help decision-makers develop targeted and effective intervention strategies to ensure better care delivery [ 19 – 21 ]. Probabilistic methods such as latent class analysis (LCA) have become popular, especially in the health care domain, for characterizing complex populations by uncovering “hidden” subgroups within the population [ 22 – 29 ]. LCA allows for estimating the probability that an individual belongs to a particular class (subgroup). This provides a more nuanced understanding of subgroup membership compared to commonly used clustering methods that fail to account for uncertainties. Another major strength of LCA is that, by assuming the existence of latent (unobserved) variables that give rise to the observed data, it is particularly useful in medical research where underlying conditions or factors may not be directly measurable or observable. LCA can model complex interactions between variables within each latent class, enabling a more detailed understanding of the relationships within subgroups without extensive model specifications required by regression modelling. Besides, it can simultaneously handle multiple outcome variables, allowing for a comprehensive analysis of patient profiles [ 22 , 30 – 32 ]. In this study, we aim to uncover and characterize subgroups of TS patients based on their demographic and clinical characteristics by employing LCA and interpreting the differences across subgroups. Such characterization of the TS patients will add to the scarce amount of data-driven insights about these patients, especially in terms of the interactions between the demographic variables, indicators of medical history, health care use, and patient outcomes. Such insights could potentially be used to improve and optimize the design and organization of TS facilities. Methods We utilized a cohort of 11,284 patients who had at least one temporary stay within the period from January 1, 2016, to December 31, 2023, across 14 Danish municipalities. Demographic, clinical, and administrative information for each patient in the cohort was obtained from Danish administrative and health registries. Data Dates of TS move-in and move-out were provided by the municipalities together with the unique Central Person Register (CPR) numbers for each patient, which allowed us to link these data with the administrative and health registries at an individual level. Information about dates of emigration, immigration, or death and demographic information, including date of birth and sex, were provided by the Civil Registration System [ 33 ]. Information about dates of hospital contacts and the diagnosis codes according to the International Classification of Diseases, tenth revision (ICD-10) was obtained from the Danish National Patient Registry [ 34 ]. The National Patient Registry also contains data about clinical procedures, e.g., administrative activities, treatments, and operations via the Health Care Classification System (SKS) [ 35 , 36 ]. We utilized the SKS procedure codes to identify surgery events for each patient. Additionally, we obtained information about the filled prescriptions from the Danish National Prescription Registry, which contains data on all drugs dispensed at Danish pharmacies since 1995 [ 37 ]. From this registry, we extracted data on the type of drugs according to the Anatomical Therapeutic Chemical Classification System (ATC) and the date on which the drugs were dispensed. Study population Initially, the cohort included 11,584 adult patients across 14 municipalities with valid temporary stay information whose temporary stay fell completely within the period of 2016–2023. We excluded those who did not have a continuous residence in Denmark during the five years before TS admission (n = 300) due to their incomplete medical records in Danish registries. As a result, 11,284 patients remained in the study cohort. If a patient had multiple temporary stays separated by less than two days, the stays were merged to form a single stay. Additionally, if a patient had multiple temporary stays separated by two or more days, only the first stay was included in this study. Analytic process Using the CPR number of each patient, we linked the data from municipalities to the data from registries. We employed the LCA method to identify the “unobserved” subgroups of temporary stay patients based on demographic and clinical indicators, i.e., observed variables. We chose to focus on variables at high levels of abstraction, such as counts and sums, to capture the general medical characteristics and health care use of our widely heterogeneous cohort. We left lower-level characteristics, such as the exact morbidities and prescribed medication, as “unobserved” variables and analyzed them after identifying the subgroups. Initially, ten categorical variables were derived as candidate indicators for our analysis. The numeric variables were categorized to facilitate interpretability and to meet the requirements of the statistical tools used in this study. The candidate indicators are presented in Table 1 . Table 1 Description of candidate indicators for the latent class analysis. Variable Description Categorical levels sex Sex as assigned at birth female, male age Age at the time of TS admission 18–69, 70–79, 80–89, 90+ multimorb No. unique Charlson morbidities [ 38 ] based on the 5-year diagnosis history 0, 1–2, 3+ CCI Charlson Comorbidity Index [ 39 ] based on the 5-year diagnosis history 0, 1–2, 3+ drugs No. unique filled prescription drugs at the 4th ATC level within one year before TS admission 0–4, 5–9, 10+ surg No. surgeries within one year before TS admission. Excluded SKS codes are presented in supplementary material – Table A1. 0, 1, 2+ IP No. hospitalizations (inpatient contacts) within one year before TS admission 0–1, 2–3, 4+ OP No. outpatient hospital contacts within 1 year before TS admission 0–1, 2–3, 4+ LH No. days between the end of the previous hospitalization and TS admission 0, 1–6, 7+ surg_1w Whether the patient had undergone surgery during a hospitalization that ended within seven days before TS admission yes, no Correlation measures between each pair of indicators were calculated to avoid including highly correlated indicators in the model [ 31 , 32 ]. Depending on the type of indicator pairs, i.e., categorical-categorical, categorical-binary, or binary-binary, we used Spearman’s rank correlation, point-biserial correlation, or phi coefficient, respectively. Consequently, the pairs with correlations ≥ 0.3 were marked for further investigation. From each pair of marked indicators, one of them was excluded by the consensus of the authors. Given that there are no fixed methods for specifying the “optimal” LCA model [ 31 , 32 ], we iteratively fitted models with 2–10 latent classes and selected the one with minimum Bayesian information criterion (BIC) as the optimal model. The minimum BIC value ensures that the model is the most likely to fit the data yet is not too complex. Additionally, we made sure that the model converges and that the entropy is at least 0.8 since high entropy is essential for a model to generate well-separated and distinct subgroups [ 31 , 32 ]. After selecting the optimal model, the prevalences of each categorical level of variables were calculated for each subgroup and the whole cohort. Mean and standard deviation (SD) were also calculated for non-binary variables at the numeric level. The Chi-square test was used to evaluate the differences in binary variables among the subgroups, considering \(\:p<0.05\) as significant. Following a significant Chi-square test, we performed pairwise comparisons with Bonferroni correction to identify differences between the latent classes. Effect sizes for binary variables were calculated using Cohen’s h [ 40 ]. For non-binary variables, the differences among subgroups were evaluated using the Kruskal-Wallis test with \(\:p<0.05\) considered significant. Post hoc contrasts were made using Dunn’s test with Bonferroni-adjusted p-values, and the effect sizes were calculated as \(\:r=\:Z/\sqrt{N}\) where Z is the Dunn’s test statistic and N is the sample size. We used non-parametric statistical tests due to the non-normal distribution of variables and the violation of the homogeneity of variances. Additionally, prevalence proportions of morbidities and selected filled prescriptions were calculated. When analyzing the distribution of morbidities across subgroups, besides the Charlson morbidity mappings, alcohol abuse was added to the morbidity set according to the Elixhauser ICD-10 mapping [ 38 ]. To analyze the distribution of prescribed medications across subgroups, we used the ATC mappings in supplementary material –Table A2. We used logistic regression to investigate the association of subgroup membership with the history of individual morbidities and prescribed drug categories. Odds ratios and 95% confidence intervals (CI) described the association of membership in a given subgroup with history of morbidities/drugs compared to all other patients in the cohort. We also calculated the prevalence proportion of recent fall injuries before temporary stay as a measure of frailty. The ICD-10 mappings for fall injuries are provided in supplementary material –Table A3. A recent fall injury was defined as a fall injury diagnosed during a hospitalization that ended within seven days before admission to TS. Similarly, prevalence proportions of recent surgeries were calculated. To analyze the patient outcomes, median and interquartile range (IQR) for length of stay (LOS) at TS, median and 95% CIs of LOS adjusted for death at TS by censoring, median time-to-death (survival time) after temporary stay, 30-day mortality rates, median time-to-hospitalization from the start of the temporary stay, and 30-day hospitalization rate were calculated for each subgroup and the whole cohort. The adjusted median LOS and the median time to death were calculated using the Kaplan-Meier method. The CIs were calculated using the log-minus-log transformation method [ 41 ]. Median time-to-hospitalization, considering death as a competing risk, was calculated using the Aalen-Johansen method for estimating the cumulative incidence function (CIF) [ 42 ]. To construct the 95% CIs, we employed bootstrapping with 1000 resamples. The statistical tests for evaluating the differences in 30-day mortality and 30-day hospital admission were the same as the previously mentioned tests for binary variables. All calculations and analyses were performed using R version 4.3.3. The latent class analysis was done using the poLCA package version 1.6.0.1 [ 43 ], and each model (k = 2, 3, …, 10) was fit with 20,000 maximum iterations and 20 repetitions with different random starting points to ensure that the underlying expectation maximization and Newton-Raphson algorithms locate the global maximum. Results We identified five subgroups of TS patients that can briefly be described as SG1: Middle-old multimorbid patients (38%) , SG2: Oldest-old women with low multimorbidity (18%) , SG3: Younger-old men with low polypharmacy (18%) , SG4: Middle-old patients with low hospitalization burden (11%) , SG5: Younger-old men with extreme chronic conditions (15%) . The results of indicator and model selection and the corresponding model fit statistics are reported in supplementary material –Tables A4 and A5. Description and statistical analysis of the baseline characteristics of the subgroups and the full study population are presented in Table 2 and Table 3 . Table 2 Counts (prevalence proportions %) of categorical representation of baseline demographic and clinical characteristics. SG 1 SG 2 SG 3 SG 4 SG 5 Full Cohort Size: n(%) 4249 (37.7) 2255 (20.0) 1932 (17.1) 1207 (10.7) 1641 (14.5) 11284 (100) Sex Female 2474 (58) 1994 (88) 488 (25) 612 (51) 508 (31) 6076 (54) Male 1775 (42) 261 (12) 1444 (75) 595 (49) 1133 (69) 5208 (46) Age, years [18–69] 272 (6) 36 (2) 901 (47) 126 (10) 656 (40) 1991 (18) [70–79] 927 (21) 249 (11) 604 (31) 305 (25) 710 (43) 2795 (25) [80–89] 1977 (47) 868 (38) 344 (18) 477 (40) 262 (16) 3928 (35) [90+] 1073 (25) 1102 (49) 83 (4) 299 (25) 13 (1) 2570 (23) No. Charlson morbidities, 5 years before TS admission 0 281 (7) 1459 (65) 674 (35) 484 (40) 119 (7) 3017 (27) [ 1 – 2 ] 2976 (70) 796 (35) 1214 (63) 634 (53) 709 (43) 6329 (56) >= 3 992 (23) 0 (0) 44 (2) 89 (7) 813 (50) 1938 (17) No. unique drugs, 4th ATC level, 1 year before TS admission [0–4] 0 (0) 561 (25) 1066 (55) 273 (23) 19 (1) 1919 (17) [ 5 – 9 ] 925 (22) 1305 (58) 866 (45) 504 (42) 277 (17) 3877 (34) >= 10 3324 (78) 389 (17) 0 (0) 430 (36) (82) 5564 (49) No. inpatient hospital contacts, 1 year before TS admission [0–1] 201 (5) 361 (16) 166 (9) 1095 (91) 12 (1) 1835 (16) [ 2 – 3 ] 1478 (35) 1395 (62) 874 (45) 112 (9) 26 (2) 3885 (34) >= 4 2570 (60) 499 (22) 892 (46) 0 (0) 1603 (98) 5564 (49) No. outpatient hospital contacts, 1 year before TS admission [0–1] 826 (19) 1102 (49) 782 (40) 526 (44) 111 (6) 3347 (30) [ 2 – 3 ] 751 (18) 521 (23) 445 (23) 258 (21) 124 (8) 2099 (18) >= 4 2672 (63) 632 (28) 705 (36) 423 (35) 1406 (86) 5838 (52) No. surgeries, 1 year before TS admission 0 2496 (59) 1346 (60) 1143 (59) 1088 (90) 179 (11) 6252 (55) 1 937 (22) 703 (31) 465 (24) 58 (5) 291 (18) 2454 (22) >= 2 816 (19) 206 (9) 324 (17) 61 (5) 1171 (71) 2578 (23) No. days between the end of the previous hospitalization and TS admission >= 7 702 (17) 94 (4) 143 (7) 1040 (86) 156 (10) 2135 (19) [ 1 – 6 ] 607 (14) 189 (8) 149 (8) 160 (13) 218 (13) 1323 (12) 0 2840 (69) 1972 (87) 1640 (85) 7 (1) 1267 (77) 7826 (69) Table 3 Analysis of the differences in patient characteristics. Numeric variables are presented as mean (standard deviation). SG1 SG2 SG3 SG4 SG5 Full Cohort Statistics No. Patients 4249 2255 1932 1207 1641 11284 Male, n (%) 1775 (41.8) 261 (11.6) 1444 (74.7) 595 (49.3) 1133 (69.0) 5208 (46.2) \(\:{\chi\:}^{2}\left(4\right)\) = 2103.8 p 1,4 > 2 * Age, years 82.4 (8.4) 86.9 (7.3) 70.5 (12.0) 81.3 (9.5) 71.0 (10.2) 79.5 (11.2) \(\:{\chi\:}^{2}\left(4\right)\) = 3598.7 p 1,4 > 3,5 ** No. Charlson morbidities, 5 years 1.80 (1.13) 0.44 (0.65) 0.90 (0.82) 0.95 (1.01) 2.53 (1.52) 1.39 (1.27) \(\:{\chi\:}^{2}\left(4\right)\) = 3710.4 p 1 > 3,4 > 2 ** CCI, 5 years 1.91 (2.07) 0.45 (1.04) 1.00 (1.75) 1.17 (1.64) 2.88 (2.70) 1.52 (2.08) \(\:{\chi\:}^{2}\left(4\right)\) = 2193 p 1 > 3,4 > 2 ** No. unique drugs, 4th ATC level, 1 year 13.00 (4.78) 6.95 (3.82) 4.40 (2.73) 8.31 (4.71) 14.23 (5.59) 10.00 (5.76) \(\:{\chi\:}^{2}\left(4\right)\) = 5303.5 p 2,4 > 3 ** No. inpatient contacts, 1 year 5.28 (3.98) 2.83 (1.90) 4.29 (3.45) 0.45 (0.76) 9.74 (6.81) 4.75 (4.69) \(\:{\chi\:}^{2}\left(4\right)\) = 5047 p 1,3 > 2 > 4 ** No. outpatient contacts, 1 year 8.95 (13.05) 3.17 (5.26) 5.11 (10.39) 4.00 (6.19) 19.18 (24.10) 8.10 (14.45) \(\:{\chi\:}^{2}\left(4\right)\) = 2358.2 p 1 > 2,3,4 ** No. of surgeries, 1 year 0.95 (1.84) 0.62 (1.12) 0.91 (2.07) 0.25 (1.01) 3.66 (4.21) 1.20 (2.46) \(\:{\chi\:}^{2}\left(4\right)\) = 2648.6 p 1,2,3 > 4 ** No. days between the previous hospital discharge and TS admission 