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. METHODS: Retrospective 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. RESULTS: 851.649 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%. CONCLUSION: The 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.

Five years of Hospital at Home adoption in Catalonia: impact, challenges, and proposals for quality assurance.

BACKGROUND: Hospital at home (HaH) was increasingly implemented in Catalonia (7.7 M citizens, Spain) achieving regional adoption within the 2011-2015 Health Plan. This study aimed to assess population-wide HaH outcomes over five years (2015-2019) in a consolidated regional program and provide context-independent recommendations for continuous quality improvement of the service. METHODS: A mixed-methods approach was adopted, combining population-based retrospective analyses of registry information with qualitative research. HaH (admission avoidance modality) was compared with a conventional hospitalization group using propensity score matching techniques. We evaluated the 12-month period before the admission, the hospitalization, and use of healthcare resources at 30 days after discharge. A panel of experts discussed the results and provided recommendations for monitoring HaH services. RESULTS: The adoption of HaH steadily increased from 5,185 episodes/year in 2015 to 8,086 episodes/year in 2019 (total episodes 31,901; mean age 73 (SD 17) years; 79% high-risk patients. Mortality rates were similar between HaH and conventional hospitalization within the episode [76 (0.31%) vs. 112 (0.45%)] and at 30-days after discharge [973(3.94%) vs. 1112(3.24%)]. Likewise, the rates of hospital re-admissions at 30 days after discharge were also similar between groups: 2,00 (8.08%) vs. 1,63 (6.58%)] or ER visits [4,11 (16.62%) vs. 3,97 (16.03%). The 27 hospitals assessed showed high variability in patients' age, multimorbidity, severity of episodes, recurrences, and length of stay of HaH episodes. Recommendations aiming at enhancing service delivery were produced. CONCLUSIONS: Besides confirming safety and value generation of HaH for selected patients, we found that this service is delivered in a case-mix of different scenarios, encouraging hospital-profiled monitoring of the service.

Study protocol of a randomized controlled trial to assess safety of teleconsultation compared with face-to-face consultation: the ECASeT study.

BACKGROUND: The use of remote consultation modalities has exponentially grown in the past few years, particularly since the onset of the COVID-19 pandemic. Although a huge body of the literature has described the use of phone (tele) and video consultations, very few of the studies correspond to randomized controlled trials, and none of them has assessed the safety of these consultation modalities as the primary objective. The primary objective of this trial was to assess the safety of remote consultations (both video and teleconsultation) in the follow-up of patients in the hospital setting. METHODS: Multicenter, randomized controlled trial being conducted in four centers of an administrative healthcare area in Catalonia (North-East Spain). Participants will be screened from all individuals, irrespective of age and sex, who require follow-up in outpatient consultations of any of the departments involved in the study. Eligibility criteria have been established based on the local guidelines for screening patients for remote consultation. Participants will be randomly allocated into one of the two study arms: conventional face-to-face consultation (control) and remote consultation, either teleconsultation or video consultation (intervention). Routine follow-up visits will be scheduled at a frequency determined by the physician based on the diagnostic and therapy of the baseline disease (the one triggering enrollment). The primary outcome will be the number of adverse reactions and complications related to the baseline disease. Secondary outcomes will include non-scheduled visits and hospitalizations, as well as usability features of remote consultations. All data will either be recorded in an electronic clinical report form or retrieved from local electronic health records. Based on the complications and adverse reaction rates reported in the literature, we established a target sample size of 1068 participants per arm. Recruitment started in May 2022 and is expected to end in May 2024. DISCUSSION: The scarcity of precedents on the assessment of remote consultation modalities using randomized controlled designs challenges making design decisions, including recruitment, selection criteria, and outcome definition, which are discussed in the manuscript. TRIAL REGISTRATION: NCT05094180. The items of the WHO checklist for trial registration are available in Additional file 1. Registered on 24 November 2021.

