Diagnosis, Psychiatric

The following distinct sources of the SNIIRAM were used to select persons with depression:

Diagnoses of long-term or costly conditions (Affections de Longue Durée, ALD). Patients with specific long-term or costly conditions may require full coverage for all their condition-related health expenditures upon request by their family doctor and after approval by a health insurance fund medical officer (médecin-conseil) [18 ].

Data from national hospital claims (Programme de Médicalisation des Systèmes d’Information, PMSI) for all inpatient and day-case admissions in public and private general and psychiatric hospitals, containing medical diagnoses defined as ICD-10 codes. In both general and psychiatric hospitals, a principal diagnosis is defined as the main reason for admission, while associated diagnoses provide information about conditions that significantly influenced care during the hospital stay [19 ].

Data concerning all national health insurance reimbursements for drugs, laboratory tests and outpatient medical procedures. Individuals receiving reimbursements for antidepressants (N06A section of the ATC classification except for oxitriptan) can be identified. However, these databases do not contain direct information about the diagnosis justifying the prescription, and these drugs are not specific for depression, as they can also be prescribed for other conditions (bipolar disorders, anxiety or chronic pain). An antidepressant prescription is typically valid 1 month.

All three sources were not considered to be equally reliable for identifying patients with depression. Reliability of the sources was assessed as follows: for the purpose of identifying individuals suffering from depression, full coverage for depression as a specific long-term or costly condition (source 1) was more reliable than the hospital claims database (source 2), which was more reliable than reimbursement for antidepressants (source 3). In the hospital claims database, associated diagnoses reported during general hospital stays were assumed to be less reliable than those reported during psychiatric hospital stays. The reasons underlying this classification of source reliability included (1) the mode of acquisition of the information (diagnoses resulting from medical interviews were regarded as more reliable than hospital diagnostic codes sometimes coded by non-medical staff, themselves regarded as a more reliable diagnostic markers than prescription drugs) and (2) what was as stake when the information was coded (hospital diagnostic codes that had no consequence on costs were regarded as less reliable than codes influencing costs or giving access to benefits). These reasons are described and discussed more thoroughly in the Merits and drawbacks of the various methods section of the Discussion section of this article.
Accordingly, five estimation methods with decreasing order of reliability were defined. ICD-10 codes F32 to F39 were used in all estimation methods to identify depression (either as a full health coverage code or as a principal or associated diagnosis). At least three reimbursements for antidepressants were used to identify treatment by antidepressant. Hospital stays in the last 5 years with a principal or associated diagnosis of depression were used to identify principal diagnosis history and associated diagnosis history of depression respectively.

Method A (Full coverage for depression): Selection of individuals with full coverage for depression as a specific long-term or costly condition during the study (source 1);

Method B (Hospitalisation for depression): Selection of individuals with depression as principal or associated diagnosis in a psychiatric hospital stay or as principal diagnosis in a general hospital stay using two timeframes: (a) the current calendar year and (b) the last two calendar years (source 2). Calendar years were used for technical reasons.

Method C (Current antidepressant treatment + History of hospitalisation during the past 5 years): Selection of individuals treated by antidepressant and with a general hospital principal diagnosis history of depression or a psychiatric hospital principal or associated diagnosis history of depression (combination of sources 2 and 3);

Method D (Hospitalisation in a general hospital with an associated diagnosis of depression): Selection of individuals with depression as associated diagnosis in a general hospital stay using two timeframes: (a) the current calendar year and (b) the last two calendar years (source 2);

Method E (Current antidepressant treatment + History of hospitalisation in a general hospital with an associated diagnosis of depression during the past 5 years): Selection of individuals treated by antidepressant and with a general hospital associated diagnosis history of depression (combination of sources 2 and 3).

Individuals with a hospital diagnosis of bipolar disorder (ICD-10 codes F30 or F31) in the last 5 years or a specific treatment for bipolar disorder (lithium, divalproex or valpromide) were not included in the study.