5.7 (21.7) 2.1 (15.4) 2.2 (13.3) 266.7 (498.3) 2.6 (9.9) 31.8 (182.8) \(\:{\chi\:}^{2}\left(4\right)\) = 4299.2 p 1 > 2,3 ** 4 > 5 ** Recent surgery, n (%) 648 (15.3) 577 (25.6) 358 (18.5) 6 (0.5) 488 (29.7) 2077 (18.4) \(\:{\chi\:}^{2}\left(4\right)\) = 503.7 p 1,3 > 4 * Recent fall injury, n (%) 580 (13.7) 704 (31.2) 293 (15.2) < 5 173 (10.5) 1752 (15.5) \(\:{\chi\:}^{2}\left(4\right)\) = 683.2 p 1,3,5 > 4 * * Bonferroni-adjusted pairwise Chi-Square p 0.2 ** Bonferroni-adjusted pairwise Dunn’s p 0.1 Characterization of subgroups SG1: Middle-old multimorbid patients This subgroup accounts for the largest portion of the patients (38%), with a median age of 83.4 ( \(\:IQR=77.8-88.0\) ; 72% ≥ 80). As shown in Table 2 and Table 3 , this patient group showed a relatively high level of multimorbidity. Similar patterns of high polypharmacy and high frequency of hospitalization were identified in this subgroup. These patients had a moderate frequency of undergoing surgery. They also had a relatively high prevalence of most morbidities provided in Table 4 . Membership in this subgroup was associated with increased odds of having almost all morbidities compared to other patients. The exceptions were liver disease and alcohol abuse, as illustrated in Fig. 1. High prevalences of almost all prescribed drug categories were also observed in this patient group. In fact, Fig. 2 demonstrates that patients in this subgroup had higher odds of having a history of all prescription categories except for anti-dementia drugs. An important characteristic of these patients was the shorter survival time. Table 5 shows that 17% of the patients in this subgroup died within 30 days after beginning their temporary stay, and the median time-to-death was 15.2 months (95% CI: 14.2–16.5). SG2: Oldest-old women with low multimorbidity With a median age of 87.8 ( \(\:IQR=82.3-92.2;49\%\:\ge\:90\) ), this mostly female (88%) subgroup was relatively older compared to others. These patients had a highlighted prevalence of direct transfer from hospital to TS (87%). The lowest multimorbidity was observed in this subgroup. They had relatively moderate polypharmacy and relatively lower frequencies of hospitalization and surgery. However, this subgroup had one of the highest prevalences of recent surgery before TS admission (26%). All morbidities had very low prevalences, and in general, membership in this subgroup was associated with reduced odds of all morbidities compared to other patients. All medication categories had relatively low prevalence except for sex hormones and drugs used in thyroid therapy. Another notable characteristic of this subgroup is the highlighted prevalence of recent fall injuries (31%) compared to other subgroups. Regarding the patient outcomes, these patients had longer stays at TS, and relatively moderate survival time and hospital admission rate. 11% died within 30 days after admission to TS, and 20% were hospitalized within the same period. SG3: Younger-old men with low polypharmacy This predominantly male (75%) subgroup had a median age of 71.3 ( \(\:IQR=63.4-78.3;78\%\:\le\:79)\) . These patients had a highlighted prevalence of direct transfer from hospital to TS (85%). Moderate multimorbidity and the lowest level of polypharmacy were observed in this subgroup. Most morbidities had low prevalence, and membership in this subgroup was associated with reduced odds of all morbidities except alcohol abuse, liver disease, and cerebrovascular disease. All prescribed drug categories had low prevalences, and most of them were the least prevalent among all subgroups. Longer stays at TS, high survival times, and moderate hospital admission rates were important characteristics of this subgroup regarding patient outcomes. 8% of these patients died within 30 days after TS admission, and 24% were hospitalized. SG4: Middle-old patients with low hospitalization burden With an even distribution of males and females and a median age of 82.4 ( \(\:IQR=75.9-87.6;65\%\ge\:\:80\) ), this patient group was the smallest one in size (11% of the cohort). Direct transfers to TS from hospitals were almost nonexistent (0.6%), and 85% of these patients did not experience hospitalization during the week leading to TS admission. In fact, the hospitalization frequency in this subgroup was the lowest among all subgroups. Almost 91% of them had zero or one hospitalization on their one-year record, and none had four or more hospitalizations. Similarly, the lowest frequency of surgery belonged to this subgroup, as 90% did not undergo any surgeries during the year before TS admission. Despite a very low frequency of hospitalization, these patients had moderate levels of outpatient hospital contact. Also, relatively moderate levels of multimorbidity and polypharmacy were observed in this subgroup. Most morbidities in this subgroup had relatively low prevalence, except dementia, which was highlighted with a prevalence of 26% and a strong association with membership in this subgroup. The prescribed drug categories that had increased odds associated with membership in this subgroup were antidementia drugs, antipsychotics, anti-Parkinson drugs, antidepressants, and urologicals. These patients had the lowest prevalence of recent fall injuries and recent surgeries. They also had the shortest median length of stay at TS, moderate survival times, and the lowest hospital admission rate. 9% died within 30 days after TS admission, and 16% were hospitalized. SG5: Younger-old men with extreme chronic conditions This predominantly male (69%) subgroup had a median age of 72.6 ( \(\:IQR=65.4-77.6;83\%\:\le\:\) 79), which makes it one of the youngest subgroups. We observed extreme levels of multimorbidity, polypharmacy, frequency of hospitalization, and frequency of surgery. Membership in this subgroup was associated with increased odds of history of all morbidities except dementia. Especially, highlighted prevalences of diabetes, liver disease, renal disease, cancer, and peripheral vascular disease can be indicative of severe chronic burden in this patient group. Additionally, this subgroup had a high prevalence of most prescribed drugs. The most distinctively lower prevalences belonged to anti-dementia drugs and sex hormones. This subgroup had relatively short survival times and the highest hospital admission rate. 15% died within 30 days after TS admission, and 40% were hospitalized in that period. Table 4 Counts and prevalence proportions (%) of morbidities and selected prescribed drug categories. SG1 SG2 SG3 SG4 SG5 Full Cohort No. Patients 4249 2255 1932 1207 1641 11284 5-year Morbidities Myocardial Infarction 240 (6) 27 (1) 42 (2) 16 (1) 168 (10) 493 (4) Congestive Heart Failure 684 (16) 64 (3) 57 (3) 52 (4) 330 (20) 1187 (11) Peripheral Vascular Disorders 518 (12) 44 (2) 92 (5) 57 (5) 350 (21) 1061 (9) Cerebrovascular Disease 1284 (30) 296 (13) 515 (27) 204 (17) 497 (30) 2796 (25) Diabetes (uncomplicated) 833 (20) 59 (3) 133 (7) 91 (8) 521 (32) 1637 (15) Diabetes (complicated) 301 (7) 10 (< 1) 35 (2) 31 (3) 338 (21) 715 (6) Chronic Pulmonary Disease 1025 (24) 76 (3) 131 (7) 108 (9) 424 (26) 1764 (16) Rheumatoid Disease 222 (5) 31 (1) 28 (1) 36 (3) 78 (5) 395 (4) Peptic Ulcer Disease 190 (4) 18 (< 1) 45 (2) 22 (2) 92 (6) 367 (3) Renal Disease 426 (10) 31 (1) 45 (2) 34 (3) 275 (17) 811 (7) Metastatic Solid Tumor 213 (5) 15 (< 1) 65 (3) 27 (2) 163 (10) 483 (4) Malignancy 1029 (24) 147 (7) 273 (14) 141 (12) 575 (35) 2165 (19) Mild Liver Disease 107 (3) 9 (< 1) 88 (5) 8 (< 1) 150 (9) 362 (3) Moderate/Severe Liver Disease 41 (1) < 5 41 (2) < 5 69 (4) 156 (1) Dementia 501 (12) 161 (7) 133 (7) 309 (26) 87 (5) 1191 (11) Alcohol Abuse 171 (4) 49 (2) 336 (17) 39 (3) 237 (14) 832 (7) 1-year Prescribed Drug Classes Drugs used in diabetes 1030 (24) 127 (6) 167 (9) 161 (13) 590 (36) 2075 (18) Antithrombotic agents 3018 (71) 910 (40) 573 (30) 599 (50) 1095 (67) 6195 (55) Antianemic preparations 859 (20) 268 (12) 139 (7) 177 (15) 347 (21) 1790 (16) Drugs used in cardiac therapy 859 (20) 184 (8) 49 (3) 106 (9) 299 (18) 1496 (13) Diuretics 2444 (58) 770 (34) 282 (15) 377 (31) 882 (54) 4757 (42) Vasoprotectives 222 (5) 64 (3) 27 (1) 37 (3) 76 (5) 426 (4) Beta-blocking agents 1806 (43) 539 (24) 282 (15) 297 (25) 728 (44) 3652 (32) Calcium channel blockers 1414 (33) 610 (27) 343 (18) 310 (26) 548 (33) 3225 (29) Agents acting on renin-angiotensin system 2159 (51) 875 (39) 535 (28) 429 (36) 844 (51) 4842 (43) Lipid-modifying agents 2067 (49) 547 (24) 497 (26) 402 (33) 889 (54) 4402 (39) Sex hormones 504 (12) 249 (11) 27 (1) 69 (6) 110 (7) 959 (8) Urologicals 723 (17) 197 (9) 182 (9) 200 (17) 316 (19) 1618 (14) Drugs used in thyroid therapy 484 (11) 226 (10) 52 (3) 108 (9) 133 (8) 1003 (9) Opioids 2016 (47) 651 (29) 290 (15) 307 (25) 923 (56) 4187 (37) Antiepileptics 267 (6) 67 (3) 96 (5) 69 (6) 142 (9) 641 (6) Antiparkinson drugs 340 (8) 89 (4) 59 (3) 107 (9) 108 (7) 703 (6) Antipsychotics 445 (10) 113 (5) 118 (6) 164 (14) 203 (12) 1043 (9) Anxiolytics 510 (12) 160 (7) 96 (5) 94 (8) 230 (14) 1090 (10) Hypnotics and sedatives 912 (21) 279 (12) 124 (6) 170 (14) 414 (25) 723 (17) Antidepressants 1358 (32) 455 (20) 282 (15) 402 (33) 546 (33) 3043 (27) Antidementia drugs 345 (8) 128 (6) 92 (5) 293 (24) 41 (2) 899 (8) Drugs for obstructive airway disease 1260 (30) 203 (9) 166 (9) 175 (14) 487 (30) 2291 (20) All overall Chi-square tests for each morbidity/drug category had p < 0.0001. Table 5. Patient outcomes for each subgroup and the full cohort. SG 1 SG 2 SG 3 SG 4 SG 5 Full Cohort N (%) 4249 (37.7) 2255 (20.0) 1932 (17.1) 1207 (10.7) 1641 (14.5) 11284 (100) Length of stay, days Median (IQR) 23 (11–46) 28 (15–50) 28 (14–56) 20 (7–52) 24 (12–48) 25 (12–49) Length of stay – adjusted for death at MIDO, days Median (95% CI) 28 (27–28) 31 (30–33) 30 (28–32) 23 (20–25) 28 (27–31) 28 (28–29) 30-day mortality n (%) 715 (16.8) 241 (10.7) 156 (8.1) 114 (9.4) 253 (15.4) 1479 (13.1) \(\:{\chi\:}^{2}\left(4\right)=\) 128.1, p 2,3,4** Survival time, months Median (95% CI) 15.2 (14.2–16.5) 29.5 (27.2–31.6) 44.7 (41.2–49.7) 26.1 (23.5–28.2) 18.2 (15.4–21.3) 23.8 (22.8–24.8) 30-day hospitalization n (%) 1178 (27.7) 462 (20.5) 463 (24.0) 194 (16.1) 661 (40.3) 2957 (26.2) \(\:{\chi\:}^{2}\left(4\right)=\) 280.3, p 1,2,3,4**; 1 > 2**; 1,3 > 4**; 2 > 4*; 1 > 3* Time-to-hospitalization, months Median (95% CI) 5.2 (4.7–5.7) 12.4 (10.8–14.2) 8.7 (7.8–10.2) 16.3 (14.4–19.9) 1.9 (1.6–2.3) 7.0 (6.5–7.5) LOS = length of stay at TS; IQR = interquartile range; aLOS = length of stay adjusted for death at TS Pairwise p-values are Bonferroni-adjusted. * p < 0.05; ** p < 0.0001 Discussion The variation observed across subgroups reinforces earlier findings that intermediate care populations are clinically diverse and may not fit neatly into standardized care pathways [ 44 – 47 ]. For instance, SG1 and SG5 had high levels of multimorbidity and polypharmacy, shorter survival times, and frequent hospital contacts. In contrast, SG2 exhibited a high prevalence of recent fall injuries but a relatively lower chronic disease burden. SG3 and SG4 captured profiles with lower overall disease burden, including patients with indicators of alcohol abuse (SG3) and low hospitalization frequency (SG4). This heterogeneity highlights the potential value of stratifying care approaches to better align with patient needs, which is also supported by findings from intermediate care settings in other European countries [ 48 – 50 ]. Rather than applying a one-size-fits-all model of care, municipalities could consider using insights from subgroup patterns to inform differentiated care planning and resource prioritization, in line with the intended flexibility of TS as an intermediate care model. To support such tailored care, the integration of comprehensive geriatric assessment (CGA) at key transition points – ideally at hospital discharge or upon arrival at TS – may be particularly valuable by enabling more individualized and proactive care planning [ 51 – 54 ]. Differences in clinical and functional complexity across TS patient subgroups underscore the importance of aligning staffing models and care competencies with subgroup-specific needs [ 55 , 56 ]. For instance, patients in SG1 and SG5 exhibited high levels of polypharmacy and chronic burden, indicating a need for enhanced medical oversight and structured medication review. Studies from intermediate care settings have shown that pharmacist-led reviews can effectively identify and resolve drug-related problems with high implementation rates of medication changes [ 57 ]. Broader multicomponent interventions that include medication reconciliation, patient education, and transitional follow-up have also been associated with significantly reduced hospital readmissions [ 58 ], which may especially be helpful for SG5 patients with extreme 30-day hospital readmissions (40%). Other patients, such as those in SG2, may benefit from a greater focus on rehabilitation and fall prevention strategies like multifactorial fall prevention interventions, including tailored exercise and environmental risk assessments, which have been shown to significantly reduce fall incidence in institutional settings [ 59 ]. SG3 patients, characterized by lower polypharmacy but increased prevalence of alcohol abuse, liver disease, and cerebrovascular disease, highlight the need for specialized care planning that addresses cognitive or behavioral health needs and provides proactive behavioral health support [ 60 , 61 ]. Conversely, SG4 included patients with minimal hospital contact, increased prevalence of dementia, and shorter TS stays, raising questions about whether specific admissions could be better managed through enhanced home care or outpatient rehabilitation [ 62 , 63 ]. For instance, a study in the United States showed that Medicare patients with dementia had no differences in readmission or mortality whether receiving post-acute care via home health versus a skilled nursing facility [ 64 ]. Our findings offer novel insights that enhance our understanding of the medical complexity associated with intermediate care patient populations, which are relatively scarce. Currently, scholarly contributions struggle to define and specify how intermediate care functions are to be organized in accordance with patient populations and their mixed characteristics [ 10 ]. These challenges are potentially highlighted by the fact that intermediate care functions are often located in decentralized units presenting their own care environment [ 65 ]. In Denmark, TS facilities governed by municipalities operate within The Social Service Act and do not have health care mandate (§ 84.2) [ 66 ]. In practice, this means that neither doctors nor nurses are mandated. Further, the main staff – nurse assistants – do not have access to medication, pharmacists’ expertise, or patient records. Staff potentially lack basic and crucial information for enabling continuous support and care throughout the patient’s treatment scheme and trajectory. Thus, the access to medical competence and resources