Drug prescription clusters in the UK Biobank: An assessment of drug-drug interactions and patient outcomes in a large patient cohort

In recent decades, there has been an increase in polypharmacy, the concurrent administration of multiple drugs per patient. Studies have shown that polypharmacy is linked to adverse patient outcomes and there is interest in elucidating the exact causes behind this observation. In this paper, we are studying the relationship between drug prescriptions, drug-drug interactions (DDIs) and patient mortality. Our focus is not so much on the number of prescribed drugs, the typical metric in polypharmacy research, but rather on the specific combinations of drugs leading to a DDI. To learn the space of real-world drug combinations, we first assessed the drug prescription landscape of the UK Biobank, a large patient data registry. We observed distinct drug constellation patterns driven by the UK Biobank participants' disease status. We show that these drug prescription clusters matter in terms of the number and types of expected DDIs, and may possibly explain observed differences in health outcomes

Drug prescription clusters in the UK Biobank: An assessment of drug-drug interactions and patient outcomes in a large patient cohort

In recent decades, there has been an increase in polypharmacy, the concurrent administration of multiple drugs per patient. Studies have shown that polypharmacy is linked to adverse patient outcomes and there is interest in elucidating the exact causes behind this observation. In this paper, we are studying the relationship between drug prescriptions, drug-drug interactions (DDIs) and patient mortality. Our focus is not so much on the number of prescribed drugs, the typical metric in polypharmacy research, but rather on the specific combinations of drugs leading to a DDI. To learn the space of real-world drug combinations, we first assessed the drug prescription landscape of the UK Biobank, a large patient data registry. We observed distinct drug constellation patterns driven by the UK Biobank participants' disease status. We show that these drug prescription clusters matter in terms of the number and types of expected DDIs, and may possibly explain observed differences in health outcomes

Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models

We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personalized treatment and future outcome trajectories through deep generative time series models. In particular, our framework enables the generation of novel multivariate treatment strategies tailored to the personalized patient history and trained for optimal expected future outcomes based on conditional expected utility maximization. We demonstrate our framework by generating personalized insulin treatment strategies and blood glucose predictions for hospitalized diabetes patients, showcasing the potential of our approach for generating improved personalized treatment strategies. Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose predictionComment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 17 page

Socioeconomic disparities in physical activity, sedentary behavior and sleep patterns among 6- to 9-year-old children from 24 countries in the WHO European region

Physical activity, sedentary behavior, and sleep are important predictors of children's health. This paper aimed to investigate socioeconomic disparities in physical activity, sedentary behavior, and sleep across the WHO European region. This cross-sectional study used data on 124,700 children aged 6 to 9 years from 24 countries participating in the WHO European Childhood Obesity Surveillance Initiative between 2015 and 2017. Socioeconomic status (SES) was measured through parental education, parental employment status, and family perceived wealth. Overall, results showed different patterns in socioeconomic disparities in children's movement behaviors across countries. In general, high SES children were more likely to use motorized transportation. Low SES children were less likely to participate in sports clubs and more likely to have more than 2 h/day of screen time. Children with low parental education had a 2.24 [95% CI 1.94-2.58] times higher risk of practising sports for less than 2 h/week. In the pooled analysis, SES was not significantly related to active play. The relationship between SES and sleep varied by the SES indicator used. Importantly, results showed that low SES is not always associated with a higher prevalence of "less healthy" behaviors. There is a great diversity in SES patterns across countries which supports the need for country-specific, targeted public health interventions.The authors gratefully acknowledge support from a grant from the Russian Government in the context of the WHO European Office for the Prevention and Control of NCDs. Data collection in the countries was made possible through funding from: Croatia: Ministry of Health, Croatian Institute of Public Health and WHO Regional Office for Europe. Albania: World Health Organization (WHO) Country Office Albania and the WHO Regional Office for Europe. Bulgaria: WHO Regional Office for Europe. Czech Republic: Ministry of Health of the Czech Republic, grant nr. AZV MZČR 17-31670 A and MZČR–RVO EÚ 00023761. Denmark: The Danish Ministry of Health. France: Santé publique France, the French Agency for Public Health. Georgia: WHO. Ireland: Health Service Executive. Italy: Italian Ministry of Health; Italian National Institute of Health (Istituto Superiore di Sanità). Kazakhstan: the Ministry of Health of the Republic of Kazakhstan within the scientific and technical program. Kyrgyzstan: World Health Organization. Latvia: Centre for Disease Prevention and Control, Ministry of Health, Latvia. Lithuania: Science Foundation of Lithuanian University of Health Sciences and Lithuanian Science Council and WHO. Malta: Ministry of Health. Montenegro: WHO and Institute of Public Health of Montenegro. Poland: National Health Programme, Ministry of Health. Portugal: Ministry of Health Institutions, the National Institute of Health, Directorate General of Health, Regional Health Directorates and the kind technical support from the Center for Studies and Research on Social Dynamics and Health (CEIDSS). Romania: Ministry of Health. Russian Federation: WHO. San Marino: Health Ministry. Spain: the Spanish Agency for Food Safety & Nutrition. Tajikistan: WHO Country Office in Tajikistan and Ministry of Health and Social Protection; Turkmenistan: WHO Country Office in Turkmenistan and Ministry of Health. Turkey: Turkish Ministry of Health and World Bank. Austria: Federal Ministry of Labor, Social Affairs, Health and Consumer Protection of Austria.info:eu-repo/semantics/publishedVersio

Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991-2019.