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

Objectives: To assess excess mortality among older adults institutionalized in nursing homes within the successive waves of the COVID-19 pandemic in Catalonia (north-east Spain). Design: Observational, retrospective analysis of population-based central healthcare registries. Setting and participants: Individuals aged >65 years admitted in any nursing home in Catalonia between January 1, 2015, and April 1, 2022. Methods: Deaths 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. Results: The 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. Conclusion: Excess 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.

Is Age the Most Important Risk Factor in COVID-19 Patients? The Relevance of Comorbidity Burden: A Retrospective Analysis of 10,551 Hospitalizations.

Purpose: To assess the contribution of age and comorbidity to the risk of critical illness in hospitalized COVID-19 patients using increasingly exhaustive tools for measuring comorbidity burden. Patients and Methods: We assessed the effect of age and comorbidity burden in a retrospective, multicenter cohort of patients hospitalized due to COVID-19 in Catalonia (North-East Spain) between March 1, 2020, and January 31, 2022. Vaccinated individuals and those admitted within the first of the six COVID-19 epidemic waves were excluded from the primary analysis but were included in secondary analyses. The primary outcome was critical illness, defined as the need for invasive mechanical ventilation, transfer to the intensive care unit (ICU), or in-hospital death. Explanatory variables included age, sex, and four summary measures of comorbidity burden on admission extracted from three indices: the Charlson index (17 diagnostic group codes), the Elixhauser index and count (31 diagnostic group codes), and the Queralt DxS index (3145 diagnostic group codes). All models were adjusted by wave and center. The proportion of the effect of age attributable to comorbidity burden was assessed using a causal mediation analysis. Results: The primary analysis included 10,551 hospitalizations due to COVID-19; of them, 3632 (34.4%) experienced critical illness. The frequency of critical illness increased with age and comorbidity burden on admission, irrespective of the measure used. In multivariate analyses, the effect size of age decreased with the number of diagnoses considered to estimate comorbidity burden. When adjusting for the Queralt DxS index, age showed a minimal contribution to critical illness; according to the causal mediation analysis, comorbidity burden on admission explained the 98.2% (95% CI 84.1-117.1%) of the observed effect of age on critical illness. Conclusion: Comorbidity burden (when measured exhaustively) explains better than chronological age the increased risk of critical illness observed in patients hospitalized with COVID-19.

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.

Introduction: This 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. Methods: Qualitative 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. Results: The 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. Discussion: The 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.

Population-based evaluation of the impact of socioeconomic status on clinical outcomes in patients with heart failure in integrated care settings.

INTRODUCTION AND OBJECTIVES: Low socioeconomic status (SES) is associated with poor outcomes in patients with heart failure (HF). We aimed to examine the influence of SES on health outcomes after a quality of care improvement intervention for the management of HF integrating hospital and primary care resources in a health care area of 209 255 inhabitants. METHODS: We conducted a population-based pragmatic evaluation of the implementation of an integrated HF program by conducting a natural experiment using health care data. We included all individuals consecutively admitted to hospital with at least one ICD-9-CM code for HF as the primary diagnosis and discharged alive in Catalonia between January 1, 2015 and December 31, 2019. We compared outcomes between patients exposed to the new HF program and those in the remaining health care areas, globally and stratified by SES. RESULTS: A total of 77 554 patients were included in the study. Death occurred in 37 469 (48.3%), clinically-related hospitalization in 41 709 (53.8%) and HF readmission in 29 755 (38.4%). On multivariate analysis, low or very low SES was associated with an increased risk of all-cause death and clinically-related hospitalization (all Ps <.05). The multivariate models showed a significant reduction in the risk of all-cause death (HR, 0.812; 95%CI, 0.723-0.912), clinically-related hospitalization (HR, 0.886; 95%CI, 0.805-0.976) and HF hospitalization (HR, 0.838; 95%CI, 0.745-0.944) in patients exposed to the new HF program compared with patients exposed to the remaining health care areas and this effect was independent of SES. CONCLUSIONS: An intensive transitional HF management program improved clinical outcomes, both overall and across SES strata.

Effectiveness of Self-guided Tailored Implementation Strategies in Integrating and Embedding Internet-Based Cognitive Behavioral Therapy in Routine Mental Health Care: Results of a Multicenter Stepped-Wedge Cluster Randomized Trial.