necessary to act timely and in accordance with the complex needs of these patient subgroups – particularly SG1 and SG5 – may be compromised, which may represent a hazard to patient safety and a challenge to promote continuity. More recent publications have highlighted that intermediate care practices may present patients with remarkably different environments, which has implications for patient safety, continuity of care and health disparities [ 67 , 68 ]. Such differences have earned less attention in research. Therefore, evidence elucidating complexities in intermediate care patient populations can also be helpful in the future design of intermediate care organizations. Strengths and limitations Our study utilized a large cohort of TS patients from 14 municipalities distributed across four out of five regions of Denmark (see supplementary material –Table A6). Although two other studies have used a similarly large cohort to describe medication use, patient trajectories, and baseline characteristics of TS patients [ 4 , 11 ], our identification and characterization of patient subgroups shed light on interactions of demographic and clinical indicators and patient outcomes in a nuanced manner. Another point of strength in our study is that the seven observed variables based on which the subgroups were identified are simple and easily interpretable. Although we excluded variables at a lower level of abstraction, such as the exact morbidities, prescribed medication, and recent fall injuries from the set of observed indicators, we later analyzed these unobserved variables, uncovering distinct patterns across different subgroups that further underscores the ability of the model to capture complex interactions between patient variables. We also refrained from relying on p-values as the only marker of difference across subgroups and employed effect size measures and confidence intervals where appropriate to enable more practical interpretations of the contrasts compared to relying solely on statistical significance. This study has limitations that must be acknowledged. An important limitation is the absence of socio-economic variables in the study, which could unravel more insights about the TS population and their care needs. Furthermore, in LCA, like most unsupervised methods for subgroup analysis, deciding on the number of subgroups is mainly based on statistical metrics that may not have robust clinical justification [ 31 , 32 ]. Although criteria such as minimum BIC and high entropy provide a reasonable statistical basis for determining the “optimal” number of subgroups, other models without the minimum BIC value may also be clinically relevant. In addition, as subgroup assignments are probabilistic, a clinician may find it challenging to determine precisely to which subgroup an individual patient belongs. However, overall patterns in patient variables across subgroups can still provide meaningful insights about the characteristics of each subgroup. Conclusion Our study identified five subgroups of TS patients, each representing a distinct sub-profile of patient characteristics and outcomes, reflecting substantial heterogeneity in demographic characteristics, clinical complexity, and care needs. These differences may hold important implications for the organization and delivery of care in TS settings, particularly regarding the balance between standardization and individualized support. Abbreviations TS Temporary stay LCA Latent class analysis CPR Central person register ICD International classification of diseases SKS Sundhedsvæsenets klassifikationssystem (Health care classification system) ATC Anatomical therapeutic chemical classification BIC Bayesian information criterion sd Standard deviation CI Confidence interval IQR Interquartile range LOS Length of stay aLOS Adjusted length of stay SG Subgroup CCI Charlson Comorbidity Index CGA Comprehensive geriatric assessment Declarations This research project has been conducted in accordance with the principles of the Helsinki Declaration and further adheres to the legal requirements of the study country. This study was register-based, only anonymized data was used, data is presented in aggregate and anonymous form, and study participants were not contacted nor required any active participation. According to Danish law, approval from an ethics committee and informed consent to participate are not required for register-based studies (section 14.2 of the Act on Research Ethics Review of Health Research Projects and section 10 of the Data Protection Act). In terms of data protection, the study was registered at the University of Southern Denmark inventory (record no. 11.436). Acknowledgements Not applicable. Authors’ contribution MR, KK, and KE designed the study. MR performed the statistical analysis and wrote the manuscript. MR and RFG interpreted the results. RFG, MS, and OA contributed to writing the discussion. KK, KE, RFG, MS, OA, and AP have read and commented on the manuscript. All authors have read and approved the final version of the manuscript for submission. Funding The study was funded by Novo Nordisk Foundation (grant number 0075454). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Availability of data and material Danish legislation does not allow individual-level data to be publicly available. Anonymized data can be accessed by authorized researchers after application to Forskeservice at the Danish Health Data Authority. MR had full access to the data. Coding scripts for the analysis are available upon request. Ethics approval and consent to participate This research project has been conducted in accordance with the principles of the Helsinki Declaration and further adheres to the legal requirements of the study country. This study was register-based, only anonymized data was used, data is presented in aggregate and anonymous form, and study participants were not contacted nor required any active participation. According to Danish law, approval from an ethics committee and informed consent to participate are not required for register-based studies (section 14.2 of the Act on Research Ethics Review of Health Research Projects and section 10 of the Data Protection Act). In terms of data protection, the study was registered at the University of Southern Denmark inventory (record no. 11.436). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. 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Int J Nurs Stud. 2020;106:103564. https://doi.org/10.1016/j.ijnurstu.2020.103564 . Plys E, Levy CR, Brenner LA, Vranceanu A-M. Let’s Integrate! The Case for Bringing Behavioral Health to Nursing Home–Based Post-Acute and Subacute Care. J Am Med Dir Assoc. 2022;23:1461–e14677. https://doi.org/10.1016/j.jamda.2022.06.004 . Bucy TI, Cross DA. Information sharing to support care transitions for patients with complex mental health and social needs. J Am Geriatr Soc. 2023;71:1963–73. https://doi.org/10.1111/jgs.18278 . McGarry BE, Grabowski DC, Ding L, McWilliams JM. Outcomes After Shortened Skilled Nursing Facility Stays Suggest Potential For Improving Postacute Care Efficiency: Study examines the impact of shortened skilled nursing facility stays. Health Aff (Millwood). 2021;40:745–53. https://doi.org/10.1377/hlthaff.2020.00649 . Mas MÀ, Inzitari M, Sabaté S, Santaeugènia SJ, Miralles R. Hospital-at-home Integrated Care Programme for the management of disabling health crises in older patients: comparison with bed-based Intermediate Care. Age Ageing. 2017;46:925–31. https://doi.org/10.1093/ageing/afx099 . Burke RE, Xu Y, Ritter AZ, Werner RM. Postacute care outcomes in home health or skilled nursing facilities in patients with a diagnosis of dementia. Health Serv Res. 2022;57:497–504. https://doi.org/10.1111/1475-6773.13855 . Agerholm J, Pulkki J, Jensen NK, Keskimäki I, Andersen I, Burström B, et al. The organisation and responsibility for care for older people in Denmark, Finland and Sweden: outline and comparison of care systems. Scand J Public Health. 2024;52:119–22. https://doi.org/10.1177/14034948221137128 . Ministry of Social Affairs. Bekendtgørelse af lov om social service [Executive Order on the Act on Social Services]. 2002. https://www.retsinformation.dk/eli/lta/2002/755 (accessed May 5, 2025). Agerholm J, Pulkki J, Jensen NK, Keskimäki I, Andersen I, Burström B, et al. The organisation and responsibility for care for older people in Denmark, Finland and Sweden: outline and comparison of care systems. Scand J Public Health. 2024;52:119–22. https://doi.org/10.1177/14034948221137128 . Liljas AEM, Pulkki J, Jensen NK, Jämsen E, Burström B, Andersen I, et al. Opportunities for transitional care and care continuity following hospital discharge of older people in three Nordic cities: A comparative study. Scand J Public Health. 2024;52:5–9. https://doi.org/10.1177/14034948221122386 . Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.docx Cite Share Download PDF Status: Published Journal Publication published 30 Apr, 2026 Read the published version in BMC Health Services Research → Version 1 posted Editorial decision: Revision requested 18 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 26 Dec, 2025 Reviews received at journal 15 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor assigned by journal 18 Jun, 2025 Editor invited by journal 28 May, 2025 Submission checks completed at journal 28 May, 2025 First submitted to journal 28 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6744694","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473014316,"identity":"a225f175-3a5f-40fd-93cf-45c7863971b1","order_by":0,"name":"Mahan Rajaeigolsefidi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYFACHhC2YWwAUpINJGhJY+whUQvDYRK06LafPfbhh8x52f0SCYw3ZxCjxexMXvLMHp7bxj0SCcyWG4jSciDHmIGH53YiUAub5AOitJx/Y8z4h+ccKVpu5Bgz8/AcgGghzmE33hgzy/AkG/ecedhsSZz3z+cYM77tsZNtb08+eLOHGC1gAI4UBnACIBr8IEXxKBgFo2AUjDgAAJ3JMoLfZ1QkAAAAAElFTkSuQmCC","orcid":"","institution":"Technical University of Denmark","correspondingAuthor":true,"prefix":"","firstName":"Mahan","middleName":"","lastName":"Rajaeigolsefidi","suffix":""},{"id":473014317,"identity":"03bdc53e-1c5b-4de5-be3a-807186540794","order_by":1,"name":"Rebecca Futtrup Gantriis","email":"","orcid":"","institution":"Technical University of Denmark","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"Futtrup","lastName":"Gantriis","suffix":""},{"id":473014318,"identity":"bd5c33ad-0c68-43e9-a2ae-6c69f3448559","order_by":2,"name":"Kasper Edwards","email":"","orcid":"","institution":"Technical University of Denmark","correspondingAuthor":false,"prefix":"","firstName":"Kasper","middleName":"","lastName":"Edwards","suffix":""},{"id":473014319,"identity":"925634e1-7e8b-47a8-8d83-d9a71e423b55","order_by":3,"name":"Martin Schultz","email":"","orcid":"","institution":"Amager and Hvidovre Hospital","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Schultz","suffix":""},{"id":473014320,"identity":"da2a4855-884f-442d-952b-b7d2f12648cb","order_by":4,"name":"Ove Andersen","email":"","orcid":"","institution":"Amager and Hvidovre Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ove","middleName":"","lastName":"Andersen","suffix":""},{"id":473014321,"identity":"2dab7df2-d09e-4d38-95a6-cee4caeacfbd","order_by":5,"name":"Anton Pottegård","email":"","orcid":"","institution":"University of Southern Denmark","correspondingAuthor":false,"prefix":"","firstName":"Anton","middleName":"","lastName":"Pottegård","suffix":""},{"id":473014322,"identity":"6e4dfb05-0642-45e8-b018-18fb1132f917","order_by":6,"name":"Kathrin Kirchner","email":"","orcid":"","institution":"Technical University of Denmark","correspondingAuthor":false,"prefix":"","firstName":"Kathrin","middleName":"","lastName":"Kirchner","suffix":""}],"badges":[],"createdAt":"2025-05-25 15:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6744694/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6744694/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12913-026-14583-2","type":"published","date":"2026-04-30T15:58:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85304217,"identity":"d16b7c02-df88-4299-806e-6b8b40533ed9","added_by":"auto","created_at":"2025-06-24 12:35:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57486,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of subgroup membership with history of morbidities (5 years).\u003c/p\u003e\n\u003cp\u003eLegend: Each subgroup was compared to all other patients in the cohort using logistic regression. Liver disease: mild, moderate, or severe. Cancer: metastatic solid tumor, or any other malignancy. Diabetes: with or without chronic complications.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6744694/v1/815c063dc807f5572db095af.png"},{"id":85305793,"identity":"ac0a0ef7-fa8e-4eca-b258-a12a1f054c74","added_by":"auto","created_at":"2025-06-24 12:43:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70005,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of subgroup membership with history of prescribed drugs (1 year).\u003c/p\u003e\n\u003cp\u003eLegend: Each subgroup was compared to all other patients in the cohort using logistic regression.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6744694/v1/5cf066fcd4e2ead8f5c1a955.png"},{"id":108438960,"identity":"497fca14-0542-40b5-859d-43aed6ac46a9","added_by":"auto","created_at":"2026-05-04 16:12:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":927952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6744694/v1/f5ae406d-8c53-46a2-b7d1-05db36951599.pdf"},{"id":85304220,"identity":"598d2ab1-a8d8-496d-a1d3-d13478c2cd63","added_by":"auto","created_at":"2025-06-24 12:35:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22033,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6744694/v1/e9d64a430538ae1f4ab8b5dd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying and Characterizing Subgroups of Medically Complex Older Patients in Community-based Intermediate Care: A Latent Class Analysis of Danish Municipal Temporary Stay Patients","fulltext":[{"header":"Background","content":"\u003cp\u003eAs the world\u0026rsquo;s population is aging, the World Health Organization emphasizes the importance of developing services that provide older-person-centered and integrated care situated as close as possible to where the older people live [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Denmark, municipal temporary stays (TS) have been established in all municipalities as intermediate care structures to receive mostly older patients, usually, but not necessarily after a period of hospitalization, who require short-term care that cannot be provided at home [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The TS facilities, like most intermediate care structures, are expected to provide continuity of care and reduce hospital (re)admissions in the face of accelerated hospital discharge [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. There are no centralized guidelines for TS operations, and the municipalities independently organize their TS structure and care delivery. TS facilities are not obligated to have physicians on board, and the responsibility for treatment lies with the patients\u0026rsquo; general practitioner or the hospital from which they were transferred [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe landscape of intermediate care functions across Western European countries is fragmented. Despite their increasing emergence throughout the past 30 years, there is no consensus on the clinical gap that intermediate care functions fill and how they are to be organized purposefully for the various and complex needs that may adhere to the plethora of patients they serve [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, data-driven insights about TS patients are also very scarce. A recent study used a cohort of 11,424 TS patients across 14 Danish municipalities to describe their medication use patterns. This study reported high levels of polypharmacy, noticeable prevalence of risk medications, and high diversity of medications used by TS patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In another study on the same cohort, baseline patient characteristics and care trajectories, such as morbidity, mortality, and hospital readmissions, were analyzed, and complex patient profiles were reported [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. So far, these two were the only quantitative studies conducted on a large cohort of Danish TS patients from multiple municipalities, and both studies uncovered high levels of heterogeneity in a wide range of clinical variables.