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Sharing research data to improve public health

To receive the best care, people share their health data (HD) with their health practitioners (known as sharing HD for primary purposes). However, during the past two decades, sharing for other (i.e., secondary) purposes has become of great importance in numerous fields, including public health, personalized medicine, research, and development. We aimed to conduct the first comprehensive overview of all studies that investigated people's HD sharing attitudes—along with associated barriers/motivators and significant influencing factors—for all data types and across both primary and secondary uses.

We searched PubMed, MEDLINE, PsycINFO, Web of Science, EMBASE, and CINAHL for relevant studies published in English between database inception and February 28, 2023, using a predefined set of keywords. Studies were included, regardless of their design, if they reported outcomes related to attitudes towards sharing HD. We extracted key data from the included studies, including the type of HD involved and findings related to: HD sharing attitudes (either in general or depending on type of data/user); barriers/motivators/benefits/concerns of the study participants; and sociodemographic and other variables that could impact HD sharing behaviour. The qualitative synthesis was conducted by dividing the studies according to the data type (resulting in five subgroups) as well as the purpose the data sharing was focused on (primary, secondary or both). The Newcastle–Ottawa Scale (NOS) was used to assess the quality of non-randomised studies. This work was registered with PROSPERO, CRD42023413822.

Of 2109 studies identified through our search, 116 were included in the qualitative synthesis, yielding a total of 228,501 participants and various types of HD represented: person-generated HD (n = 17 studies and 10,771 participants), personal HD in general (n = 69 studies and 117,054 participants), Biobank data (n = 7 studies and 27,073 participants), genomic data (n = 13 studies and 54,716 participants), and miscellaneous data (n = 10 studies and 18,887 participants). The majority of studies had a moderate level of quality (83 [71.6%] of 116 studies), but varying levels of quality were observed across the included studies. Overall, studies suggest that sharing intentions for primary purposes were observed to be high regardless of data type, and it was higher than sharing intentions for secondary purposes. Sharing for secondary purposes yielded variable findings, where both the highest and the lowest intention rates were observed in the case of studies that explored sharing biobank data (98% and 10%, respectively). Several influencing factors on sharing intentions were identified, such as the type of data recipient, data, consent. Further, concerns related to data sharing that were found to be mutual for all data types included privacy, security, and data access/control, while the perceived benefits included those related to improvements in healthcare. Findings regarding attitudes towards sharing varied significantly across sociodemographic factors and depended on data type and type of use. In most cases, these findings were derived from single studies and therefore warrant confirmations from additional studies.

Sharing health data is a complex issue that is influenced by various factors (the type of health data, the intended use, the data recipient, among others) and these insights could be used to overcome barriers, address people's concerns, and focus on spreading awareness about the data sharing process and benefits.

None.

Los indicadores del estado de pandemia se han basado en el número total de casos diagnosticados de la COVID-19, el número de personas hospitalizadas o en unidades de cuidados intensivos y los fallecimientos por la infección. El objetivo de este estudio es describir los datos disponibles sobre pruebas diagnósticas, servicio sanitario utilizado para el diagnóstico de COVID-19 y seguimiento/detección de casos.

Estudio descriptivo con revisión de datos oficiales disponibles en las páginas web de las consejerías de sanidad de España correspondientes a 17 comunidades autónomas, 2 ciudades autónomas y el Ministerio de Sanidad. Las variables recogidas hacen referencia al estudio de contactos, diagnóstico de casos, uso de servicios sanitarios y seguimiento.

Todas las regiones de España muestran datos de los casos diagnosticados de COVID-19 y fallecidos. Los casos hospitalizados e ingresos en cuidados intensivos se muestran en todas las regiones excepto Baleares. Las pruebas diagnósticas de COVID-19 se han registrado en todas las regiones excepto en la Comunidad de Madrid y en Extremadura, habiendo poca información sobre qué tipo de prueba se ha realizado (presente en 7 comunidades autónomas), servicio peticionario y estudio de contactos.

La información disponible en las páginas web oficiales de las consejerías de sanidad de las diferentes regiones de España son heterogéneas. Los datos sobre el uso o carga laboral a nivel de atención primaria o servicios de urgencias hospitalarios y extrahospitalarios son cuasi inexistentes.

