Impact of the COVID-19 Pandemic on the Incidence of Suicidal Behaviors: A Retrospective Analysis of Integrated Electronic Health Records in a Population of 7.5 Million

The COVID-19 pandemic has caused remarkable psychological overwhelming and an increase in stressors that may trigger suicidal behaviors. However, its impact on the rate of suicidal behaviors has been poorly reported. We conducted a population-based retrospective analysis of all suicidal behaviors attended in healthcare centers of Catalonia (northeast Spain; 7.5 million inhabitants) between January 2017 and June 2022 (secondary use of data routinely reported to central suicide and diagnosis registries). We retrieved data from this period, including an assessment of suicide risk and individuals' socioeconomic as well as clinical characteristics. Data were summarized yearly and for the periods before and after the onset of the COVID-19 pandemic in Spain in March 2020. The analysis included 26,458 episodes of suicidal behavior (21,920 individuals); of these, 16,414 (62.0%) were suicide attempts. The monthly moving average ranged between 300 and 400 episodes until July 2020, and progressively increased to over 600 episodes monthly. In the postpandemic period, suicidal ideation increased at the expense of suicidal attempts. Cases showed a lower suicide risk; the percentage of females and younger individuals increased, whereas the prevalence of classical risk factors, such as living alone, lacking a family network, and a history of psychiatric diagnosis, decreased. In summary, suicidal behaviors have increased during the COVID-19 pandemic, with more episodes of suicidal ideation without attempts in addition to younger and lower risk profiles

Therapist perceptions of the implementation of a new screening procedure using the ItFits-toolkit in an iCBT routine care clinic: A mixed-methods study using the consolidated framework for implementation research

IntroductionThis study investigates the implementation of a new, more automated screening procedure using the ItFits-toolkit in the online clinic, Internet Psychiatry (iPsych) (www.internetpsykiatrien.dk), delivering guided iCBT for mild to moderate anxiety and depressive disorders. The study focuses on how the therapists experienced the process.MethodsQualitative data were collected from semi-structured individual interviews with seven therapists from iPsych. The interviews were conducted using an interview guide with questions based on the Consolidated Framework for Implementation Research (CFIR). Quantitative data on the perceived level of normalization were collected from iPsych therapists, administrative staff, and off-site professionals in contact with the target demographic at 10-time points throughout the implementation.ResultsThe therapists experienced an improvement in the intake procedure. They reported having more relevant information about the patients to be used during the assessment and the treatment; they liked the new design better; there was a better alignment of expectations between patients and therapists; the patient group was generally a better fit for treatment after implementation; and more of the assessed patients were included in the program. The quantitative data support the interview data and describe a process of normalization that increases over time.DiscussionThe ItFits-toolkit appears to have been an effective mediator of the implementation process. The therapists were aided in the process of change, resulting in an enhanced ability to target the patients who can benefit from the treatment program, less expenditure of time on the wrong population, and more satisfied therapists

The adjusted morbidity groups (GMA): an exhaustive and severity-balanced tool for risk assessment

Grups morbiditat ajustada; GMA; Eina d'estratificació; Avaluació de riscosGrupos morbilidad ajustada; GMA; Herramienta de estratificación; Evaluación de riesgosAdjusted morbidity groups; GMA; Stratification tool; Risk assessmentEls GMA consisteixen en una eina que permet avaluar el risc en salut a partir de les característiques demogràfiques dels pacients, les seves malalties cròniques i aquelles situacions o malalties agudes que puguin tenir-hi impacte. Aquesta eina proporciona un índex de risc que es pot utilitzar com a factor d’ajust en models específics d’una determinada malaltia i a la vegada actua com un agrupament per estratificar la població en diferents nivells de risc.Los GMA consisten en una herramienta que permite evaluar el riesgo en salud a partir de las características demográficas de los pacientes, sus enfermedades crónicas y aquellas situaciones o enfermedades agudas que puedan tener impacto. Esta herramienta proporciona un índice de riesgo que se puede utilizar como factor de ajuste en modelos específicos de una determinada enfermedad y al mismo tiempo actúa como un agrupamiento para estratificar la población en diferentes niveles de riesgo.GMAs are a tool that assesses health risk based on the demographic characteristics of patients, their chronic diseases and those situations or acute diseases that may have an impact. This tool provides a risk index that can be used as an adjustment factor in specific models of a given disease and at the same time acts as a grouping to stratify the population at different levels of risk

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study

Background: Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection. Objective: The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes. Methods: Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles. Results: Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%). Conclusions: The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation

Effectiveness of an integrated care program for intensive home care services after discharge of stroke patients

The continuity of care in hospital discharge is a cornerstone of patient-centred care, particularly after an acute episode with a high impact on patients’ autonomy. In the setting of stroke, a highly disabling disease, early delivery of post-discharge support services has been associated with better health outcomes. However, the lack of integration between social and health care services often delays the start of home care services in these patients, likely worsening health outcomes. In our area, a post-stroke intensive home care program (RHP) was launched to integrate social and health care services for improving the domiciliary care of stroke patients after hospital discharge