\u003c/p\u003e \u003cp\u003eHigh heterogeneity among patients may pose challenges to the delivery of effective and coordinated care, especially in intermediate care facilities with generally lower specialization levels and limited resources, such as Danish TS facilities. The mix of multimorbidity, polypharmacy, and social vulnerability, especially when combined with the variability in organizational structures and the absence of standardized protocols, increases the risk of adverse outcomes such as mortality or increased health care use [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, a heterogeneous patient population requires a broad range of specialized care and facilities may lack the appropriate staffing mix, leading to inefficiencies and potential decline in care quality [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These challenges highlight the importance of systematic characterization of patient profiles to provide more insight into the patients\u0026rsquo; needs and help decision-makers develop targeted and effective intervention strategies to ensure better care delivery [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProbabilistic methods such as latent class analysis (LCA) have become popular, especially in the health care domain, for characterizing complex populations by uncovering \u0026ldquo;hidden\u0026rdquo; subgroups within the population [\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26 CR27 CR28\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. LCA allows for estimating the probability that an individual belongs to a particular class (subgroup). This provides a more nuanced understanding of subgroup membership compared to commonly used clustering methods that fail to account for uncertainties. Another major strength of LCA is that, by assuming the existence of latent (unobserved) variables that give rise to the observed data, it is particularly useful in medical research where underlying conditions or factors may not be directly measurable or observable. LCA can model complex interactions between variables within each latent class, enabling a more detailed understanding of the relationships within subgroups without extensive model specifications required by regression modelling. Besides, it can simultaneously handle multiple outcome variables, allowing for a comprehensive analysis of patient profiles [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we aim to uncover and characterize subgroups of TS patients based on their demographic and clinical characteristics by employing LCA and interpreting the differences across subgroups. Such characterization of the TS patients will add to the scarce amount of data-driven insights about these patients, especially in terms of the interactions between the demographic variables, indicators of medical history, health care use, and patient outcomes. Such insights could potentially be used to improve and optimize the design and organization of TS facilities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe utilized a cohort of 11,284 patients who had at least one temporary stay within the period from January 1, 2016, to December 31, 2023, across 14 Danish municipalities. Demographic, clinical, and administrative information for each patient in the cohort was obtained from Danish administrative and health registries.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eDates of TS move-in and move-out were provided by the municipalities together with the unique Central Person Register (CPR) numbers for each patient, which allowed us to link these data with the administrative and health registries at an individual level. Information about dates of emigration, immigration, or death and demographic information, including date of birth and sex, were provided by the Civil Registration System [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Information about dates of hospital contacts and the diagnosis codes according to the International Classification of Diseases, tenth revision (ICD-10) was obtained from the Danish National Patient Registry [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The National Patient Registry also contains data about clinical procedures, e.g., administrative activities, treatments, and operations via the Health Care Classification System (SKS) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. We utilized the SKS procedure codes to identify surgery events for each patient. Additionally, we obtained information about the filled prescriptions from the Danish National Prescription Registry, which contains data on all drugs dispensed at Danish pharmacies since 1995 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. From this registry, we extracted data on the type of drugs according to the Anatomical Therapeutic Chemical Classification System (ATC) and the date on which the drugs were dispensed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eInitially, the cohort included 11,584 adult patients across 14 municipalities with valid temporary stay information whose temporary stay fell completely within the period of 2016\u0026ndash;2023. We excluded those who did not have a continuous residence in Denmark during the five years before TS admission (n\u0026thinsp;=\u0026thinsp;300) due to their incomplete medical records in Danish registries. As a result, 11,284 patients remained in the study cohort. If a patient had multiple temporary stays separated by less than two days, the stays were merged to form a single stay. Additionally, if a patient had multiple temporary stays separated by two or more days, only the first stay was included in this study.\u003c/p\u003e\n\u003ch3\u003eAnalytic process\u003c/h3\u003e\n\u003cp\u003eUsing the CPR number of each patient, we linked the data from municipalities to the data from registries. We employed the LCA method to identify the \u0026ldquo;unobserved\u0026rdquo; subgroups of temporary stay patients based on demographic and clinical indicators, i.e., observed variables. We chose to focus on variables at high levels of abstraction, such as counts and sums, to capture the general medical characteristics and health care use of our widely heterogeneous cohort. We left lower-level characteristics, such as the exact morbidities and prescribed medication, as \u0026ldquo;unobserved\u0026rdquo; variables and analyzed them after identifying the subgroups.\u003c/p\u003e \u003cp\u003eInitially, ten categorical variables were derived as candidate indicators for our analysis. The numeric variables were categorized to facilitate interpretability and to meet the requirements of the statistical tools used in this study. The candidate indicators are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eDescription of candidate indicators for the latent class analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical levels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex as assigned at birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efemale, male\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge at the time of TS admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;69, 70\u0026ndash;79, 80\u0026ndash;89, 90+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emultimorb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. unique Charlson morbidities [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] based on the 5-year diagnosis history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0, 1\u0026ndash;2, 3+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharlson Comorbidity Index [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] based on the 5-year diagnosis history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0, 1\u0026ndash;2, 3+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edrugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. unique filled prescription drugs at the 4th ATC level within one year before TS admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;4, 5\u0026ndash;9, 10+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. surgeries within one year before TS admission. Excluded SKS codes are presented in supplementary material \u003cem\u003e\u0026ndash;\u003c/em\u003eTable A1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0, 1, 2+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. hospitalizations (inpatient contacts) within one year before TS admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1, 2\u0026ndash;3, 4+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. outpatient hospital contacts within 1 year before TS admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1, 2\u0026ndash;3, 4+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. days between the end of the previous hospitalization and TS admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0, 1\u0026ndash;6, 7+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurg_1w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the patient had undergone surgery during a hospitalization that ended within seven days before TS admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes, no\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\u003eCorrelation measures between each pair of indicators were calculated to avoid including highly correlated indicators in the model [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Depending on the type of indicator pairs, i.e., categorical-categorical, categorical-binary, or binary-binary, we used Spearman\u0026rsquo;s rank correlation, point-biserial correlation, or phi coefficient, respectively. Consequently, the pairs with correlations\u0026thinsp;\u0026ge;\u0026thinsp;0.3 were marked for further investigation. From each pair of marked indicators, one of them was excluded by the consensus of the authors.\u003c/p\u003e \u003cp\u003eGiven that there are no fixed methods for specifying the \u0026ldquo;optimal\u0026rdquo; LCA model [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], we iteratively fitted models with 2\u0026ndash;10 latent classes and selected the one with minimum Bayesian information criterion (BIC) as the optimal model. The minimum BIC value ensures that the model is the most likely to fit the data yet is not too complex. Additionally, we made sure that the model converges and that the entropy is at least 0.8 since high entropy is essential for a model to generate well-separated and distinct subgroups [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter selecting the optimal model, the prevalences of each categorical level of variables were calculated for each subgroup and the whole cohort. Mean and standard deviation (SD) were also calculated for non-binary variables at the numeric level. The Chi-square test was used to evaluate the differences in binary variables among the subgroups, considering \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e as significant. Following a significant Chi-square test, we performed pairwise comparisons with Bonferroni correction to identify differences between the latent classes. Effect sizes for binary variables were calculated using Cohen\u0026rsquo;s \u003cem\u003eh\u003c/em\u003e [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For non-binary variables, the differences among subgroups were evaluated using the Kruskal-Wallis test with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e considered significant. Post hoc contrasts were made using Dunn\u0026rsquo;s test with Bonferroni-adjusted p-values, and the effect sizes were calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r=\\:Z/\\sqrt{N}\\)\u003c/span\u003e\u003c/span\u003e where Z is the Dunn\u0026rsquo;s test statistic and N is the sample size. We used non-parametric statistical tests due to the non-normal distribution of variables and the violation of the homogeneity of variances.\u003c/p\u003e \u003cp\u003eAdditionally, prevalence proportions of morbidities and selected filled prescriptions were calculated. When analyzing the distribution of morbidities across subgroups, besides the Charlson morbidity mappings, alcohol abuse was added to the morbidity set according to the Elixhauser ICD-10 mapping [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. To analyze the distribution of prescribed medications across subgroups, we used the ATC mappings in supplementary material \u0026ndash;Table A2. We used logistic regression to investigate the association of subgroup membership with the history of individual morbidities and prescribed drug categories. Odds ratios and 95% confidence intervals (CI) described the association of membership in a given subgroup with history of morbidities/drugs compared to all other patients in the cohort. We also calculated the prevalence proportion of recent fall injuries before temporary stay as a measure of frailty. The ICD-10 mappings for fall injuries are provided in supplementary material \u0026ndash;Table A3. A recent fall injury was defined as a fall injury diagnosed during a hospitalization that ended within seven days before admission to TS. Similarly, prevalence proportions of recent surgeries were calculated.