The blue side of diabetes: assessing the impact of depression in people with type 2 diabetes using real-world data

The aim of the thesis is to assess the impact of depression in people with type 2 diabetes. Using Healthcare Utilization Databases, I estimated in a large population-based cohort with type 2 diabetes the incidence of depression over 10 year-period, identified the demographic and clinical predictors of depression, and determined the extent to which depression is a risk factor for acute and long-term complications and mortality. In the context of COVID-19 pandemic, I evaluated whether the presence of a history of depression in type 2 diabetes increased the Emergency Department (ED) access rate for diabetes-related complications, and I investigated changes in the incidence of depression during the first year of the pandemic. Findings from the first study indicated that developing depression was associated with being a woman, being over 65 years, living in rural areas, having insulin as initial diabetes medication and having comorbid conditions; the study also confirmed that depression was associated with an increased risk for acute and long-term diabetes complications and all-cause mortality. The second observational study showed a higher rate of ED access for diabetes-related complications during the pandemic in people with type 2 diabetes and a history of depression than in those without a history of depression, similar to what was observed in a pre-pandemic period. As shown in the third population-based study, the incidence of depression decreased in 2020 compared to 2019, mainly during the first and the second waves of the COVID-19 pandemic, when people probably had difficulty reaching healthcare services. This new real-world evidence will help healthcare professionals identify timely patients at high risk of developing depression. Lastly, policymakers and physicians will benefit from new evidence of the effects of the COVID-19 pandemic on depression in people with type 2 diabetes to ensure a high level of care during crisis periods

A multi-state analysis of disease trajectories and mental health transitions in patients with type 2 diabetes: A population-based retrospective cohort study utilizing health administrative data

Aims: To investigate the risk of major depression and dementia in patients with type 2 diabetes, including dementia resulting from depression, and their impact on diabetes-related complications and mortality. Methods: We conducted a population-based retrospective cohort study including 11,441 incident cases of diabetes in 2015-2017, with follow-up until 2022. A multi-state survival analysis was performed on a seven-state model with 15 transitions to capture disease progression and onset of mental disorders. Results: Eight-year probabilities of depression, dementia, diabetes-related complications, and death were 9.7% (95% CI 8.7-10.7), 0.9% (95% CI 0.5-1.3), 10.4% (95% CI 9.5-11.4), and 14.8% (95% CI 13.9-15.7), respectively. Depression increased the risk of dementia up to 3.7% (95% CI 2.0-5.4), and up to 10.3% (95% CI 0.3-20.4) if coupled with diabetes complications. Eight-year mortality was 37.5% (95% CI 33.1-42.0) after depression, 74.1% (95% CI 63.7-84.5) after depression plus complications, 76.4% (95% CI 68.8-83.9) after dementia, and 98.6% (95% CI 96.1-100.0) after dementia plus complications. Conclusions: The interconnections observed across depression, dementia, complications, and mortality underscore the necessity for comprehensive and integrated approaches in managing diabetes. Early screening for depression, followed by timely and targeted interventions, may mitigate the risk of dementia and improve diabetes prognosis

Assessing attitudes towards insulin pump therapy in adults with type 1 diabetes: Italian validation of the Insulin Pump Attitudes Questionnaire (IT-IPA questionnaire)

Aims: The aim of the study was to adapt the German version of the insulin pump therapy (IPA) questionnaire to Italian (IT-IPA) and to evaluate its psychometric properties in adults with type 1 diabetes. Methods: We conducted a cross-sectional study, data were collected through an online survey. In addition to IT-IPA, questionnaires evaluating depression, anxiety, diabetes distress, self-efficacy, and treatment satisfaction were administered. The six factors identified in the IPA German version were assessed using confirmatory factor analysis; psychometric testing included construct validity and internal consistency. Results: The online survey was compiled by 182 individuals with type 1 diabetes: 45.6% continuous subcutaneous insulin infusion (CSII) users and 54.4% multiple daily insulin injection users. The six-factor model had a very good fit in our sample. The internal consistency was acceptable (Cronbach's α = 0.75; 95% IC [0.65-0.81]). Diabetes treatment satisfaction was positively correlated with a positive attitude towards CSII therapy (Spearman's rho = 0.31; p < 0.01), less Technology Dependency, higher Ease of Use, and less Impaired Body Image. Furthermore, less Technology Dependency was associated with lower diabetes distress and depressive symptoms. Conclusions: The IT-IPA is a valid and reliable questionnaire evaluating attitudes towards insulin pump therapy. The questionnaire can be used for clinical practice during consultations for shared decision-making to CSII therapy