BACKGROUND: Internet-based cognitive behavioral therapy (iCBT) services for common mental health disorders have been found to be effective. There is a need for strategies that improve implementation in routine practice. One-size-fits-all strategies are likely to be ineffective. Tailored implementation is considered as a promising approach. The self-guided integrated theory-based Framework for intervention tailoring strategies toolkit (ItFits-toolkit) supports local implementers in developing tailored implementation strategies. Tailoring involves identifying local barriers; matching selected barriers to implementation strategies; developing an actionable work plan; and applying, monitoring, and adapting where necessary. OBJECTIVE: This study aimed to compare the effectiveness of the ItFits-toolkit with implementation-as-usual (IAU) in implementing iCBT services in 12 routine mental health care organizations in 9 countries in Europe and Australia. METHODS: A stepped-wedge cluster randomized trial design with repeated measures was applied. The trial period lasted 30 months. The primary outcome was the normalization of iCBT delivery by service providers (therapists, referrers, IT developers, and administrators), which was measured with the Normalization Measure Development as a proxy for implementation success. A 3-level linear mixed-effects modeling was applied to estimate the effects. iCBT service uptake (referral and treatment completion rates) and implementation effort (hours) were used as secondary outcomes. The perceived satisfaction (Client Satisfaction Questionnaire), usability (System Usability Scale), and impact of the ItFits-toolkit by implementers were used to assess the acceptability of the ItFits-toolkit. RESULTS: In total, 456 mental health service providers were included in this study. Compared with IAU, the ItFits-toolkit had a small positive statistically significant effect on normalization levels in service providers (mean 0.09, SD 0.04; P=.02; Cohen d=0.12). The uptake of iCBT by patients was similar to that of IAU. Implementers did not spend more time on implementation work when using the ItFits-toolkit and generally regarded the ItFits-toolkit as usable and were satisfied with it. CONCLUSIONS: The ItFits-toolkit performed better than the usual implementation activities in implementing iCBT services in routine practice. There is practical utility in the ItFits-toolkit for supporting implementers in developing and applying effective tailored implementation strategies. However, the effect on normalization levels among mental health service providers was small. These findings warrant modesty regarding the effectiveness of self-guided tailored implementation of iCBT services in routine practice. TRIAL REGISTRATION: ClinicalTrials.gov NCT03652883; https://clinicaltrials.gov/ct2/show/NCT03652883. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-020-04686-4.

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.

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.

Associations between behavioral flexibility and health behavior in cardiac patients in the Do CHANGE trials.

OBJECTIVE: Health behaviors (e.g., physical inactivity, poor diet) are associated with poor prognosis and mortality in cardiac patients. Changing these behaviors is challenging and only a minority of patients succeeds in this endeavor. Studies show that behavioral flexibility (defined as responding less habitually to stimuli and having a large behavioral repertoire) is a potentially important facilitator of health behaviors. The current study examines the association between behavioral flexibility and health behaviors (health responsibility, physical activity, nutrition, spiritual growth, interpersonal relations, stress management) in patients with cardiac disease. METHOD: A total of 387 patients with stable cardiac disease were recruited as part of the Do Cardiac Health: Advanced New Generation Ecosystem Trials. Behavioral flexibility (via the Do Something Different Questionnaire) was assessed at baseline and health behaviors including the above described six domains (HPLP-II at baseline, at 3 months, and at 6 months. Linear mixed models were used to answer the research question. RESULTS: The sample consisted of predominantly male patients (n = 274/71%) with a mean age of 62 (SD = 10), diagnosed with hypertension (n = 198/51%), coronary artery disease (n = 114/30%), and/or heart failure (n = 75/19%). The analyses revealed a positive but small (r = .106-.270, B = .00-.31) association between behavioral flexibility and all self-reported health behaviors over time. CONCLUSIONS: This is the first study to examine the association between behavioral flexibility and health behaviors in cardiac patients. Current results showed a positive association between behavioral flexibility and health behaviors over time. More research is needed to further examine causal effects of behavioral flexibility on health behaviors. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

Development and validation of a population-based risk stratification model for severe COVID-19 in the general population.