\u003c/p\u003e \u003cp\u003eTo analyze the patient outcomes, median and interquartile range (IQR) for length of stay (LOS) at TS, median and 95% CIs of LOS adjusted for death at TS by censoring, median time-to-death (survival time) after temporary stay, 30-day mortality rates, median time-to-hospitalization from the start of the temporary stay, and 30-day hospitalization rate were calculated for each subgroup and the whole cohort. The adjusted median LOS and the median time to death were calculated using the Kaplan-Meier method. The CIs were calculated using the log-minus-log transformation method [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Median time-to-hospitalization, considering death as a competing risk, was calculated using the Aalen-Johansen method for estimating the cumulative incidence function (CIF) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. To construct the 95% CIs, we employed bootstrapping with 1000 resamples. The statistical tests for evaluating the differences in 30-day mortality and 30-day hospital admission were the same as the previously mentioned tests for binary variables.\u003c/p\u003e \u003cp\u003eAll calculations and analyses were performed using R version 4.3.3. The latent class analysis was done using the poLCA package version 1.6.0.1 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and each model (k\u0026thinsp;=\u0026thinsp;2, 3, \u0026hellip;, 10) was fit with 20,000 maximum iterations and 20 repetitions with different random starting points to ensure that the underlying expectation maximization and Newton-Raphson algorithms locate the global maximum.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe identified five subgroups of TS patients that can briefly be described as \u003cem\u003eSG1: Middle-old multimorbid patients (38%)\u003c/em\u003e, \u003cem\u003eSG2: Oldest-old women with low multimorbidity (18%)\u003c/em\u003e, \u003cem\u003eSG3: Younger-old men with low polypharmacy (18%)\u003c/em\u003e, \u003cem\u003eSG4: Middle-old patients with low hospitalization burden (11%)\u003c/em\u003e, \u003cem\u003eSG5: Younger-old men with extreme chronic conditions (15%)\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe results of indicator and model selection and the corresponding model fit statistics are reported in supplementary material \u0026ndash;Tables A4 and A5. Description and statistical analysis of the baseline characteristics of the subgroups and the full study population are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCounts (prevalence proportions %) of categorical representation of baseline demographic and clinical characteristics.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG 4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG 5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFull Cohort\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSize: n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4249 (37.7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2255 (20.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1932 (17.1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1207 (10.7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1641 (14.5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e11284 (100)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2474 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1994 (88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e488 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e612 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e508 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6076 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1775 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1444 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e595 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1133 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5208 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[18\u0026ndash;69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e901 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e656 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1991 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[70\u0026ndash;79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e927 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e604 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e710 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2795 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[80\u0026ndash;89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1977 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e868 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e477 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3928 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[90+]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1073 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1102 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2570 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. Charlson morbidities, 5 years before TS admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1459 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e674 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e484 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3017 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2976 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e796 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1214 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e634 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e709 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6329 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;= 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e992 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e813 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1938 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. unique drugs, 4th ATC level, 1 year before TS admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0\u0026ndash;4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e561 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1066 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1919 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e925 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1305 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e866 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e504 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e277 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3877 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;= 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3324 (78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e389 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5564 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. inpatient hospital contacts, 1 year before TS admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0\u0026ndash;1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e361 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1095 (91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1835 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1478 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1395 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e874 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3885 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;= 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2570 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e892 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1603 (98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5564 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. outpatient hospital contacts, 1 year before TS admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0\u0026ndash;1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e826 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1102 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e782 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e526 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3347 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e751 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e521 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e445 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2099 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;= 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2672 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e632 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e705 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e423 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1406 (86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5838 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. surgeries, 1 year before TS admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2496 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1346 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1143 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1088 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6252 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e937 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e703 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e465 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e291 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2454 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;= 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e816 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e324 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1171 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2578 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. days between the end of the previous hospitalization and TS admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;= 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e702 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1040 (86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2135 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e607 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1323 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2840 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1972 (87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1640 (85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1267 (77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7826 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of the differences in patient characteristics. Numeric variables are presented as mean (standard deviation).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFull Cohort\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. Patients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4249\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2255\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1932\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1207\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1641\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e11284\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1775\u003c/p\u003e\n \u003cp\u003e(41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003cp\u003e(11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1444\u003c/p\u003e\n \u003cp\u003e(74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e595\u003c/p\u003e\n \u003cp\u003e(49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1133\u003c/p\u003e\n \u003cp\u003e(69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5208\u003c/p\u003e\n \u003cp\u003e(46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 2103.8\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e3,5\u0026thinsp;\u0026gt;\u0026thinsp;1,4\u0026thinsp;\u0026gt;\u0026thinsp;2 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.4\u003c/p\u003e\n \u003cp\u003e(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.9\u003c/p\u003e\n \u003cp\u003e(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.5\u003c/p\u003e\n \u003cp\u003e(12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003cp\u003e(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.0\u003c/p\u003e\n \u003cp\u003e(10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.5\u003c/p\u003e\n \u003cp\u003e(11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 3598.7\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e2\u0026thinsp;\u0026gt;\u0026thinsp;1,4\u0026thinsp;\u0026gt;\u0026thinsp;3,5 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. Charlson morbidities,\u003c/p\u003e\n \u003cp\u003e5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003cp\u003e(1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003cp\u003e(0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e(0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e(1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003cp\u003e(1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003cp\u003e(1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 3710.4\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e5\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;3,4\u0026thinsp;\u0026gt;\u0026thinsp;2 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCI, 5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003cp\u003e(2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003cp\u003e(1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003cp\u003e(1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003cp\u003e(2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003cp\u003e(2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 2193\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e5\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;3,4\u0026thinsp;\u0026gt;\u0026thinsp;2 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. unique drugs, 4th ATC level,\u003c/p\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003cp\u003e(4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003cp\u003e(3.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.40\u003c/p\u003e\n \u003cp\u003e(2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.31\u003c/p\u003e\n \u003cp\u003e(4.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.23\u003c/p\u003e\n \u003cp\u003e(5.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003cp\u003e(5.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 5303.5\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e1,5\u0026thinsp;\u0026gt;\u0026thinsp;2,4\u0026thinsp;\u0026gt;\u0026thinsp;3 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. inpatient contacts, 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.28\u003c/p\u003e\n \u003cp\u003e(3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003cp\u003e(1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003cp\u003e(3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003cp\u003e(0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003cp\u003e(6.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003cp\u003e(4.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 5047\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e5\u0026thinsp;\u0026gt;\u0026thinsp;1,3\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;4 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. outpatient contacts, 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.95\u003c/p\u003e\n \u003cp\u003e(13.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003cp\u003e(5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003cp\u003e(10.