Decision-making and risk-taking in forensic and non-forensic patients with schizophrenia spectrum disorders: A multicenter European study

Studies of patients with schizophrenia and offenders with severe mental disorders decision-making performance have produced mixed findings. In addition, most earlier studies have assessed decision-making skills in offenders or people with mental disorders, separately, thus neglecting the possible additional contribution of a mental disorder on choice patterns in people who offend. This study aimed to fill this gap by comparing risk-taking in patients with schizophrenia spectrum disorders (SSD), with and without a history of serious violent offending assessing whether, and to what extent, risk-taking represents a significant predictor of group membership, controlling for their executive skills, as well as for sociodemographic and clinical characteristics. Overall, 115 patients with a primary diagnosis of SSD were recruited: 74 were forensic patients with a lifetime history of severe interpersonal violence and 41 were patients with SSD without such a history. No significant group differences were observed on psychopathological symptoms severity. Forensic generally displayed lower scores than non-forensic patients in all cognitive subtests of the Brief Assessment of Cognition in Schizophrenia (except for the "token motor" and the "digit sequencing" tasks) and on all the six dimensions of the Cambridge Gambling Task, except for "Deliberation time", in which forensic scored higher than non-forensic patients. "Deliberation time" was also positively, although weakly correlated with "poor impulse control". Identifying those facets of impaired decision-making mostly predicting offenders' behaviour among individuals with mental disorder might inform risk assessment and be targeted in treatment and rehabilitation protocols

Comparing risk adjustment estimation methods under data availability constraints

none4siThe Italian National Healthcare Service relies on per capita allocation for healthcare funds, despite having a highly detailed and wide range of data to potentially build a complex risk-adjustment formula. However, heterogeneity in data availability limits the development of a national model. This paper implements and evaluates machine learning (ML) and standard risk-adjustment models on different data scenarios that a Region or Country may face, to optimize information with the most predictive model. We show that ML achieves a small but generally statistically insignificant improvement of adjusted R2 and mean squared error with fine data granularity compared to linear regression, while in coarse granularity and poor range of variables scenario no differences were observed. The advantage of ML algorithms is greater in the coarse granularity and fair/rich range of variables set and limited with fine granularity scenarios. The inclusion of detailed morbidity- and pharmacy- based adjustors generally increases fit, although the trade-off of creating adverse economic incentives must be considered.mixedIommi, Marica; Bergquist, Savannah; Fiorentini, Gianluca; Paolucci, FrancescoIommi, Marica; Bergquist, Savannah; Fiorentini, Gianluca; Paolucci, Francesc

Efficacy and safety of 24-week pramipexole augmentation in patients with treatment resistant depression. A retrospective cohort study