In the era of remote access to the healthcare system, the accurate measures of patient’s health complexity is a mainstay for a person-centred service allocation

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Healthcare risk stratification model for emergency departments based on drugs, income and comorbidities: the DICER-score

Background During the last decade, the progressive increase in age and associated chronic comorbidities and polypharmacy. However, assessments of the risk of emergency department (ED) revisiting published to date often neglect patients' pharmacotherapy plans, thus overseeing the Drug-related problems (DRP) risks associated with the therapy burden. The aim of this study is to develop a predictive model for ED revisit, hospital admission, and mortality based on patient's characteristics and pharmacotherapy.MethodsRetrospective cohort study including adult patients visited in the ED (triage 1, 2, or 3) of multiple hospitals in Catalonia (Spain) during 2019. The primary endpoint was a composite of ED visits, hospital admission, or mortality 30 days after ED discharge. The study population was randomly split into a model development (60%) and validation (40%) datasets. The model included age, sex, income level, comorbidity burden, measured with the Adjusted Morbidity Groups (GMA), and number of medications. Forty-four medication groups, associated with medication-related health problems, were assessed using ATC codes. To assess the performance of the different variables, logistic regression was used to build multivariate models for ED revisits. The models were created using a stepwise-forward approach based on the Bayesian Information Criterion (BIC). Area under the curve of the receiving operating characteristics (AUCROC) curve for the primary endpoint was calculated.Results851.649 patients were included; 134.560 (15.8%) revisited the ED within 30 days from discharge, 15.2% were hospitalized and 9.1% died within 30 days from discharge. Four factors (sex, age, GMA, and income level) and 30 ATC groups were identified as risk factors and combined into a final score. The model showed an AUCROC values of 0.720 (95%CI:0.718-0.721) in the development cohort and 0.719 (95%CI.0.717-0.721) in the validation cohort. Three risk categories were generated, with the following scores and estimated risks: low risk: 18.3%; intermediate risk: 40.0%; and high risk: 62.6%.ConclusionThe DICER score allows identifying patients at high risk for ED revisit within 30 days based on sociodemographic, clinical, and pharmacotherapeutic characteristics, being a valuable tool to prioritize interventions on discharge. Risk scores are often used to predict the clinical outcomes of patients in many healthcare settings.To the date, no prediction model of emergency department (ED) visits based on patients' pharmacotherapy, income level, and comorbidities have been developed.We have designed an ED risk score combined four risk factors (sex, age, comorbidity score and income level) and 30 drug categories to identify those patients at high risk of health-care visit

Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Background Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model. Methods The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. Results Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively. Conclusions The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates

Excess mortality among older adults institutionalized in long-term care facilities during the COVID-19 pandemic: a population-based analysis in Catalonia

ObjectivesTo assess excess mortality among older adults institutionalized in nursing homes within the successive waves of the COVID-19 pandemic in Catalonia (north-east Spain).DesignObservational, retrospective analysis of population-based central healthcare registries.Setting and participantsIndividuals aged &gt;65 years admitted in any nursing home in Catalonia between January 1, 2015, and April 1, 2022.MethodsDeaths reported during the pre-pandemic period (2015–2019) were used to build a reference model for mortality trends (a Poisson model, due to the event counting nature of the variable “mortality”), adjusted by age, sex, and clinical complexity, defined according to the adjusted morbidity groups. Excess mortality was estimated by comparing the observed and model-based expected mortality during the pandemic period (2020–2022). Besides the crude excess mortality, we estimated the standardized mortality rate (SMR) as the ratio of weekly deaths’ number observed to the expected deaths’ number over the same period.ResultsThe analysis included 175,497 older adults institutionalized (mean 262 days, SD 132), yielding a total of 394,134 person-years: 288,948 person-years within the reference period (2015–2019) and 105,186 within the COVID-19 period (2020–2022). Excess number of deaths in this population was 5,403 in the first wave and 1,313, 111, −182, 498, and 329 in the successive waves. The first wave on March 2020 showed the highest SMR (2.50; 95% CI 2.45–2.56). The corresponding SMR for the 2nd to 6th waves were 1.31 (1.27–1.34), 1.03 (1.00–1.07), 0.93 (0.89–0.97), 1.13 (1.10–1.17), and 1.07 (1.04–1.09). The number of excess deaths following the first wave ranged from 1,313 (2nd wave) to −182 (4th wave). Excess mortality showed similar trends for men and women. Older adults and those with higher comorbidity burden account for higher number of deaths, albeit lower SMRs.ConclusionExcess mortality analysis suggest a higher death toll of the COVID-19 crisis in nursing homes than in other settings. Although crude mortality rates were far higher among older adults and those at higher health risk, younger individuals showed persistently higher SMR, indicating an important death toll of the COVID-19 in these groups of people