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003cp\u003e(6.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.18\u003c/p\u003e\n \u003cp\u003e(24.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.10\u003c/p\u003e\n \u003cp\u003e(14.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 2358.2\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e5\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;2,3,4 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of surgeries,\u003c/p\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e(1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003cp\u003e(1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e(2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003cp\u003e(1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003cp\u003e(4.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003cp\u003e(2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 2648.6\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e5\u0026thinsp;\u0026gt;\u0026thinsp;1,2,3\u0026thinsp;\u0026gt;\u0026thinsp;4 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. days between the previous hospital discharge and TS admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003cp\u003e(21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003cp\u003e(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003cp\u003e(13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266.7\u003c/p\u003e\n \u003cp\u003e(498.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003cp\u003e(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.8\u003c/p\u003e\n \u003cp\u003e(182.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 4299.2\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e4\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;2,3 **\u003c/p\u003e\n \u003cp\u003e4\u0026thinsp;\u0026gt;\u0026thinsp;5 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecent surgery,\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e648\u003c/p\u003e\n \u003cp\u003e(15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003cp\u003e(25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003cp\u003e(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e488\u003c/p\u003e\n \u003cp\u003e(29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2077\u003c/p\u003e\n \u003cp\u003e(18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 503.7\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e2,5\u0026thinsp;\u0026gt;\u0026thinsp;1,3\u0026thinsp;\u0026gt;\u0026thinsp;4 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecent fall injury,\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003cp\u003e(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e704\u003c/p\u003e\n \u003cp\u003e(31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003cp\u003e(15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003cp\u003e(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1752\u003c/p\u003e\n \u003cp\u003e(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e = 683.2\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003e2\u0026thinsp;\u0026gt;\u0026thinsp;1,3,5\u0026thinsp;\u0026gt;\u0026thinsp;4 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e* Bonferroni-adjusted pairwise Chi-Square p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, |Cohen\u0026rsquo;s h| \u0026gt; 0.2\u003c/p\u003e\n \u003cp\u003e** Bonferroni-adjusted pairwise Dunn\u0026rsquo;s p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, |r| \u0026gt; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eCharacterization of subgroups\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSG1: Middle-old multimorbid patients\u003c/h2\u003e\n \u003cp\u003eThis subgroup accounts for the largest portion of the patients (38%), with a median age of 83.4 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IQR=77.8-88.0\\)\u003c/span\u003e\u003c/span\u003e; 72% \u0026ge; 80). As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, this patient group showed a relatively high level of multimorbidity. Similar patterns of high polypharmacy and high frequency of hospitalization were identified in this subgroup. These patients had a moderate frequency of undergoing surgery. They also had a relatively high prevalence of most morbidities provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Membership in this subgroup was associated with increased odds of having almost all morbidities compared to other patients. The exceptions were liver disease and alcohol abuse, as illustrated in Fig.\u0026nbsp;1. High prevalences of almost all prescribed drug categories were also observed in this patient group. In fact, Fig.\u0026nbsp;2 demonstrates that patients in this subgroup had higher odds of having a history of all prescription categories except for anti-dementia drugs. An important characteristic of these patients was the shorter survival time. Table\u0026nbsp;5 shows that 17% of the patients in this subgroup died within 30 days after beginning their temporary stay, and the median time-to-death was 15.2 months (95% CI: 14.2\u0026ndash;16.5).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSG2: Oldest-old women with low multimorbidity\u003c/h3\u003e\n\u003cp\u003eWith a median age of 87.8 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IQR=82.3-92.2;49\\%\\:\\ge\\:90\\)\u003c/span\u003e\u003c/span\u003e), this mostly female (88%) subgroup was relatively older compared to others. These patients had a highlighted prevalence of direct transfer from hospital to TS (87%). The lowest multimorbidity was observed in this subgroup. They had relatively moderate polypharmacy and relatively lower frequencies of hospitalization and surgery. However, this subgroup had one of the highest prevalences of recent surgery before TS admission (26%). All morbidities had very low prevalences, and in general, membership in this subgroup was associated with reduced odds of all morbidities compared to other patients. All medication categories had relatively low prevalence except for sex hormones and drugs used in thyroid therapy. Another notable characteristic of this subgroup is the highlighted prevalence of recent fall injuries (31%) compared to other subgroups. Regarding the patient outcomes, these patients had longer stays at TS, and relatively moderate survival time and hospital admission rate. 11% died within 30 days after admission to TS, and 20% were hospitalized within the same period.\u003c/p\u003e\n\u003ch3\u003eSG3: Younger-old men with low polypharmacy\u003c/h3\u003e\n\u003cp\u003eThis predominantly male (75%) subgroup had a median age of 71.3 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IQR=63.4-78.3;78\\%\\:\\le\\:79)\\)\u003c/span\u003e\u003c/span\u003e. These patients had a highlighted prevalence of direct transfer from hospital to TS (85%). Moderate multimorbidity and the lowest level of polypharmacy were observed in this subgroup. Most morbidities had low prevalence, and membership in this subgroup was associated with reduced odds of all morbidities except alcohol abuse, liver disease, and cerebrovascular disease. All prescribed drug categories had low prevalences, and most of them were the least prevalent among all subgroups. Longer stays at TS, high survival times, and moderate hospital admission rates were important characteristics of this subgroup regarding patient outcomes. 8% of these patients died within 30 days after TS admission, and 24% were hospitalized.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSG4: Middle-old patients with low hospitalization burden\u003c/h2\u003e\n \u003cp\u003eWith an even distribution of males and females and a median age of 82.4 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IQR=75.9-87.6;65\\%\\ge\\:\\:80\\)\u003c/span\u003e\u003c/span\u003e), this patient group was the smallest one in size (11% of the cohort). Direct transfers to TS from hospitals were almost nonexistent (0.6%), and 85% of these patients did not experience hospitalization during the week leading to TS admission. In fact, the hospitalization frequency in this subgroup was the lowest among all subgroups. Almost 91% of them had zero or one hospitalization on their one-year record, and none had four or more hospitalizations. Similarly, the lowest frequency of surgery belonged to this subgroup, as 90% did not undergo any surgeries during the year before TS admission. Despite a very low frequency of hospitalization, these patients had moderate levels of outpatient hospital contact. Also, relatively moderate levels of multimorbidity and polypharmacy were observed in this subgroup. Most morbidities in this subgroup had relatively low prevalence, except dementia, which was highlighted with a prevalence of 26% and a strong association with membership in this subgroup. The prescribed drug categories that had increased odds associated with membership in this subgroup were antidementia drugs, antipsychotics, anti-Parkinson drugs, antidepressants, and urologicals. These patients had the lowest prevalence of recent fall injuries and recent surgeries. They also had the shortest median length of stay at TS, moderate survival times, and the lowest hospital admission rate. 9% died within 30 days after TS admission, and 16% were hospitalized.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eSG5: Younger-old men with extreme chronic conditions\u003c/h2\u003e\n \u003cp\u003eThis predominantly male (69%) subgroup had a median age of 72.6 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IQR=65.4-77.6;83\\%\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 79), which makes it one of the youngest subgroups. We observed extreme levels of multimorbidity, polypharmacy, frequency of hospitalization, and frequency of surgery. Membership in this subgroup was associated with increased odds of history of all morbidities except dementia. Especially, highlighted prevalences of diabetes, liver disease, renal disease, cancer, and peripheral vascular disease can be indicative of severe chronic burden in this patient group. Additionally, this subgroup had a high prevalence of most prescribed drugs. The most distinctively lower prevalences belonged to anti-dementia drugs and sex hormones. This subgroup had relatively short survival times and the highest hospital admission rate. 15% died within 30 days after TS admission, and 40% were hospitalized in that period.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCounts and prevalence proportions (%) of morbidities and selected prescribed drug categories.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSG5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFull Cohort\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. Patients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4249\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2255\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1932\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1207\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1641\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e11284\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"16\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5-year Morbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMyocardial Infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e493 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCongestive Heart Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e684 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1187 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeripheral Vascular Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e518 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1061 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebrovascular Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1284 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e296 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e515 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e497 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2796 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes (uncomplicated)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e833 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e521 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1637 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes (complicated)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (\u0026lt;\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e715 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic Pulmonary Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1025 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e424 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1764 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRheumatoid Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e395 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeptic Ulcer Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (\u0026lt;\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e426 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e275 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e811 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetastatic Solid Tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (\u0026lt;\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e483 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1029 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e575 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2165 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild Liver Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (\u0026lt;\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (\u0026lt;\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e362 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate/Severe Liver Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e501 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e309 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1191 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol Abuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e832 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"22\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1-year Prescribed Drug Classes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrugs used in diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1030 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e590 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2075 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntithrombotic agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3018 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e910 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e573 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e599 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1095 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6195 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntianemic preparations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e859 