Depression and the risk of hospitalization in type 2 diabetes patients: A nested case-control study accounting for non-persistence to antidiabetic treatment

Association between change in alcohol use reported during routine healthcare screening and change in subsequent hospitalization: A retrospective cohort study

BACKGROUND AND AIMS

Primary care systems often screen for unhealthy alcohol use with brief self-report tools such as the 3-item Alcohol Use Disorders Identification Test for consumption (AUDIT-C). There is little research examining whether change in alcohol use measured on the AUDIT-C captures meaningful change in outcomes affected by alcohol use. This study aimed to measure the association between change in AUDIT-C and change in all-cause hospitalization risk, measured in the year after each AUDIT-C.

DESIGN

Retrospective cohort study.

SETTING

Health system in the state of Washington, USA, that conducts annual screening with the AUDIT-C in outpatient care.

PARTICIPANTS

Adults (n = 165 101) who had completed at least two AUDIT-Cs 11-24 months apart (2016-2020).

MEASUREMENTS

AUDIT-C scores were grouped into five risk categories reflecting no drinking (0), drinking without unhealthy alcohol use [1-2 (female)/1-3 (male)] and unhealthy alcohol use with moderate risk [3-6 (female)/4-6 (male)], high risk (7-8), and very high risk (9-12). Changes in AUDIT-C were based on the number of category levels that changed (0-4). Hospitalizations were binary, reflecting one or more hospitalizations in the 365 days after each AUDIT-C, identified from insurance claims.

FINDINGS

Of 165 101 eligible patients, 5.7% and 6.1% were hospitalized the year after the first and second AUDIT-C, respectively. Decreases in AUDIT-C risk category of 1 or ≥2 levels were associated with statistically significant decreases in risk of hospitalization, compared with the change in hospitalization risk for those with no change in AUDIT-C [1-level decrease: ratio of adjusted risk ratios (aRR) = 0.92, 95% confidence interval (CI) = 0.86-0.99; ≥2-level decrease: ratio of aRR = 0.68, 95% CI = 0.58-0.81]. Increases in AUDIT-C risk category of 1 or ≥2 levels were not associated with statistically significant differences in risk of hospitalization, compared with those with no change in AUDIT-C.

CONCLUSIONS

A decrease in AUDIT-C score risk category is associated with a decreased risk of both all-cause hospitalizations and hospitalizations with conditions directly or potentially attributable to alcohol. An increase in AUDIT-C score does not appear to be associated with a change in risk of hospitalization in the following year.

Incidence and factors associated with new depressive episodes in adults with newly treated type 2 diabetes: A cohort study

AIMS

Several methods are available to help identify people with depression; however, there is little guidance on when to start screening. This study estimated the incidence of new depressive episodes and identified factors associated with onset in adults with newly treated type 2 diabetes.

METHODS

Administrative health data from Alberta, Canada was used to identify people starting metformin between April 2011 and March 2015. People with a history of depression before metformin initiation were excluded. Person-time analysis was used to calculate the incidence rate of new depressive episodes over the next 3 years, stratified by sex, age, and year. Multivariable logistic regression was used to identify factors independently associated with a new depressive episode.

RESULTS

42,694 adults initiated metformin; mean age 56 years, 38 % female. A new depressive episode occurred in 2752 (6 %) individuals, mean time to onset was 1.4 years and overall incidence rate was 22.3/1000 person-years. Factors associated with a new depressive episode were female sex, younger age, previous mental health conditions, frequent healthcare utilization, and multiple comorbid conditions.

CONCLUSIONS

Screening for depression should begin within 1-2 years of metformin initiation and focus on females, those < 55 years old, those with a history of mental health conditions, and those with multiple comorbid conditions.

The interface of depression and diabetes: treatment considerations

This state-of-the-art review explores the relationship between depression and diabetes, highlighting the two-way influences that make treatment challenging and worsen the outcomes of both conditions. Depression and diabetes often co-occur and share genetic, lifestyle, and psychosocial risk factors. Lifestyle elements such as diet, physical activity, and sleep patterns play a role on the development and management of both conditions, highlighting the need for integrated treatment strategies. The evidence suggests that traditional management strategies focusing on either condition in isolation fall short of addressing the intertwined nature of diabetes and depression. Instead, integrated care models encompassing psychological support and medical management are recommended to improve treatment efficacy and patient adherence. Such models require collaboration across multiple healthcare disciplines, including endocrinology, psychiatry, and primary care, to offer a holistic approach to patient care. This review also identifies significant patient-related barriers to effective management, such as stigma, psychological resistance, and health literacy, which need to be addressed through patient-centered education and support systems. Future directions for research include longitudinal studies in diverse populations to further elucidate causal relationships and the exploration of novel therapeutic targets, as well as the effectiveness of healthcare models aimed at preventing the onset of one condition in individuals diagnosed with the other.

Depression screening model for middle-aged and elderly diabetic patients in China

Diabetes is a common global disease closely associated with an increased risk of depression. This study analyzed China Health and Retirement Longitudinal Study (CHARLS) data to examine depression in diabetic patients across China. using 29 variables including demographic, behavioral, health conditions, and mental health parameters. The dataset was randomly divided into a 70% training set and a 30% validation set. Predictive factors significantly associated with depression were identified using least absolute shrinkage and selection operator (LASSO) and logistic regression analysis. A nomogram model was constructed using these predictive factors. The model evaluation included the C-index, calibration curves, the Hosmer-Lemeshow test, and DCA. Depression prevalence was 39.1% among diabetic patients. Multifactorial logistic regression identified significant predictors including gender, permanent address, self-perceived health status, presence of lung disease, arthritis, memory disorders, life satisfaction, cognitive function score, ADL score, and social activity. The nomogram model showed high consistency and accuracy, with AUC values of 0.802 for the training set and 0.812 for the validation set. Both sets showed good model fit with Hosmer-Lemeshow P > 0.05. Calibration curves showed significant consistency between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had a good predictive performance. The nomogram developed in this study effectively assesses depression risk in diabetic patients, helping clinicians identify high-risk individuals. This tool could potentially improve patient outcomes.

Depression literacy, mental health literacy, and their relationship with psychological status and quality of life in patients with type 2 diabetes mellitus

Background

This study was conducted to measure depression literacy (D-Lit) and mental health literacy (MHL) and to investigate their relationship with psychological status and quality of life among Iranian patients with type 2 diabetes mellitus (T2DM).

Methods

This cross-sectional study was conducted in 2021 among 400 patients with T2DM in Iran. Samples were selected using proportional stratified sampling. Data collection tools comprised a demographic questionnaire, measures of MHL and D-Lit, the diabetes quality of life (DQOL) scale, and the DASS-21. After confirming the normality of the data using the Kolmogorov-Smirnov test, parametric statistical tests (such as one-way ANOVA, independent samples t-test, and Chi-Square) were used to investigate the relationship between the variables using SPSS v22 software. The results of continuous quantitative data are reported in the form of means and standard deviations, and qualitative data are reported in the form of absolute and relative frequencies.

Results

In this study, 10.25% of the participants (n = 41) had severe depression, while 36.75% (n = 147) experienced severe anxiety. The mean (standard deviation) of MHL was 80.92 (9.16) from 130 points. Of the participants, only 1.7% (n = 7) did not answer any questions correctly on the D-lit scale, and only 5.8% (n = 23) were able to answer 15 questions or more correctly on the D-lit. MHL had a significant negative correlation with depression (r = -0.236), anxiety (r = -0.243), and stress (r = -0.155) (P < 0.001). There was a positive and significant correlation between MHL and D-Lit (r = 0.186) (P < 0.001). D-Lit had a significant negative correlation with depression (r = -0.192), anxiety (r = -0.238), and stress (r = -0.156) (P < 0.001). There was a positive and significant correlation between the ability to recognize disorders (r = 0.163), knowledge of self-treatment (r = 0.154), and DQOL (P < 0.001). Depression (r = -0.251), anxiety (r = -0.257), and stress (r = -0.203) had a significant negative correlation with DQOL (P < 0.001).

