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Li S, Zhang L, Yang B, Huang Y, Guan Y, Huang N, Wu Y, Wang W, Wang Q, Cai H, Sun Y, Xu Z, Wu Q. Development and Validation of a Community-Based Prediction Model for Depression in Elderly Patients with Diabetes: A Cross-Sectional Study. Diabetes Metab Syndr Obes 2024; 17:2627-2638. [PMID: 38974949 PMCID: PMC11225955 DOI: 10.2147/dmso.s465052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 06/20/2024] [Indexed: 07/09/2024] Open
Abstract
Background In elderly diabetic patients, depression is often overlooked because professional evaluation requires psychiatrists, but such specialists are lacking in the community. Therefore, we aimed to create a simple depression screening model that allows earlier detection of depressive disorders in elderly diabetic patients by community health workers. Methods The prediction model was developed in a primary cohort that consisted of 210 patients with diabetes, and data were gathered from December 2022 to February 2023. The independent validation cohort included 99 consecutive patients from February 2023 to March 2023. Multivariable logistic regression analysis was used to develop the predictive model. We incorporated common demographic characteristics, diabetes-specific factors, family structure characteristics, the self-perceived burden scale (SPBS) score, and the family APGAR (adaptation, partnership, growth, affection, resolution) score. The performance of the nomogram was assessed with respect to its calibration (calibration curve, the Hosmer-Lemeshow test), discrimination (the area under the curve (AUC)), and clinical usefulness (Decision curve analysis (DCA)). Results The prediction nomogram incorporated 5 crucial factors such as glucose monitoring status, exercise status, monthly income, sleep disorder status, and the SPBS score. The model demonstrated strong discrimination in the primary cohort, with an AUC of 0.839 (95% CI, 0.781-0.897). This discriminative ability was further validated in the validation cohort, with an AUC of 0.857 (95% CI, 0.779-0.935). Moreover, the nomogram exhibited satisfactory calibration. DCA suggested that the prediction of depression in elderly patients with diabetes mellitus was of great clinical value. Conclusion The prediction model provides precise and user-friendly guidance for community health workers in preliminary screenings for depression among elderly patients with diabetes.
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Affiliation(s)
- Shanshan Li
- Medical College, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
- Jiangsu Engineering Research Centers for Cardiovascular and Cerebrovascular Disease and Cancer Prevention and Control, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
| | - Le Zhang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Boyi Yang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Yi Huang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Yuqi Guan
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Nanbo Huang
- Medical College, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
| | - Yingnan Wu
- Medical College, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
| | - Wenshuo Wang
- Medical College, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
| | - Qing Wang
- Medical College, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
| | - Haochen Cai
- Medical College, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
| | - Yong Sun
- Medical College, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
| | - Zijun Xu
- Medical College, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
| | - Qin Wu
- Medical College, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
- Jiangsu Engineering Research Centers for Cardiovascular and Cerebrovascular Disease and Cancer Prevention and Control, Jiangsu Vocational College of Medicine, Yancheng, People’s Republic of China
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Page-Reeves J, Murray-Krezan C, Burge MR, Mishra SI, Regino L, Bleecker M, Perez D, McGrew HC, Bearer EL, Erhardt E. A patient-centered comparative effectiveness research study of culturally appropriate options for diabetes self-management. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.31.23285236. [PMID: 36778329 PMCID: PMC9915824 DOI: 10.1101/2023.01.31.23285236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This project compared the effectiveness of two evidence-based models of culturally competent diabetes health promotion: The Diabetes Self-Management Support Empowerment Model (DSMS), and The Chronic Care Model (CCM). Our primary outcome was improvement in patient capacity for diabetes self-management as measured by the Diabetes Knowledge Questionnaire (DKQ) and the Patient Activation Measure (PAM). Our secondary outcome was patient success at diabetes self-management as measured by improvement in A1c, depression sores using the PHQ-9, and Body Mass Index (BMI). We also gathered data on the cultural competence of the program using the Consumer Assessment of Healthcare Providers and Systems Cultural Competence Set (CAHPS-CC). We compared patient outcomes in two existing sites in Albuquerque, New Mexico that serve a large population of Latino diabetes patients from low-income households. Participants were enrolled as dyads-a patient participant (n=226) and a social support participant (n=226). Outcomes over time and by program were analyzed using longitudinal linear mixed modeling, adjusted for patient participant demographic characteristics and other potential confounding covariates. Secondary outcomes were also adjusted for potential confounders. Interactions with both time and program helped to assess outcomes. This study did not find a difference between the two sites with respect to the primary outcome measures and only one of the three secondary outcomes showed differential results. The main difference between programs was that depression decreased more for CCM than for DSMS. An exploratory, subgroup analysis revealed that at CCM, patient participants with a very high A1c (>10) demonstrated a clinically meaningful decrease. However, given the higher cultural competence rating for the CCM, statistically significant improvement in depression, and the importance of social support to the patients, results suggest that a culturally and contextually situated diabetes self-management and education program design may deliver benefit for patients, especially for patients with higher A1c levels.
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Affiliation(s)
- Janet Page-Reeves
- Department of Family & Community Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
- Office for Community Health, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Cristina Murray-Krezan
- Department of Medicine, Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Shiraz I. Mishra
- Department of Family & Community Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
- Department of Pediatrics, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Lidia Regino
- Office for Community Health, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Molly Bleecker
- Office for Community Health, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Daniel Perez
- Office for Community Health, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | | | - Elaine L. Bearer
- Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Erik Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico, USA
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