STI epidemic re-emergence, socio-epidemiological clusters characterisation and HIV coinfection in Catalonia, Spain, during 2017-2019 : A retrospective population-based cohort study

Objectives To describe the epidemiology of sexually transmitted infections (STIs), identify and characterise socio-epidemiological clusters and determine factors associated with HIV coinfection. Design Retrospective population-based cohort. Setting Catalonia, Spain. Participants 42 283 confirmed syphilis, gonorrhoea, chlamydia and lymphogranuloma venereum cases, among 34 600 individuals, reported to the Catalan HIV/STI Registry in 2017-2019. Primary and secondary outcomes Descriptive analysis of confirmed STI cases and incidence rates. Factors associated with HIV coinfection were determined using logistic regression. We identified and characterized socio-epidemiological STI clusters by Basic Health Area (BHA) using K-means clustering. Results The incidence rate of STIs increased by 91.3% from 128.2 to 248.9 cases per 100 000 population between 2017 and 2019 (p<0.001), primarily driven by increase among women (132%) and individuals below 30 years old (125%). During 2017-2019, 50.1% of STIs were chlamydia and 31.6% gonorrhoea. Reinfections accounted for 10.8% of all cases and 6% of cases affected HIV-positive individuals. Factors associated with the greatest likelihood of HIV coinfection were male sex (adjusted OR (aOR) 23.69; 95% CI 16.67 to 35.13), age 30-39 years (versus <20 years, aOR 18.58; 95% CI 8.56 to 52.13), having 5-7 STI episodes (vs 1 episode, aOR 5.96; 95% CI 4.26 to 8.24) and living in urban areas (aOR 1.32; 95% CI 1.04 to 1.69). Living in the most deprived BHAs (aOR 0.60; 95% CI 0.50 to 0.72) was associated with the least likelihood of HIV coinfection. K-means clustering identified three distinct clusters, showing that young women in rural and more deprived areas were more affected by chlamydia, while men who have sex with men in urban and less deprived areas showed higher rates of STI incidence, multiple STI episodes and HIV coinfection. Conclusions We recommend socio-epidemiological identification and characterisation of STI clusters and factors associated with HIV coinfection to identify at-risk populations at a small health area level to design effective interventions

Cohort profile: The Ageing Trajectories of Health – Longitudinal Opportunities and Synergies (ATHLOS) project

This project, Ageing Trajectories of Health – Longitudinal Opportunities and Synergies (ATHLOS), funded by the European Union’s Horizon 2020 Research and Innovation Program, aims to achieve a better understanding of the impact of ageing on health by developing a new single measure of health status. With this measure, the project intends to identify patterns of healthy ageing trajectories and their determinants, the critical points in time when changes in trajectories are produced, and to propose timely clinical and public health interventions to optimize and promote healthy ageing. To achieve this, a new cohort has been composed from harmonized datasets of existing international longitudinal cohorts related to health and ageing

Development of a common scale for measuring healthy ageing across the world: results from the ATHLOS consortium

Background: Research efforts to measure the concept of healthy ageing have been diverse and limited to specific populations. This diversity limits the potential to compare healthy ageing across countries and/or populations. In this study, we developed a novel measurement scale of healthy ageing using worldwide cohorts. Methods: In the Ageing Trajectories of Health-Longitudinal Opportunities and Synergies (ATHLOS) project, data from 16 international cohorts were harmonized. Using ATHLOS data, an item response theory (IRT) model was used to develop a scale with 41 items related to health and functioning. Measurement heterogeneity due to intra-dataset specificities was detected, applying differential item functioning via a logistic regression framework. The model accounted for specificities in model parameters by introducing cohort-specific parameters that rescaled scores to the main scale, using an equating procedure. Final scores were estimated for all individuals and converted to T-scores with a mean of 50 and a standard deviation of 10. Results: A common scale was created for 343 915 individuals above 18 years of age from 16 studies. The scale showed solid evidence of concurrent validity regarding various sociodemographic, life and health factors, and convergent validity with healthy life expectancy (r = 0.81) and gross domestic product (r = 0.58). Survival curves showed that the scale could also be predictive of mortality. Conclusions: The ATHLOS scale, due to its reliability and global representativeness, has the potential to contribute to worldwide research on healthy ageing