Pramipexole is a dopamine agonist with potential antidepressant, neuroprotective, antioxidant and antiinflammatory activity. In the present study we investigated the 24 weeks effect and safety of traditional AD augmentation with pramipexole for treatment-resistant depression. The study includes 116 patients, 37 (32%) with bipolar disorders and 79 (68%) with major depressive disorder, who failed to respond to at least 2 ADs trials of different classes and that were treated with AD augmented with pramipexole. Mood stabilizers and/or second-generation antipsychotics were added in patients with bipolar or mixed depression. Exclusion criteria were psychotic depression, rapid cycling bipolar course and previous unsuccessful treatment with pramipexole. After 24 weeks of pramipexole augmentation (median max dose 1.05 mg/day, IQR 0.72-1.08) 74.1% of patients responded (>= 50% reduction of baseline Hamilton Depression Rating Scale21 total score) and 66.4% remitted (Hamilton Depression Rating Scale21 total score < 7). Global Assessment of Functioning score significantly increase from 53 (50-60) at baseline to 80 (71-81) at 24 weeks (Wilcoxon signed rank test = 8.174, p < 0.001]. Ten patients (8.6%) dropped out (8 due to side effects and 2 for lack of efficacy) and 1 experienced an induced hypomanic switch. No patient committed a suicide attempt, had suicidal ideation, needed hospitalization, reported lethargy, gambling, hypersexuality and compulsive shopping. The limitations of the study are the observational design, the lack of a control group, the inclusion of outpatients only, the unblinded outcomes assessment, and the flexibility of the add-on schedule. The findings of the present study showed that off-label use of pramipexole as augmentation of traditional AD is an effective and safe 24 weeks treatment of resistant unipolar and bipolar depression. These results need confirmation from randomized clinical trials on larger samples

Designing feasible and effective health plan payments in countries with data availability constraints

Risk equalization schemes, which transfer money to/from insurers that have above/below average risks, are a fundamental tool in regulated health insurance markets in many countries. Risk sharing (the transfer of some responsibility for costs from a plan to the regulator or the overall insurance market), are an additional method of insulating insurers who attract higher-than-average risks. This paper proposes, implements and quantifies incorporating risk sharing within a risk equalization scheme that can be applied in a data-poor context. Using Chile's private health insurance market as case study, we show that modest amount of risk sharing greatly improves fit even in simple demographic-based risk equalization. Expanding the model's formula to include morbidity-based adjustors and risk sharing redirects compensations at insurer level and reduces opportunity to engage in profitable risk selection at the group level. Our emphasis on feasibility may make alternatives proposed attractive to countries facing data-availability constraints

INTEGRAZIONE DI DATABASE CLINICI E FLUSSI AMMINISTRATIVI PER VALUTARE GESTIONE ED ESITI NEL DMT2

Obiettivo: l’obiettivo di questo studio di coorte retrospettivo è indagare la relazione fra aderenza alle visite ed agli esami annuali, previsti dalle linee guida della Regione Emilia-Romagna per la gestione integrata del diabete di tipo 2, e il controllo glicemico nonché l’insorgenza di complicanze. Metodi: la coorte di pazienti è stata reclutata per lo studio sulla valutazione dell’autoefficacia nel 2017 (CE-AVEC, n. 31/2017/U/Oss), per il quale sono state rilevate le informazioni cliniche dalla cartella informatizzata MyStar Connect. Attraverso record-linkage, il database clinico è stato integrato con i flussi amministrativi Assistenza Specialistica Ambulatoriale (ASA) e Scheda di Dimissione Ospedaliera (SDO) relativi al periodo 2002-2017. Dal flusso ASA sono stati estratti il numero annuale di visite specialistiche diabetologiche e di esami richiesti per la gestione del diabete al fine di calcolare l’aderenza media del paziente ai controlli ambulatoriali, mentre da SDO sono stati rilevati i ricoveri per complicanze Micro- e Macrovascolari. Per valutare la relazione dell’aderenza ai controlli ambulatoriali rispetto al valore di emoglobina glicata e rispetto all’insorgenza di complicanze sono stati utilizzati modelli multipli di regressione logistica. Risultati: il merge ha permesso di collegare univocamente 94 di 142 pazienti ai flussi amministrativi. Dalla analisi è emerso che l’aderenza alla visita diabetologica, tra i controlli previsti, riduce significativamente il rischio di sviluppare complicanze (OR=0,02; 95% IC [0,002; 0,181]). Non sono state invece evidenziate relazioni rispetto al controllo glicemico. Conclusioni: da questo studio emerge il ruolo centrale dello specialista nella presa in carico del paziente con diabete come consultant e coordinatore nei team multidisciplinari per ridurre il rischio di complicanze del diabete. Sarebbe inoltre auspicabile facilitare l’integrazione tra i database clinici e i flussi amministrativi, ricchi di informazioni e accessibili con costi e tempi limitati, al fine di individuare i fattori clinici-organizzativi che determinano gli esiti di salute del paziente cronico. Attraverso questi sistemi di monitoraggio è infatti possibile agire in maniera preventiva sullo sviluppo delle complicanze e favorire una medicina proattiva per la gestione della cronicità

Modified-Chronic Disease Score (M-CDS): Predicting the individual risk of death using drug prescriptions.

BackgroundEstimating the morbidity of a population is strategic for health systems to improve healthcare. In recent years administrative databases have been increasingly used to predict health outcomes. In 1992, Von Korff proposed a Chronic Disease Score (CDS) to predict 1-year mortality by only using drug prescription data. Because pharmacotherapy underwent many changes over the last 3 decades, the original version of the CDS has limitations. The aim of this paper is to report on the development of the modified version of the CDS.MethodsThe modified CDS (M-CDS) was developed using 33 variables (from drug prescriptions within two-year before 01/01/2018) to predict one-year mortality in Bologna residents aged ≥50 years. The population was split into training and testing sets for internal validation. Score weights were estimated in the training set using Cox regression model with LASSO procedure for variables selection. The external validation was carried out on the Imola population. The predictive ability of M-CDS was assessed using ROC analysis and compared with that of the Charlson Comorbidity Index (CCI), that is based on hospital data only, and of the Multisource Comorbidity Score (MCS), which uses hospital and pharmaceutical data.ResultsThe predictive ability of M-CDS was similar in the training and testing sets (AUC 95% CI: training [0.760-0.770] vs. testing [0.750-0.772]) and in the external population (Imola AUC 95% CI [0.756-0.781]). M-CDS was significantly better than CCI (M-CDS AUC = 0.761, 95% CI [0.750-0.772] vs. CCI-AUC = 0.696, 95% CI [0.681-0.711]). No significant difference was found between M-CDS and MCS (MCS AUC = 0.762, 95% CI [0.749-0.775]).ConclusionsM-CDS, using only drug prescriptions, has a better performance than the CCI score in predicting 1-year mortality, and is not inferior to the multisource comorbidity score. M-CDS can be used for population risk stratification, for risk-adjustment in association studies and to predict the individual risk of death

Socio-economic status and severity of plaque psoriasis: a cross-sectional study in the metropolitan city of Bologna

Only a limited amount of data is available on the demographic and socio-economic status of psoriasis patients and its correlation with disease severity and impact on quality of life. The aim of this study was to investigate whether the socio-economic status of psoriasis patients is associated with higher PASI (Psoriasis Area Severity Index) and DLQI (Dermatology Life Quality Index) scores and global severity of disease. A total of 300 adults with plaque psoriasis, attending our psoriasis clinic for the first time (January 2015 to April 2018), were included in the study. Severity of psoriasis was defined according to three different definitions: PASI > 10, DLQI >10, and global severity based on the "Rule of tens" > 10%. The three outcomes were compared between patients with mild psoriasis and those with moderate-to-severe psoriasis using the t-test and χ2-test. Multiple logistic regression analyses were used to evaluate the association between each of the three outcomes and clinical and socio-economic features. Patients with lower educational level, employed in manual or office work, and with lower income were more likely to have moderate-to-severe psoriasis, considering the PASI and DLQI scores separately and the global severity of disease. The association between severity of disease and income was also significant based on multiple regression models. This study confirms the negative association between psoriasis severity and socio-economic status and is aimed at raising awareness among health professionals to investigate and consider this aspect in the management and therapeutic decisions in affected patients