The shortage of recently approved vaccines against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the need for evidence-based tools to prioritize healthcare resources for people at higher risk of severe coronavirus disease (COVID-19). Although age has been identified as the most important risk factor (particularly for mortality), the contribution of underlying comorbidities is often assessed using a pre-defined list of chronic conditions. Furthermore, the count of individual risk factors has limited applicability to population-based "stratify-and-shield" strategies. We aimed to develop and validate a COVID-19 risk stratification system that allows allocating individuals of the general population into four mutually-exclusive risk categories based on multivariate models for severe COVID-19, a composite of hospital admission, transfer to intensive care unit (ICU), and mortality among the general population. The model was developed using clinical, hospital, and epidemiological data from all individuals among the entire population of Catalonia (North-East Spain; 7.5 million people) who experienced a COVID-19 event (i.e., hospitalization, ICU admission, or death due to COVID-19) between March 1 and September 15, 2020, and validated using an independent dataset of 218,329 individuals with COVID-19 confirmed by reverse transcription-polymerase chain reaction (RT-PCR), who were infected after developing the model. No exclusion criteria were defined. The final model included age, sex, a summary measure of the comorbidity burden, the socioeconomic status, and the presence of specific diagnoses potentially associated with severe COVID-19. The validation showed high discrimination capacity, with an area under the curve of the receiving operating characteristics of 0.85 (95% CI 0.85-0.85) for hospital admissions, 0.86 (0.86-0.97) for ICU transfers, and 0.96 (0.96-0.96) for deaths. Our results provide clinicians and policymakers with an evidence-based tool for prioritizing COVID-19 healthcare resources in other population groups aside from those with higher exposure to SARS-CoV-2 and frontline workers.

A Web-Based App for Emotional Management During the COVID-19 Pandemic: Platform Development and Retrospective Analysis of its Use Throughout Two Waves of the Outbreak in Spain.

BACKGROUND: Quarantines and nationwide lockdowns implemented for containing the spread of the COVID-19 pandemic may lead to distress and increase the frequency of anxiety and depression symptoms among the general population. During the nationwide lockdown of the first wave of the COVID-19 outbreak in Spain, we developed and launched a web-based app to promote emotional self-care in the general population and facilitate contact with health care professionals. OBJECTIVE: This study aimed to describe a web-based app and analyze its utilization pattern throughout 2 successive waves of the COVID-19 outbreak in Spain. METHODS: Our web-based app targeted all individuals aged 18 years or more and was designed by adapting the contents of a mobile app for adjuvant treatment of posttraumatic stress disorder (ie, the PTSD Coach app) to the general population and the pandemic or lockdown scenario. We retrospectively assessed the utilization pattern of the web-based app using data systematically retrieved from Google Analytics. Data were grouped into 3 time periods, defined using Joinpoint regression analysis of COVID-19 incidence in our area: first wave, between-wave period, and second wave. RESULTS: The resulting web-based app, named gesioemocional.cat, maintains the navigation structure of the PTSD Coach app, with three main modules: tools for emotional self-care, a self-assessment test, and professional resources for on-demand contact. The self-assessment test combines the Patient Health Questionnaire-2 and the 7-item Generalized Anxiety Disorder scale and offers professional contact in the advent of a high level of depression and anxiety; contact is prioritized in accordance with a screening questionnaire administered at the time of obtaining individual consent to be contacted. The tools for emotional self-care can be accessed either on-demand or symptom-driven. The utilization analysis showed a high number of weekly accesses during the first wave. In this period, press releases regarding critical events of the pandemic progression and government decisions on containment measures were followed by a utilization peak, irrespective of the sense (ie, positive or negative) of the information. Positive information pieces (eg, relaxation of containment measures due to a reduction of COVID-19 cases) resulted in a sharp increase in utilization immediately after information release, followed by a successive decline in utilization. The second wave was characterized by a lower and less responsive utilization of the web-based app. CONCLUSIONS: mHealth tools may help the general population cope with stressful conditions associated with the pandemic scenario. Future studies shall investigate the effectiveness of these tools among the general population-including individuals without diagnosed mental illnesses-and strategies to reach as many people as possible.

Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients.

BACKGROUND: Comorbidity burden has been identified as a relevant predictor of critical illness in patients hospitalized with coronavirus disease 2019 (COVID-19). However, comorbidity burden is often represented by a simple count of few conditions that may not fully capture patients' complexity. PURPOSE: To evaluate the performance of a comprehensive index of the comorbidity burden (Queralt DxS), which includes all chronic conditions present on admission, as an adjustment variable in models for predicting critical illness in hospitalized COVID-19 patients and compare it with two broadly used measures of comorbidity. MATERIALS AND METHODS: We analyzed data from all COVID-19 hospitalizations reported in eight public hospitals in Catalonia (North-East Spain) between June 15 and December 8 2020. The primary outcome was a composite of critical illness that included the need for invasive mechanical ventilation, transfer to ICU, or in-hospital death. Predictors including age, sex, and comorbidities present on admission measured using three indices: the Charlson index, the Elixhauser index, and the Queralt DxS index for comorbidities on admission. The performance of different fitted models was compared using various indicators, including the area under the receiver operating characteristics curve (AUROCC). RESULTS: Our analysis included 4607 hospitalized COVID-19 patients. Of them, 1315 experienced critical illness. Comorbidities significantly contributed to predicting the outcome in all summary indices used. AUC (95% CI) for prediction of critical illness was 0.641 (0.624-0.660) for the Charlson index, 0.665 (0.645-0.681) for the Elixhauser index, and 0.787 (0.773-0.801) for the Queralt DxS index. Other metrics of model performance also showed Queralt DxS being consistently superior to the other indices. CONCLUSION: In our analysis, the ability of comorbidity indices to predict critical illness in hospitalized COVID-19 patients increased with their exhaustivity. The comprehensive Queralt DxS index may improve the accuracy of predictive models for resource allocation and clinical decision-making in the hospital setting.

Performance of quantitative measures of multimorbidity: a population-based retrospective analysis.

BACKGROUND: Multimorbidity measures are useful for resource planning, patient selection and prioritization, and factor adjustment in clinical practice, research, and benchmarking. We aimed to compare the explanatory performance of the adjusted morbidity group (GMA) index in predicting relevant healthcare outcomes with that of other quantitative measures of multimorbidity. METHODS: The performance of multimorbidity measures was retrospectively assessed on anonymized records of the entire adult population of Catalonia (North-East Spain). Five quantitative measures of multimorbidity were added to a baseline model based on age, gender, and socioeconomic status: the Charlson index score, the count of chronic diseases according to three different proposals (i.e., the QOF, HCUP, and Karolinska institute), and the multimorbidity index score of the GMA tool. Outcomes included all-cause death, total and non-scheduled hospitalization, primary care and ER visits, medication use, admission to a skilled nursing facility for intermediate care, and high expenditure (time frame 2017). The analysis was performed on 10 subpopulations: all adults (i.e., aged > 17 years), people aged > 64 years, people aged > 64 years and institutionalized in a nursing home for long-term care, and people with specific diagnoses (e.g., ischemic heart disease, cirrhosis, dementia, diabetes mellitus, heart failure, chronic kidney disease, and chronic obstructive pulmonary disease). The explanatory performance was assessed using the area under the receiving operating curves (AUC-ROC) (main analysis) and three additional statistics (secondary analysis). RESULTS: The adult population included 6,224,316 individuals. The addition of any of the multimorbidity measures to the baseline model increased the explanatory performance for all outcomes and subpopulations. All measurements performed better in the general adult population. The GMA index had higher performance and consistency across subpopulations than the rest of multimorbidity measures. The Charlson index stood out on explaining mortality, whereas measures based on exhaustive definitions of chronic diagnostic (e.g., HCUP and GMA) performed better than those using predefined lists of diagnostics (e.g., QOF or the Karolinska proposal). CONCLUSIONS: The addition of multimorbidity measures to models for explaining healthcare outcomes increase the performance. The GMA index has high performance in explaining relevant healthcare outcomes and may be useful for clinical practice, resource planning, and public health research.

Determinants of Catalan public primary care professionals' intention to use digital clinical consultations (eConsulta) in the post-COVID-19 context: optical illusion or permanent transformation?

BACKGROUND: During lockdown due to the COVID-19 pandemic, telemedicine has become a necessary component of clinical practice for the purpose of providing safer patient care, and it has been used to support the healthcare needs of COVID-19 patients and routine primary care patients alike. However, this change has not been fully consolidated. OBJECTIVE: The objective of this study was to analyse the determinants of healthcare professionals' intention to use the eConsulta digital clinical consultations tool in the post-COVID-19 context. METHODS: A literature review of the Technology Acceptance Model (TAM) allowed us to construct a theoretical model and establish a set of hypotheses derived from it about the influence that a variety of different factors relating to both healthcare professionals and the institutions where they work had on those professionals' intention to use eConsulta. In order to confirm the proposed model, a mixed qualitative and quantitative methodology was used, and a questionnaire was designed to serve as the data collection instrument. The data were analysed using univariate and bivariate analysis techniques. To confirm the theoretical model, exploratory factor analysis and binary logistic regression were applied. RESULTS: The most important variables were those referring to perceived benefits (B=2.408) and the type of use that individuals habitually made of eConsulta (B=0.715). Environmental pressure (B=0.678), experience of technology (B=0.542), gender (B=0.639) and the degree of eConsulta implementation (B=0.266) were other variables influencing the intention to use the tool in the post-COVID-19 context. When replicating the previous analysis by professional group, experience of technology and gender in the physician group, and experience of the tool's use and the centre where a professional works in the nurse group, were found to be of considerable importance. CONCLUSIONS: The implementation and use of eConsulta had increased significantly as a consequence of the COVID-19 pandemic, and the majority of the healthcare professionals were satisfied with its use in practice and planned to incorporate it into their practices in the post-COVID-19 context. Perceived benefits and environmental pressure were determining factors in the attitude towards and intention to use eConsulta.

Characteristics of Citizens and Their Use of Teleconsultations in Primary Care in the Catalan Public Health System Before and During the COVID-19 Pandemic: Retrospective Descriptive Cross-sectional Study.

BACKGROUND: eConsulta-that is, asynchronous, two-way teleconsultation in primary care-is one of the most important telemedicine developments in the Catalan public health system, a service that has been heavily boosted by the onset of the COVID-19 pandemic. It is vital to know the characteristics of its users in order to be able to meet their needs and understand the coverage of this service in a context where there is reduced accessibility to the health system. OBJECTIVE: This study aims to analyze the profile of the citizens who use the eConsulta tool and the reasons for their use, as well as to gain an understanding of the elements that characterize their decision to use it while distinguishing between those who used it before and those who have used it since the onset of the COVID-19 pandemic. METHODS: A descriptive, observational study based on administrative data was performed. This study differentiates between the COVID-19 pandemic era and the period preceding it, considering the day the state of emergency was declared in Spain (ie, March 12, 2020) as the cut-off point. It also differentiates between eConsulta users who send messages and those who only receive them. RESULTS: During the pandemic, the number of unique users of this teleconsultation service had almost tripled, with up to 33.10 visits per 1000 inhabitants per month reported in the first three months. For the two user profiles analyzed, most users since the start of the COVID-19 outbreak were predominantly female, systematically younger, more actively employed, and with less complex pathologies. Furthermore, eConsulta users received more messages proactively from the health professionals. There was also a relative decrease in the number of conversations initiated by higher-income urban users and an increase in conversations initiated by users in rural areas. CONCLUSIONS: The COVID-19 pandemic has helped to generalize the use of telemedicine as a tool to compensate, to some extent, for the decline in face-to-face visits, especially among younger citizens in Catalonia. Telemedicine has made it possible to maintain contact between citizens and the health care system in the context of maximum complexity.

Guided Internet-Based Cognitive Behavioral Therapy for Depression: Implementation Cost-Effectiveness Study.

BACKGROUND: Major depressive disorder is a chronic condition; its prevalence is expected to grow with the aging trend of high-income countries. Internet-based cognitive-behavioral therapy has proven efficacy in treating major depressive disorder. OBJECTIVE: The objective of this study was to assess the cost-effectiveness of implementing a community internet-based cognitive behavioral therapy intervention (Super@, the Spanish program for the MasterMind project) for treating major depressive disorder. METHODS: The cost-effectiveness of the Super@ program was assessed with the Monitoring and Assessment Framework for the European Innovation Partnership on Active and Healthy Ageing tool, using a 3-state Markov model. Data from the cost and effectiveness of the intervention were prospectively collected from the implementation of the program by a health care provider in Badalona, Spain; the corresponding data for usual care were gathered from the literature. The health states, transition probabilities, and utilities were computed using Patient Health Questionnaire-9 scores. RESULTS: The analysis was performed using data from 229 participants using the Super@ program. Results showed that the intervention was more costly than usual care; the discounted (3%) and nondiscounted incremental cost-effectiveness ratios were €29,367 and €26,484 per quality-adjusted life-year, respectively (approximately US $35,299 and $31,833, respectively). The intervention was cost-effective based on the €30,000 willingness-to-pay threshold typically applied in Spain (equivalent to approximately $36,060). According to the deterministic sensitivity analyses, the potential reduction of costs associated with intervention scale-up would reduce the incremental cost-effectiveness ratio of the intervention, although it remained more costly than usual care. A discount in the incremental effects up to 5% exceeded the willingness-to-pay threshold of €30,000. CONCLUSIONS: The Super@ program, an internet-based cognitive behavioral therapy intervention for treating major depressive disorder, cost more than treatment as usual. Nevertheless, its implementation in Spain would be cost-effective from health care and societal perspectives, given the willingness-to-pay threshold of €30,000 compared with treatment as usual.

Tailored implementation of internet-based cognitive behavioural therapy in the multinational context of the ImpleMentAll project: a study protocol for a stepped wedge cluster randomized trial.

BACKGROUND: Internet-based Cognitive Behavioural Therapy (iCBT) is found effective in treating common mental disorders. However, the use of these interventions in routine care is limited. The international ImpleMentAll study is funded by the European Union's Horizon 2020 programme. It is concerned with studying and improving methods for implementing evidence-based iCBT services for common mental disorders in routine mental health care. A digitally accessible implementation toolkit (ItFits-toolkit) will be introduced to mental health care organizations with the aim to facilitate the ongoing implementation of iCBT services within local contexts. This study investigates the effectiveness of the ItFits-toolkit by comparing it to implementation-as-usual activities. METHODS: A stepped wedge cluster randomized controlled trial (SWT) design will be applied. Over a trial period of 30 months, the ItFits-toolkit will be introduced sequentially in twelve routine mental health care organizations in primary and specialist care across nine countries in Europe and Australia. Repeated measures are applied to assess change over time in the outcome variables. The effectiveness of the ItFits-toolkit will be assessed in terms of the degree of normalization of the use of the iCBT services. Several exploratory outcomes including uptake of the iCBT services will be measured to feed the interpretation of the primary outcome. Data will be collected via a centralized data collection system and analysed using generalized linear mixed modelling. A qualitative process evaluation of routine implementation activities and the use of the ItFits-toolkit will be conducted within this study. DISCUSSION: The ImpleMentAll study is a large-scale international research project designed to study the effectiveness of tailored implementation. Using a SWT design that allows to examine change over time, this study will investigate the effect of tailored implementation on the normalization of the use of iCBT services and their uptake. It will provide a better understanding of the process and methods of tailoring implementation strategies. If found effective, the ItFits-toolkit will be made accessible for mental health care service providers, to help them overcome their context-specific implementation challenges. TRIAL REGISTRATION: ClinicalTrials.gov NCT03652883 . Retrospectively registered on 29 August 2018.