Designing the minimum data set of bipolar disorder: A basis for introducing the effective factors in managing, controlling, and monitoring the bipolar disorder

Minimum data set harmonization in the management of cross-border Multi Casualty Incidents. Modified Delphi (VALKYRIES-H2020 project)

INTRODUCTION

There is a need to develop harmonized procedures and a Minimum Data Set (MDS) for cross-border Multi Casualty Incidents (MCI) in medical emergency scenarios to ensure appropriate management of such incidents, regardless of place, language and internal processes of the institutions involved. That information should be capable of real-time communication to the command-and-control chain. It is crucial that the models adopted are interoperable between countries so that the rights of patients to cross-border healthcare are fully respected.

OBJECTIVE

To optimize management of cross-border Multi Casualty Incidents through a Minimum Data Set collected and communicated in real time to the chain of command and control for each incident. To determine the degree of agreement among experts.

METHOD

We used the modified Delphi method supplemented with the Utstein technique to reach consensus among experts. In the first phase, the minimum requirements of the project, the profile of the experts who were to participate, the basic requirements of each variable chosen and the way of collecting the data were defined by providing bibliography on the subject. In the second phase, the preliminary variables were grouped into 6 clusters, the objectives, the characteristics of the variables and the logistics of the work were approved. Several meetings were held to reach a consensus to choose the MDS variables using a Modified Delphi technique. Each expert had to score each variable from 1 to 10. Non-voting variables were eliminated, and the round of voting ended. In the third phase, the Utstein Style was applied to discuss each group of variables and choose the ones with the highest consensus. After several rounds of discussion, it was agreed to eliminate the variables with a score of less than 5 points. In phase four, the researchers submitted the variables to the external experts for final assessment and validation before their use in the simulations. Data were analysed with SPSS Statistics (IBM, version 2) software.

RESULTS

Six data entities with 31 sub-entities were defined, generating 127 items representing the final MDS regarded as essential for incident management. The level of consensus for the choice of items was very high and was highest for the category 'Incident' with an overall kappa of 0.7401 (95% CI 0.1265-0.5812, p 0.000), a good level of consensus in the Landis and Koch model. The items with the greatest degree of consensus at ten were those relating to location, type of incident, date, time and identification of the incident. All items met the criteria set, such as digital collection and real-time transmission to the chain of command and control.

CONCLUSIONS

This study documents the development of a MDS through consensus with a high degree of agreement among a group of experts of different nationalities working in different fields. All items in the MDS were digitally collected and forwarded in real time to the chain of command and control. This tool has demonstrated its validity in four large cross-border simulations involving more than eight countries and their emergency services.

The global prevalence of depression, anxiety, and sleep disorder among patients coping with Post COVID-19 syndrome (long COVID): a systematic review and meta-analysis

BACKGROUND

Post COVID-19 syndrome, also known as "Long COVID," is a complex and multifaceted condition that affects individuals who have recovered from SARS-CoV-2 infection. This systematic review and meta-analysis aim to comprehensively assess the global prevalence of depression, anxiety, and sleep disorder in individuals coping with Post COVID-19 syndrome.

METHODS

A rigorous search of electronic databases was conducted to identify original studies until 24 January 2023. The inclusion criteria comprised studies employing previously validated assessment tools for depression, anxiety, and sleep disorders, reporting prevalence rates, and encompassing patients of all age groups and geographical regions for subgroup analysis Random effects model was utilized for the meta-analysis. Meta-regression analysis was done.

RESULTS

The pooled prevalence of depression and anxiety among patients coping with Post COVID-19 syndrome was estimated to be 23% (95% CI: 20%-26%; I2 = 99.9%) based on data from 143 studies with 7,782,124 participants and 132 studies with 9,320,687 participants, respectively. The pooled prevalence of sleep disorder among these patients, derived from 27 studies with 15,362 participants, was estimated to be 45% (95% CI: 37%-53%; I2 = 98.7%). Subgroup analyses based on geographical regions and assessment scales revealed significant variations in prevalence rates. Meta-regression analysis showed significant correlations between the prevalence and total sample size of studies, the age of participants, and the percentage of male participants. Publication bias was assessed using Doi plot visualization and the Peters test, revealing a potential source of publication bias for depression (p = 0.0085) and sleep disorder (p = 0.02). However, no evidence of publication bias was found for anxiety (p = 0.11).

CONCLUSION

This systematic review and meta-analysis demonstrate a considerable burden of mental health issues, including depression, anxiety, and sleep disorders, among individuals recovering from COVID-19. The findings emphasize the need for comprehensive mental health support and tailored interventions for patients experiencing persistent symptoms after COVID-19 recovery.

A data set for the design and implementation of the upper limb disability registry

BACKGROUND

If the data elements needed for patient registries are not identified, designing and implementing them can be very challenging. Identifying and introducing a Data Set (DS) can help solve this challenge. The aim of this study was to identify and present a DS for the design and implementation of the upper limb disability registry.

MATERIALS AND METHODS

This cross-sectional study was conducted in two phases. In the first phase, to identify the administrative and clinical data elements required for registry, a comprehensive study was conducted in PubMed, Web of Science, and Scopus databases. Then, the necessary data elements were extracted from the studies and a questionnaire was designed based on them. In the second phase, in order to confirm the DS, the questionnaire was distributed to 20 orthopedic, physical medicine and rehabilitation physicians and physiotherapists during a two-round Delphi. In order to analyze the data, the frequency and mean score of each data element were calculated. Data elements that received an agreement more than 75% in the first or two-round Delphi were considered for the final DS.

RESULTS

A total of 81 data elements in five categories of "demographic data", "clinical presentation", "past medical history", "psychological issues", and "pharmacological and non-pharmacological treatments" were extracted from the studies. Finally, 78 data elements were approved by experts as essential data elements for designing a patient registry for upper limb disabilities.

CONCLUSION

In this study, the data elements necessary for the design and implementation of the upper limb disability registry were suggested. This DS can help registry designers and health data administrators know what data needs to be included in the registry system in order to have a successful design and implementation. Moreover, this standardized DS can be effective for integrating and improving the information management of people with upper limb disabilities and used to accurately gather the upper limb disabilities data for research and policymaking purposes.