The indicators of the pandemic have been based on the total number of diagnosed cases of COVID-19, the number of people hospitalized or in intensive care units, and deaths from the infection. The aim of this study is to describe the available data on diagnostic tests, health service used for the diagnosis of COVID-19, case detection and monitoring.

Descriptive study with review of official data available on the websites of the Spanish health councils corresponding to 17 Autonomous Communities, 2 Autonomous cities and the Ministry of Health. The variables collected refer to contact tracing, technics for diagnosis, use of health services and follow-up.

All regions of Spain show data on diagnosed cases of COVID-19 and deaths. Hospitalized cases and intensive care admissions are shown in all regions except the Balearic Islands. Diagnostic tests for COVID-19 have been registered in all regions except Madrid region and Extremadura, with scarcely information on what type of test has been performed (present in 7 Autonomous Communities), requesting service and study of contacts.

The information available on the official websites of the Health Departments of the different regions of Spain are heterogeneous. Data from the use of health service or workload in primary care, emergency department or out of hours services are almost non-existent.

Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment. Thanks to technological advancements, many millions of healthy and diseased genomes have already been sequenced; however, obtaining the most suitable data for a specific study, and specifically for validation studies, remains challenging with respect to scale and access. Therefore, in silico genomics sequence generators have been proposed as a possible solution. However, the current generators produce inferior data using mostly shallow (stochastic) connections, detected with limited computational complexity in the training data. This means they do not take the appropriate biological relations and constraints, that originally caused the observed connections, into consideration. To address this issue, we propose cancer-inspired genomics mapper model (CGMM), that combines genetic algorithm (GA) and deep learning (DL) methods to tackle this challenge. CGMM mimics processes that generate genetic variations and mutations to transform readily available control genomes into genomes with the desired phenotypes. We demonstrate that CGMM can generate synthetic genomes of selected phenotypes such as ancestry and cancer that are indistinguishable from real genomes of such phenotypes, based on unsupervised clustering. Our results show that CGMM outperforms four current state-of-the-art genomics generators on two different tasks, suggesting that CGMM will be suitable for a wide range of purposes in genomic medicine, especially for much-needed validation studies.

Longitudinal cohorts can provide timely and cost-efficient evidence about the best points of health service and preventive interventions over the life course. Working systematically across cohorts has the potential to further exploit these valuable data assets, such as by improving the precision of estimates, enhancing (or appropriately reducing) confidence in the replicability of findings, and investigating interrelated questions within a broader theoretical model. In this conceptual review, we explore the opportunities and challenges presented by multi-cohort approaches in life course research. Specifically, we: 1) describe key motivations for multi-cohort work and the analytic approaches that are commonly used in each case; 2) flag some of the scientific and pragmatic challenges that arise when adopting these approaches; and 3) outline emerging directions for multi-cohort work in life course research. Harnessing their potential while thoughtfully considering limitations of multi-cohort approaches can contribute to the robust and granular evidence base needed to promote health and wellbeing over the life span.

HIV-1 pol sequences from antiretroviral therapy (ART)-naive and ART-experienced people living with HIV-1 are fundamental to understanding the genetic correlates and epidemiology of HIV-1 drug resistance (HIVDR). To assess the public availability of HIV-1 pol sequences and ART histories of the individuals from whom sequenced viruses were obtained, we performed a systematic review of PubMed and GenBank for HIVDR studies published between 2010 and 2019 that reported HIV-1 pol sequences. 934 studies met inclusion criteria, including 461 studies of ART-naive adults, 407 of ART-experienced adults, and 66 of ART-naive and ART-experienced children. Sequences were available for 317 (68·8%) studies of ART-naive individuals, 190 (46·7%) of ART-experienced individuals, and 45 (68·2%) of children. Among ART-experienced individuals, sequences plus linked ART histories were available for 82 (20·1%) studies. Sequences were available for 21 (29·2%) of 72 clinical trials. Among journals publishing more than ten studies, the proportion with available sequences ranged from 8·3% to 86·9%. Strengthened implementation of data sharing policies is required to increase the number of studies with available HIVDR data to support the enterprise of global ART in the face of emerging HIVDR.