Subjects were recruited from an obesity treatment center in a university hospital in Taiwan. The obesity treatment center personnel comprised a multi-disciplinary team, and included a surgeon, internal physician, psychiatrist, urologist, obstetrics and gynecology doctor, nurse, case manager, dietician, and physical activity director. The obesity treatments in this center included non-surgical procedures: meal replacement, pharmacotherapy, psychiatric bio-feedback treatment and intra-gastric balloon, and surgery: bariatric surgery (sleeve, band, Roux-en-Y gastric bypass). First of all, the patients made up their mind as to the treatment modality. However, the patients who wanted to receive bariatric surgery had to meet the criteria of morbid obesity. They then needed to undergo a complete pre-operation evaluation, including a psychiatric evaluation. Our hospital has a committee in charge of determining whether the patients are eligible for bariatric surgery.
Patients received a complete physical evaluation during their first visit, and also completed two questionnaires: the Taiwanese Depression Questionnaire (TDQ) and the Chinese Health Questionnaire (CHQ). The TDQ is a 0-3-point, 18-question questionnaire used to screen clinical depressive disorder.
[22 (link)]. The cut-off point in the community population is 18/19 points. The CHQ
[23 (link)] is a 12-question, 2-reverse questions, 0-1-point questionnaire for screening “minor psychiatric disorders” such as anxiety disorder. The cut-off point in community surveys screening minor mental disorders is 4/5 points.
To avoid false negative results, we lowered the cut-off points for the CHQ and TDQ in our clinical practice. Those patients with CHQ <3 and TDQ <13 were regarded as having no psychiatric disorder. If any of the two scores were above the cut-off point (i.e., CHQ ≧3 or TDQ ≧13, or both), the patients would be referred to psychiatrists for further evaluation. The lifetime psychiatric diagnosis was made based on the psychiatrist’s diagnostic interview, using the Structured Clinical Interview for the DSM-IV (SCID).
We recruited all patients that visited the obesity treatment center of E-Da Hospital from January 2007 to December 2010. The exclusion criteria were age younger than 18 years, having incomplete BMI, TDQ or CHQ data, and refusal of psychiatric interview when needed.
All analyses were performed with the Statistical Package for Social Sciences, SPSS Version 17.0. The chi-square test was used to compare differences for categorical variables and the t-test was used to compare differences for continuous variables. The level of statistical significances was 0.05, two-tailed. Logistic regression was applied to examine whether BMI was associated with a psychiatric disorder.
This study was approved by the Institutional Review Board of E-Da Hospital, Taiwan (EMPR-098-073). The study design and performance complied with the Declaration of Helsinki.

The SUD diagnoses and psychiatric diagnoses according to ICD-10 criteria, were made by a medical specialist or clinical psychologist using standardized clinical interviews and tools.
For the purpose of the current study, information on the dependence-level SUD diagnosis and any co-occurring psychiatric diagnosis was obtained from the medical record. The following binary SUD diagnosis (1 = presence, 0 = absence) were included in analyses: Alcohol use disorder (F10); Opioid use disorder (F11); Cannabis use disorder (F12); Sedatives use disorder (F13); Stimulant use disorder (F15). The psychiatric diagnoses were grouped into the following binary variables (1 = presence, 0 = absence): Mood disorders (F30-F39); Anxiety disorders (F40-F49); Personality disorders (F60-F69); ADHD (F90-F90.0), and other psychiatric diagnoses.

Statistical analysis included descriptive statistics for the prevalence of co-occurring psychiatric diagnoses in the total sample, and according to types of SUD diagnoses. We compared the characteristics of patients with - and without COD using proportion tests and independent samples t-tests. The prevalence of types of CODs was examined for the following psychiatric disorders: anxiety (F40-F49); mood (F30-39), ADHD (F90-90.9); personality disorder (F60-69); multiple CODs. Gender differences in the prevalence of each types of CODs were examined using bivariate logistic regression analysis. Bivariate logistic regression analyses were also undertaken to investigate factors associated with relapse. Repeated-measures generalized logistic mixed modeling (GLMM) with a diagonal covariance matrix was used to assess the multivariate association of demographic (age, gender, education), psychological (motivation, mental distress) and types of SUD diagnoses with relapse at 3 month follow-up. The analysis accounted for the prospective nested nature of the data structure (i.e., the same patients nested over time). Since mental distress was measured at two time points (baseline and follow-up), this variable was entered as a time-varying covariate accounting for variation in mental distress across the study period. Variables indicating the center where the patients were treated (unit 1–5) and the length of stay (number of days) were included in the multivariate models to control for any treatment- related differences in relapse rates. We did not incorporate the treatment center variable as a random effect in the analysis due to the small number of patients at each treatment center, which made it complicated to account for the variance of treatment center as a random effect due to the substantial risk of Type II error. The variance inflation factors were < 2 for all independent variables, indicating that multicollinearity was not a concern [35 ]. We ran the GLMM analyses separately for patients with and without COD. SPSS 28 was used for statistical analyses.

The sample comprised 611 patients who were included in the prospective cohort study, of whom 289 patients (47.3%) had at least one co-occurring psychiatric diagnosis (F20-F99).
In total, 426 of the patients participated in the follow-up interview 3 months after discharge from treatment (70%), of whom 206 (48.4%) were patients with COD. The follow-up response rate was similar for patients with COD (71.3%) and those without COD (68.3%). Among patients with COD, those who did not respond were more likely younger (OR = 2.54, p = 0.002), with lower education level (OR = 1.77, p = 0.035), and less likely to have an alcohol use disorder (OR = 0.588, p = 0.053). Among patients without COD, those who were lost for follow-up appeared more likely younger (OR = 2.157, p = 0.002), and without a permanent housing situation (OR = 1.694, p = 0.042). About half of those who were reached at follow-up (n = 227) reported they had been in contact with SUD outpatient treatment services during the last month. Slightly fewer patients (n = 194) reported contact with a community health provider. The probability of contact with outpatient SUD services was somewhat higher for patients with COD (58.3%) than for patients without COD (48.6%) (p = 0.047). There was no difference between the groups regarding any contact with community mental health and addiction services.