Conclusion

MHL and D-Lit levels were found to be inadequate in patients with T2DM. These low levels of MHL and D-Lit among patients with T2DM were associated with higher levels of anxiety, depression, and stress, as well as a lower quality of life. Therefore, designing and implementing preventive programs to improve the mental health of patients with T2DM can help prevent mental disorders and ultimately improve their quality of life.

Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study

OBJECTIVE

Due to heterogeneity and limited medical data in primary healthcare services (PHS), assessing the psychological risk of type 2 diabetes mellitus (T2DM) patients in PHS is difficult. Using unsupervised contrastive pre-training, we proposed a deep learning framework named depression and anxiety prediction (DAP) to predict depression and anxiety in T2DM patients.

MATERIALS AND METHODS

The DAP model consists of two sub-models. Firstly, the pre-trained model of DAP used unlabeled discharge records of 85 085 T2DM patients from the First Affiliated Hospital of Nanjing Medical University for unsupervised contrastive learning on heterogeneous electronic health records (EHRs). Secondly, the fine-tuned model of DAP used case-control cohorts (17 491 patients) selected from 149 596 T2DM patients' EHRs in the Nanjing Health Information Platform (NHIP). The DAP model was validated in 1028 patients from PHS in NHIP. Evaluation included receiver operating characteristic area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC), and decision curve analysis (DCA).

RESULTS

The pre-training step allowed the DAP model to converge at a faster rate. The fine-tuned DAP model significantly outperformed the baseline models (logistic regression, extreme gradient boosting, and random forest) with ROC-AUC of 0.91±0.028 and PR-AUC of 0.80±0.067 in 10-fold internal validation, and with ROC-AUC of 0.75 ± 0.045 and PR-AUC of 0.47 ± 0.081 in external validation. The DCA indicate the clinical potential of the DAP model.

CONCLUSION

The DAP model effectively predicted post-discharge depression and anxiety in T2DM patients from PHS, reducing data fragmentation and limitations. This study highlights the DAP model's potential for early detection and intervention in depression and anxiety, improving outcomes for diabetes patients.

Prevalence of comorbid depression and associated factors among hospitalized patients with type 2 diabetes mellitus in Hunan, China

BACKGROUND

Depression and diabetes are major health challenges, with heavy economic social burden, and comorbid depression in diabetes could lead to a wide range of poor health outcomes. Although many descriptive studies have highlighted the prevalence of comorbid depression and its associated factors, the situation in Hunan, China, remains unclear. Therefore, this study aimed to identify the prevalence of comorbid depression and associated factors among hospitalized type 2 diabetes mellitus (T2DM) patients in Hunan, China.

METHODS

This cross-sectional study involved 496 patients with T2DM who were referred to the endocrinology inpatient department of Xiangya Hospital affiliated to Central South University, Hunan. Participants' data on socio-demographic status, lifestyle factors, T2DM-related characteristics, and social support were collected. Depression was evaluated using the Hospital Anxiety and Depression Scale-depression subscale. All statistical analyses were conducted using the R software version 4.2.1.

RESULTS

The prevalence of comorbid depression among hospitalized T2DM patients in Hunan was 27.22% (95% Confidence Interval [CI]: 23.3-31.1%). Individuals with depression differed significantly from those without depression in age, educational level, per capita monthly household income, current work status, current smoking status, current drinking status, regular physical activity, duration of diabetes, hypertension, chronic kidney disease, stroke, fatty liver, diabetic nephropathy, diabetic retinopathy, insulin use, HbA1c, and social support. A multivariable logistic regression model showed that insulin users (adjusted OR = 1.86, 95% CI: 1.02-3.42) had a higher risk of depression, while those with regular physical activity (adjusted OR = 0.48, 95% CI: 0.30-0.77) or greater social support (adjusted OR = 0.20, 95% CI: 0.11-0.34) had a lower risk of depression. The area under the curve of the receiver operator characteristic based on this model was 0.741 with a sensitivity of 0.785 and specificity of 0.615.

CONCLUSIONS

Depression was moderately prevalent among hospitalized T2DM patients in Hunan, China. Insulin treatment strategies, regular physical activity, and social support were significantly independently associated with depression, and the multivariable model based on these three factors demonstrated good predictivity, which could be applied in clinical practice.