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e347 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1790 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrugs used in cardiac therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e859 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1496 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2444 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e770 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e377 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e882 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4757 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVasoprotectives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e426 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta-blocking agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1806 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e539 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e728 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3652 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium channel blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1414 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e610 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e310 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e548 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3225 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgents acting on renin-angiotensin system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2159 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e875 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e535 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e429 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e844 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4842 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipid-modifying agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2067 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e547 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e497 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e402 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e889 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4402 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex hormones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e504 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e959 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrologicals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e723 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1618 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrugs used in thyroid therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e484 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1003 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOpioids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e651 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e923 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4187 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntiepileptics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e267 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e641 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntiparkinson drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e340 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e703 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntipsychotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e445 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1043 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiolytics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e510 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1090 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypnotics and sedatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e912 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e279 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e414 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e723 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntidepressants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1358 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e455 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e402 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e546 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3043 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntidementia drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e345 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e293 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e899 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrugs for obstructive airway disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1260 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e487 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2291 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eAll overall Chi-square tests for each morbidity/drug category had p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;Table\u0026nbsp;5. Patient outcomes for each subgroup and the full cohort.\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Taba\" style=\"width: 906px;\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 285px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 110.683px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSG 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSG 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSG 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSG 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSG 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95.7431px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 285px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110.683px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4249 (37.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2255 (20.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1932 (17.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1207 (10.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1641 (14.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95.7431px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11284 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 881.426px;\" colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength of stay, days\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 285px;\" align=\"left\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110.683px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e23 (11\u0026ndash;46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e28 (15\u0026ndash;50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e28 (14\u0026ndash;56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e20 (7\u0026ndash;52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e24 (12\u0026ndash;48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95.7431px;\" align=\"left\"\u003e\n \u003cp\u003e25 (12\u0026ndash;49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 881.426px;\" colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength of stay \u0026ndash; adjusted for death at MIDO, days\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 285px;\" align=\"left\"\u003e\n \u003cp\u003eMedian (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110.683px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e28 (27\u0026ndash;28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e31 (30\u0026ndash;33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e30 (28\u0026ndash;32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e23 (20\u0026ndash;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e28 (27\u0026ndash;31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95.7431px;\" align=\"left\"\u003e\n \u003cp\u003e28 (28\u0026ndash;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 881.426px;\" colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e30-day mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 285px;\" align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110.683px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e715 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e241 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e156 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e114 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e253 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95.7431px;\" align=\"left\"\u003e\n \u003cp\u003e1479 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 295px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)=\\)\u003c/span\u003e\u003c/span\u003e 128.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 385.683px;\" colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e1,5\u0026thinsp;\u0026gt;\u0026thinsp;2,3,4**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 95.7431px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 881.426px;\" colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival time, months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 285px;\" align=\"left\"\u003e\n \u003cp\u003eMedian (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110.683px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e15.2 (14.2\u0026ndash;16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e29.5 (27.2\u0026ndash;31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e44.7 (41.2\u0026ndash;49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e26.1 (23.5\u0026ndash;28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e18.2 (15.4\u0026ndash;21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95.7431px;\" align=\"left\"\u003e\n \u003cp\u003e23.8 (22.8\u0026ndash;24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 881.426px;\" colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e30-day hospitalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 285px;\" align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110.683px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1178 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e462 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e463 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e194 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e661 (40.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95.7431px;\" align=\"left\"\u003e\n \u003cp\u003e2957 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 295px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\left(4\\right)=\\)\u003c/span\u003e\u003c/span\u003e 280.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 480.683px;\" colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e5\u0026thinsp;\u0026gt;\u0026thinsp;1,2,3,4**; 1\u0026thinsp;\u0026gt;\u0026thinsp;2**; 1,3\u0026thinsp;\u0026gt;\u0026thinsp;4**; 2\u0026thinsp;\u0026gt;\u0026thinsp;4*; 1\u0026thinsp;\u0026gt;\u0026thinsp;3*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95.7431px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 881.426px;\" colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime-to-hospitalization, months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 285px;\" align=\"left\"\u003e\n \u003cp\u003eMedian (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110.683px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e5.2 (4.7\u0026ndash;5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e12.4 (10.8\u0026ndash;14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e8.7 (7.8\u0026ndash;10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e16.3 (14.4\u0026ndash;19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n \u003cp\u003e1.9 (1.6\u0026ndash;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95.7431px;\" align=\"left\"\u003e\n \u003cp\u003e7.0 (6.5\u0026ndash;7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 871.426px;\" colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eLOS\u0026thinsp;=\u0026thinsp;length of stay at TS; IQR\u0026thinsp;=\u0026thinsp;interquartile range; aLOS\u0026thinsp;=\u0026thinsp;length of stay adjusted for death at TS\u003c/p\u003e\n \u003cp\u003ePairwise p-values are Bonferroni-adjusted. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe variation observed across subgroups reinforces earlier findings that intermediate care populations are clinically diverse and may not fit neatly into standardized care pathways [\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. For instance, SG1 and SG5 had high levels of multimorbidity and polypharmacy, shorter survival times, and frequent hospital contacts. In contrast, SG2 exhibited a high prevalence of recent fall injuries but a relatively lower chronic disease burden. SG3 and SG4 captured profiles with lower overall disease burden, including patients with indicators of alcohol abuse (SG3) and low hospitalization frequency (SG4). This heterogeneity highlights the potential value of stratifying care approaches to better align with patient needs, which is also supported by findings from intermediate care settings in other European countries [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Rather than applying a one-size-fits-all model of care, municipalities could consider using insights from subgroup patterns to inform differentiated care planning and resource prioritization, in line with the intended flexibility of TS as an intermediate care model. To support such tailored care, the integration of comprehensive geriatric assessment (CGA) at key transition points \u0026ndash; ideally at hospital discharge or upon arrival at TS \u0026ndash; may be particularly valuable by enabling more individualized and proactive care planning [\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferences in clinical and functional complexity across TS patient subgroups underscore the importance of aligning staffing models and care competencies with subgroup-specific needs [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. For instance, patients in SG1 and SG5 exhibited high levels of polypharmacy and chronic burden, indicating a need for enhanced medical oversight and structured medication review. Studies from intermediate care settings have shown that pharmacist-led reviews can effectively identify and resolve drug-related problems with high implementation rates of medication changes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Broader multicomponent interventions that include medication reconciliation, patient education, and transitional follow-up have also been associated with significantly reduced hospital readmissions [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], which may especially be helpful for SG5 patients with extreme 30-day hospital readmissions (40%). Other patients, such as those in SG2, may benefit from a greater focus on rehabilitation and fall prevention strategies like multifactorial fall prevention interventions, including tailored exercise and environmental risk assessments, which have been shown to significantly reduce fall incidence in institutional settings [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. SG3 patients, characterized by lower polypharmacy but increased prevalence of alcohol abuse, liver disease, and cerebrovascular disease, highlight the need for specialized care planning that addresses cognitive or behavioral health needs and provides proactive behavioral health support [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Conversely, SG4 included patients with minimal hospital contact, increased prevalence of dementia, and shorter TS stays, raising questions about whether specific admissions could be better managed through enhanced home care or outpatient rehabilitation [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. For instance, a study in the United States showed that Medicare patients with dementia had no differences in readmission or mortality whether receiving post-acute care via home health versus a skilled nursing facility [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings offer novel insights that enhance our understanding of the medical complexity associated with intermediate care patient populations, which are relatively scarce. Currently, scholarly contributions struggle to define and specify how intermediate care functions are to be organized in accordance with patient populations and their mixed characteristics [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These challenges are potentially highlighted by the fact that intermediate care functions are often located in decentralized units presenting their own care environment [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In Denmark, TS facilities governed by municipalities operate within The Social Service Act and do not have health care mandate (\u0026sect;\u0026nbsp;84.2) [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. In practice, this means that neither doctors nor nurses are mandated. Further, the main staff \u0026ndash; nurse assistants \u0026ndash; do not have access to medication, pharmacists\u0026rsquo; expertise, or patient records. Staff potentially lack basic and crucial information for enabling continuous support and care throughout the patient\u0026rsquo;s treatment scheme and trajectory. Thus, the access to medical competence and resources necessary to act timely and in accordance with the complex needs of these patient subgroups \u0026ndash; particularly SG1 and SG5 \u0026ndash; may be compromised, which may represent a hazard to patient safety and a challenge to promote continuity. More recent publications have highlighted that intermediate care practices may present patients with remarkably different environments, which has implications for patient safety, continuity of care and health disparities [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Such differences have earned less attention in research. Therefore, evidence elucidating complexities in intermediate care patient populations can also be helpful in the future design of intermediate care organizations.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eOur study utilized a large cohort of TS patients from 14 municipalities distributed across four out of five regions of Denmark (see supplementary material \u0026ndash;Table A6). Although two other studies have used a similarly large cohort to describe medication use, patient trajectories, and baseline characteristics of TS patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], our identification and characterization of patient subgroups shed light on interactions of demographic and clinical indicators and patient outcomes in a nuanced manner. Another point of strength in our study is that the seven observed variables based on which the subgroups were identified are simple and easily interpretable. Although we excluded variables at a lower level of abstraction, such as the exact morbidities, prescribed medication, and recent fall injuries from the set of observed indicators, we later analyzed these unobserved variables, uncovering distinct patterns across different subgroups that further underscores the ability of the model to capture complex interactions between patient variables. We also refrained from relying on p-values as the only marker of difference across subgroups and employed effect size measures and confidence intervals where appropriate to enable more practical interpretations of the contrasts compared to relying solely on statistical significance.\u003c/p\u003e \u003cp\u003eThis study has limitations that must be acknowledged. An important limitation is the absence of socio-economic variables in the study, which could unravel more insights about the TS population and their care needs. Furthermore, in LCA, like most unsupervised methods for subgroup analysis, deciding on the number of subgroups is mainly based on statistical metrics that may not have robust clinical justification [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Although criteria such as minimum BIC and high entropy provide a reasonable statistical basis for determining the \u0026ldquo;optimal\u0026rdquo; number of subgroups, other models without the minimum BIC value may also be clinically relevant. In addition, as subgroup assignments are probabilistic, a clinician may find it challenging to determine precisely to which subgroup an individual patient belongs. However, overall patterns in patient variables across subgroups can still provide meaningful insights about the characteristics of each subgroup.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e Our study identified five subgroups of TS patients, each representing a distinct sub-profile of patient characteristics and outcomes, reflecting substantial heterogeneity in demographic characteristics, clinical complexity, and care needs. These differences may hold important implications for the organization and delivery of care in TS settings, particularly regarding the balance between standardization and individualized support.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTemporary stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLatent class analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral person register\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational classification of diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSKS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSundhedsv\u0026aelig;senets klassifikationssystem (Health care classification system)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnatomical therapeutic chemical classification\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esd\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLength of stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaLOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted length of stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComprehensive geriatric assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis research project has been conducted in accordance with the principles of the Helsinki Declaration and further adheres to the legal requirements of the study country. This study was register-based, only anonymized data was used, data is presented in aggregate and anonymous form, and study participants were not contacted nor required any active participation. According to Danish law, approval from an ethics committee and informed consent to participate are not required for register-based studies (section 14.2 of the Act on Research Ethics Review of Health Research Projects and section 10 of the Data Protection Act). In terms of data protection, the study was registered at the University of Southern Denmark inventory (record no. 11.436).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMR, KK, and KE designed the study. MR performed the statistical analysis and wrote the manuscript. MR and RFG interpreted the results. RFG, MS, and OA contributed to writing the discussion. KK, KE, RFG, MS, OA, and AP have read and commented on the manuscript. All authors have read and approved the final version of the manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by Novo Nordisk Foundation (grant number 0075454). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDanish legislation does not allow individual-level data to be publicly available. Anonymized data can be accessed by authorized researchers after application to Forskeservice at the Danish Health Data Authority. MR had full access to the data. Coding scripts for the analysis are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research project has been conducted in accordance with the principles of the Helsinki Declaration and further adheres to the legal requirements of the study country. This study was register-based, only anonymized data was used, data is presented in aggregate and anonymous form, and study participants were not contacted nor required any active participation. According to Danish law, approval from an ethics committee and informed consent to participate are not required for register-based studies (section 14.2 of the Act on Research Ethics Review of Health Research Projects and section 10 of the Data Protection Act). In terms of data protection, the study was registered at the University of Southern Denmark inventory (record no. 11.436).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Engineering Technology and Didactics, Technical University of Denmark, Ballerup, Denmark. \u003csup\u003e2\u003c/sup\u003eClinical Research Center, Amager and Hvidovre Hospital, Hvidovre, Denmark. \u003csup\u003e3\u003c/sup\u003eDepartment of Geriatric Diseases, Amager and Hvidovre Hospital, Hvidovre, Denmark. \u003csup\u003e4\u003c/sup\u003eDepartment of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. \u003csup\u003e5\u003c/sup\u003eDepartment of Public Health, University of Southern Denmark, Odense, Denmark. \u003csup\u003e6\u003c/sup\u003eHospital Pharmacy Funen, Odense University Hospital, Odense, Denmark.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 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Scand J Public Health. 2024;52:5\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/14034948221122386\u003c/span\u003e\u003cspan address=\"10.1177/14034948221122386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Municipal care, Intermediate Care, Temporary stay, Older patients, Complex patient profile, Stratified care, Latent class analysis, Subgroup analysis, Denmark","lastPublishedDoi":"10.21203/rs.3.rs-6744694/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6744694/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eIn Denmark, municipal temporary stays (TS) have been established in all municipalities as intermediate care structures to receive mostly older patients who require short-term care that cannot be provided at their own residence.\u003cstrong\u003e \u003c/strong\u003eThese patients are admitted to TS facilities, usually but not necessarily after hospitalization. This study aims to identify subgroups of medically complex TS patients by analyzing demographic and clinical variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe identified a cohort of 11,284 patients with at least one temporary stay across 14 Danish municipalities during 2016-2023. Demographic and clinical information were obtained from Danish administrative and health registries. We employed latent class analysis to identify subgroups of patients. We characterized the subgroups by statistical analysis of patient characteristics and outcomes across subgroups and established patient profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe uncovered five patient subgroups: \u003cem\u003eSG1: middle-old multimorbid patients (38%)\u003c/em\u003e, \u003cem\u003eSG2: oldest-old women with low multimorbidity (18%)\u003c/em\u003e, \u003cem\u003eSG3: younger-old men with low polypharmacy (18%)\u003c/em\u003e, \u003cem\u003eSG4: middle-old patients with low hospitalization burden (11%)\u003c/em\u003e, \u003cem\u003eSG5: younger-old men with extreme chronic conditions (15%)\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTwo subgroups – SG1 and SG5 – demonstrated higher 30-day mortality, with the latter having the highest 30-day hospital admission rates. Two other subgroups – SG2 and SG3 – had longer temporary stays and longer survival times. SG4 patients had the shortest temporary stays, low 30-day mortality, and the lowest 30-day hospital admissions. The distribution of morbidities and prescribed drugs across subgroups showed distinct patterns that underscored the different care needs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eTS patients are heterogeneous and have complex care needs. We identified five patient groups and analyzed their characteristics, revealing distinct patterns in demographics, history of morbidities, prescribed medication, health care use, and patient outcomes. Our findings suggest that TS patients may benefit from comprehensive geriatric assessment at key transition points and stratified care planning.\u003c/p\u003e","manuscriptTitle":"Identifying and Characterizing Subgroups of Medically Complex Older Patients in Community-based Intermediate Care: A Latent Class Analysis of Danish Municipal Temporary Stay Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-24 12:35:04","doi":"10.21203/rs.3.rs-6744694/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-18T13:28:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T14:47:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143159417053289995202719886054315681157","date":"2026-02-09T13:45:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337811414982294334569242707589098812711","date":"2025-12-26T08:12:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T15:25:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302524940483693232789048919868615079086","date":"2025-07-10T14:59:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10532379146541020583059315770622447919","date":"2025-06-27T14:38:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T06:55:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-18T09:46:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-28T18:13:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-28T14:12:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-05-28T14:11:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6da8899-3105-4051-8c18-f227b93d8828","owner":[],"postedDate":"June 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:12:09+00:00","versionOfRecord":{"articleIdentity":"rs-6744694","link":"https://doi.org/10.1186/s12913-026-14583-2","journal":{"identity":"bmc-health-services-research","isVorOnly":false,"title":"BMC Health Services Research"},"publishedOn":"2026-04-30 15:58:25","publishedOnDateReadable":"April 30th, 2026"},"versionCreatedAt":"2025-06-24 12:35:04","video":"","vorDoi":"10.1186/s12913-026-14583-2","vorDoiUrl":"https://doi.org/10.1186/s12913-026-14583-2","workflowStages":[]},"version":"v1","identity":"rs-6744694","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6744694","identity":"rs-6744694","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}