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

Implementation of the HepClink test-and-treat community strategy targeting Pakistani migrants with hepatitis C living in Catalonia (Spain) compared with the current practice of the Catalan health system: budget impact analysis

Objectives To perform a budget impact analysis of the HepClink test-and-treat strategy in which community health agents offer hepatitis C virus (HCV) testing, diagnosis and treatment to the Pakistani population living in Catalonia compared with the current practice of the Catalan health system (without targeted screening programmes).Methods We estimated the population of adult Pakistani migrants registered at the primary care centres in Catalonia by means of the Information System for the Development of Research in Primary Care (n=37 972 in 2019, Barcelona health area). This cohort was followed for a time period of 10 years after HCV diagnosis (2019–2028). The statistical significance of the differences observed in the anti-HCV positivity rate between screened and non-screened was confirmed (α=0.05). The budget impact was calculated from the perspective of the Catalan Department of Health. Sensitivity analyses included different levels of participation in HepClink: pessimistic, optimistic and maximum.Results The HepClink scenario screened a higher percentage of individuals (69.8%) compared with the current scenario of HCV care (39.7%). Viraemia was lower in the HepClink scenario compared with the current scenario (1.7% vs 2.5%, respectively). The budget impact of the HepClink scenario was €884 244.42 in 10 years.Conclusions Scaling up the HepClink strategy to the whole Catalan territory infers a high budget impact for the Department of Health and allows increasing the detection of viraemia (+17.8%) among Pakistani migrants ≥18 years. To achieve a sustainable elimination of HCV by improving screening and treatment rates, there is room for improvement at two levels. First, taking advantage of the fact that 68.08% of the Pakistani population had visited their primary care physicians to reinforce targeted screening in primary care. Second, to use HepClink at the community level to reach individuals with reluctance to use healthcare services

Enhancing the Human Health Status Prediction: The ATHLOS Project

Preventive healthcare is a crucial pillar of health as it contributes to staying healthy and having immediate treatment when needed. Mining knowledge from longitudinal studies has the potential to significantly contribute to the improvement of preventive healthcare. Unfortunately, data originated from such studies are characterized by high complexity, huge volume, and a plethora of missing values. Machine Learning, Data Mining and Data Imputation models are utilized a part of solving these challenges, respectively. Toward this direction, we focus on the development of a complete methodology for the ATHLOS Project – funded by the European Union’s Horizon 2020 Research and Innovation Program, which aims to achieve a better interpretation of the impact of aging on health. The inherent complexity of the provided dataset lies in the fact that the project includes 15 independent European and international longitudinal studies of aging. In this work, we mainly focus on the HealthStatus (HS) score, an index that estimates the human status of health, aiming to examine the effect of various data imputation models to the prediction power of classification and regression models. Our results are promising, indicating the critical importance of data imputation in enhancing preventive medicine’s crucial role

Enhancing the Human Health Status Prediction: The ATHLOS Project

Preventive healthcare is a crucial pillar of health as it contributes to staying healthy and having immediate treatment when needed. Mining knowledge from longitudinal studies has the potential to significantly contribute to the improvement of preventive healthcare. Unfortunately, data originated from such studies are characterized by high complexity, huge volume, and a plethora of missing values. Machine Learning, Data Mining and Data Imputation models are utilized a part of solving these challenges, respectively. Toward this direction, we focus on the development of a complete methodology for the ATHLOS Project–funded by the European Union’s Horizon 2020 Research and Innovation Program, which aims to achieve a better interpretation of the impact of aging on health. The inherent complexity of the provided dataset lies in the fact that the project includes 15 independent European and international longitudinal studies of aging. In this work, we mainly focus on the HealthStatus (HS) score, an index that estimates the human status of health, aiming to examine the effect of various data imputation models to the prediction power of classification and regression models. Our results are promising, indicating the critical importance of data imputation in enhancing preventive medicine’s crucial role. © 2021 Taylor & Francis

A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project

The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP). © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG

A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project

The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP)