TY - JOUR
AU - J. Wu
AU - E. Xue
AU - C. Liu
AU - J. Shao
AU - Y. Fu
AU - B. Zhao
AU - D. Chen
AU - H. Zhang
AU - Z. Ye
A1 - 
AB - AIMS: To classify the unmet integrated care needs of older adults with multimorbidity and to explore the factors associated with different categories of unmet integrated care needs among the target population. DESIGN: A cross-sectional survey using the statistical method of latent profile analysis. METHODS: From July 2022 to March 2023, 397 older adults with multimorbidity, aged 60 years or older, were recruited from one primary healthcare setting and from four secondary and tertiary hospitals to participate in face-to-face questionnaire surveys. The questionnaire used in this study to assess unmet integrated care needs among older adults with multimorbidity was self-designed through a series of steps, including a scoping review, expert consultation and cognitive interviews. Latent profile analysis was applied to uncover distinct profiles of unmet integrated care needs, and multinomial logistic regression was employed to explore whether the profiles were further distinguished by participants' sociodemographic and health-related covariates. The data were analysed using IBM SPSS v.29.0 and Mplus v.8.0. RESULTS: The optimal solution was a four-profile model, characterised by high unmet integration needs, high unmet system integration needs, low unmet system integration needs and low unmet integration needs, respectively. Multinomial logistic regression results indicated that profile differences were associated with place of residence, number of coresidents and the presence or absence of complex multimorbidity. CONCLUSION: The integrated care needs of older adults with multimorbidity have not yet been fully met. Classifying and characterising unmet integrated care needs profiles is a crucial step in the rational allocation of integrated care resources. REPORTING METHOD: This study was reported based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) for cross-sectional studies. PATIENT OR PUBLIC CONTRIBUTION: All participants were older adults with multimorbidity, and they were informed that they could withdraw from the study at any time.
AD - Nursing Department, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.; Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.; Institute of Nursing Research, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.; Nursing Department, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
AN - 39789809
BT - J Clin Nurs
C5 - Healthcare Disparities
CP - 5
DA - 05/2026
DO - 10.1111/jocn.17520
DP - NLM
ET - 20250109
IS - 5
JF - J Clin Nurs
LA - eng
N2 - AIMS: To classify the unmet integrated care needs of older adults with multimorbidity and to explore the factors associated with different categories of unmet integrated care needs among the target population. DESIGN: A cross-sectional survey using the statistical method of latent profile analysis. METHODS: From July 2022 to March 2023, 397 older adults with multimorbidity, aged 60 years or older, were recruited from one primary healthcare setting and from four secondary and tertiary hospitals to participate in face-to-face questionnaire surveys. The questionnaire used in this study to assess unmet integrated care needs among older adults with multimorbidity was self-designed through a series of steps, including a scoping review, expert consultation and cognitive interviews. Latent profile analysis was applied to uncover distinct profiles of unmet integrated care needs, and multinomial logistic regression was employed to explore whether the profiles were further distinguished by participants' sociodemographic and health-related covariates. The data were analysed using IBM SPSS v.29.0 and Mplus v.8.0. RESULTS: The optimal solution was a four-profile model, characterised by high unmet integration needs, high unmet system integration needs, low unmet system integration needs and low unmet integration needs, respectively. Multinomial logistic regression results indicated that profile differences were associated with place of residence, number of coresidents and the presence or absence of complex multimorbidity. CONCLUSION: The integrated care needs of older adults with multimorbidity have not yet been fully met. Classifying and characterising unmet integrated care needs profiles is a crucial step in the rational allocation of integrated care resources. REPORTING METHOD: This study was reported based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) for cross-sectional studies. PATIENT OR PUBLIC CONTRIBUTION: All participants were older adults with multimorbidity, and they were informed that they could withdraw from the study at any time.
PY - 2026
SN - 0962-1067
SP - 2140
EP - 2149+
ST - Classifying and Characterising Unmet Integrated Care Needs of Older Adults With Multimorbidity: A Latent Profile Analysis
T1 - Classifying and Characterising Unmet Integrated Care Needs of Older Adults With Multimorbidity: A Latent Profile Analysis
T2 - J Clin Nurs
TI - Classifying and Characterising Unmet Integrated Care Needs of Older Adults With Multimorbidity: A Latent Profile Analysis
U1 - Healthcare Disparities
U3 - 10.1111/jocn.17520
VL - 35
VO - 0962-1067
Y1 - 2026
ER -