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 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.

Turning the Crisis Into an Opportunity: Digital Health Strategies Deployed During the COVID-19 Outbreak

Digital health technologies offer significant opportunities to reshape current health care systems. From the adoption of electronic medical records to mobile health apps and other disruptive technologies, digital health solutions have promised a better quality of care at a more sustainable cost. However, the widescale adoption of these solutions is lagging behind. The most adverse scenarios often provide an opportunity to develop and test the capacity of digital health technologies to increase the efficiency of health care systems. Catalonia (Northeast Spain) is one of the most advanced regions in terms of digital health adoption across Europe. The region has a long tradition of health information exchange in the public health care sector and is currently implementing an ambitious digital health strategy. In this viewpoint, we discuss the crucial role digital health solutions play during the coronavirus disease (COVID-19) pandemic to support public health policies. We also report on the strategies currently deployed at scale during the outbreak in Catalonia.

Primary Care Doctor Characteristics That Determine the Use of Teleconsultations in the Catalan Public Health System: Retrospective Descriptive Cross-Sectional Study

BACKGROUND

eConsulta is a tele-consultation service involving doctors and patients, and is part of Catalonia's public health information technology system. The service has been in operation since the end of 2015 as an adjunct to face-to-face consultations. A key factor in understanding the barriers and facilitators to the acceptance of the tool is understanding the sociodemographic characteristics of general practitioners who determine its use.

OBJECTIVE

This study aimed to analyze the sociodemographic factors that affect the likelihood of doctors using eConsulta.

METHODS

A retrospective cross-sectional analysis of administrative data was used to perform a multivariate logistic regression analysis on the use of eConsulta in relation to sociodemographic variables.

RESULTS

The model shows that the doctors who use eConsulta are 45-54 years of age, score higher than the 80th percentile on the quality of care index, have a high degree of accessibility, are involved in teaching, and work on a health team in a high socioeconomic urban setting.

CONCLUSIONS

The results suggest that certain sociodemographic characteristics associated with general practitioners determine whether they use eConsulta. These results must be taken into account if its deployment is to be encouraged in the context of a public health system.

Validez de los grupos de morbilidad ajustados respecto a los clinical risk groups en el ámbito de la atención primaria

Objetivo

Comparar el rendimiento referente a la bondad de ajuste y el poder explicativo de 2 agrupadores de morbilidad en el ámbito de la atención primaria (AP): los grupos de morbilidad ajustados (GMA) y los clinical risk groups (CRG).

Diseño

Estudio transversal.

Emplazamiento

Ámbito de la AP del Instituto Catalán de la Salud (ICS), Cataluña, España.

Participantes

Población asignada a centros de AP del ICS para el año 2014.

Mediciones principales

Se analizan 3 indicadores de interés, como son el ingreso urgente, el número de visitas y el gasto en farmacia. Se aplica un análisis estratificado por centros ajustando modelos lineales generalizados a partir de las variables edad, sexo y agrupador de morbilidad para explicar cada una de las 3 variables de interés. Las medidas estadísticas para analizar el rendimiento de los distintos modelos aplicados son el índice de Akaike, el índice de Bayes y la seudovariabilidad explicada mediante cambio de deviance.

Resultados

Los resultados muestran que en el ámbito de la AP del ICS el poder explicativo de los GMA es superior al ofrecido por los CRG, especialmente para el caso de las visitas y el gasto en farmacia.

Conclusiones

El rendimiento de los GMA en el ámbito de la AP del ICS es superior al mostrado por los CRG.

[Clinical validation of 2 morbidity groups in the primary care setting]

INTRODUCTION

Adjusted Morbidity Groups (GMAs) and the Clinical Risk Groups (CRGs) are population morbidity based stratification tools which classify patients into mutually exclusive categories.

OBJETIVE

To compare the stratification provided by the GMAs, CRGs and that carried out by the evaluators according to the levels of complexity.

DESIGN

Random sample stratified by morbidity risk.

LOCATION

Catalonia.

PARTICIPANTS

Forty paired general practitioners in the primary care, matched pairs.

INTERVENTIONS

Each pair of evaluators had to review 25 clinical records.

MAIN OUTPUTS

The concordance by evaluators, and between the evaluators and the results obtained by the 2 morbidity tools were evaluated according to the kappa index, sensitivity, specificity, and positive and negative predicted values.

RESULTS

The concordance between general practitioners pairs was around the kappa value 0.75 (mean value=0.67), between the GMA and the evaluators was similar (mean value=0.63), and higher than for the CRG (mean value=0.35). The general practitioners gave a score of 7.5 over 10 to both tools, although for the most complex strata, according to the professionals' assignment, the GMA obtained better scores than the CRGs. The professionals preferred the GMAs over the CRGs. These differences increased with the complexity level of the patients according to clinical criteria. Overall, less than 2% of serious classification errors were found by both groupers.

CONCLUSION

The evaluators considered that both grouping systems classified the studied population satisfactorily, although the GMAs showed a better performance for more complex strata. In addition, the clinical raters preferred the GMAs in most cases.