BACKGROUND: Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial drug effectiveness (AmE) is influenced by drug resistance, drug quality, health system quality, and patient adherence to drug use; its influence on malaria burden varies through space and time. METHODS: This study uses data from 232 efficacy trials comprised of 86,776 infected individuals to estimate the artemisinin-based and non-artemisinin-based AmE for treating falciparum malaria between 1991 and 2019. Bayesian spatiotemporal models were fitted and used to predict effectiveness at the pixel-level (5 km × 5 km). The median and interquartile ranges (IQR) of AmE are presented for all malaria-endemic countries. RESULTS: The global effectiveness of artemisinin-based drugs was 67.4% (IQR: 33.3-75.8), 70.1% (43.6-76.0) and 71.8% (46.9-76.4) for the 1991-2000, 2006-2010, and 2016-2019 periods, respectively. Countries in central Africa, a few in South America, and in the Asian region faced the challenge of lower effectiveness of artemisinin-based anti-malarials. However, improvements were seen after 2016, leaving only a few hotspots in Southeast Asia where resistance to artemisinin and partner drugs is currently problematic and in the central Africa where socio-demographic challenges limit effectiveness. The use of artemisinin-based combination therapy (ACT) with a competent partner drug and having multiple ACT as first-line treatment choice sustained high levels of effectiveness. High levels of access to healthcare, human resource capacity, education, and proximity to cities were associated with increased effectiveness. Effectiveness of non-artemisinin-based drugs was much lower than that of artemisinin-based with no improvement over time: 52.3% (17.9-74.9) for 1991-2000 and 55.5% (27.1-73.4) for 2011-2015. Overall, AmE for artemisinin-based and non-artemisinin-based drugs were, respectively, 29.6 and 36% below clinical efficacy as measured in anti-malarial drug trials. CONCLUSIONS: This study provides evidence that health system performance, drug quality and patient adherence influence the effectiveness of anti-malarials used in treating uncomplicated falciparum malaria. These results provide guidance to countries' treatment practises and are critical inputs for malaria prevalence and incidence models used to estimate national level malaria burden

Künstliche Intelligenz in der Medizin : Chancen und Risiken der KI

Der Begriff der künstlichen Intelligenz stammt aus den 50er-Jahren des 20. Jahrhunderts und umfasst eine Ansammlung von Technologien, welche einem Computer erlauben, typische Eigenschaften der menschlichen Intelligenz zu emulieren. Anfänglich wurden grosse Hoffnungen in diese Technologien gesetzt und früh wurde versucht, diese in der Medizin zu etablieren. Zu Beginn des 21. Jahrhunderts war es aufgrund der anfänglich ernüchternden Ergebnisse eher still in der Erforschung der KI in der Medizin. Mehrere wichtige Entwicklungen ebneten jedoch den Weg zum Durchbruch der Technologie

Promoting health-enhancing physical activity in Europe: Current state of surveillance, policy development and implementation

This study aims to present information on the surveillance, policy developments, and implementation of physical activity policies in the 28 European Union (EU) countries. Data was collected on the implementation of the EU Recommendation on health-enhancing physical activity (HEPA) across sectors. In line with the monitoring framework proposed in the Recommendation, a questionnaire was designed to capture information on 23 physical activity indicators. Of the 27 EU countries that responded to the survey, 22 have implemented actions on more than 10 indicators, four countries have implemented more than 20 indicators, and one country has fully addressed and implemented all of the 23 indicators of the monitoring framework. The data collected under this HEPA monitoring framework provided, for the first time, an overview of the implementation of HEPA-related policies and actions at the national level throughout the EU. Areas that need more investment are the “Senior Citizens” sector followed by the “Work Environment”, and the “Environment, Urban Planning, and Public Safety” sectors. This information also enabled comparison of the state of play of HEPA policy implementation between EU Member States and facilitated the exchange of good practices

Socioeconomic disparities in physical activity, sedentary behavior and sleep patterns among 6- to 9-year-old children from 24 countries in the WHO European region

Physical activity, sedentary behavior, and sleep are important predictors of children's health. This paper aimed to investigate socioeconomic disparities in physical activity, sedentary behavior, and sleep across the WHO European region. This cross-sectional study used data on 124,700 children aged 6 to 9 years from 24 countries participating in the WHO European Childhood Obesity Surveillance Initiative between 2015 and 2017. Socioeconomic status (SES) was measured through parental education, parental employment status, and family perceived wealth. Overall, results showed different patterns in socioeconomic disparities in children's movement behaviors across countries. In general, high SES children were more likely to use motorized transportation. Low SES children were less likely to participate in sports clubs and more likely to have more than 2 h/day of screen time. Children with low parental education had a 2.24 [95% CI 1.94–2.58] times higher risk of practising sports for less than 2 h/week. In the pooled analysis, SES was not significantly related to active play. The relationship between SES and sleep varied by the SES indicator used. Importantly, results showed that low SES is not always associated with a higher prevalence of “less healthy” behaviors. There is a great diversity in SES patterns across countries which supports the need for country-specific, targeted public health interventions.</p

A Snapshot of European Children’s Eating Habits: Results from the Fourth Round of the WHO European Childhood Obesity Surveillance Initiative (COSI)

Consuming a healthy diet in childhood helps to protect against malnutrition and noncommunicable diseases (NCDs). This cross-sectional study described the diets of 132,489 children aged six to nine years from 23 countries participating in round four (2015-2017) of the WHO European Childhood Obesity Surveillance Initiative (COSI). Children's parents or caregivers were asked to complete a questionnaire that contained indicators of energy-balance-related behaviors (including diet). For each country, we calculated the percentage of children who consumed breakfast, fruit, vegetables, sweet snacks or soft drinks "every day", "most days (four to six days per week)", "some days (one to three days per week)", or "never or less than once a week". We reported these results stratified by country, sex, and region. On a daily basis, most children (78.5%) consumed breakfast, fewer than half (42.5%) consumed fruit, fewer than a quarter (22.6%) consumed fresh vegetables, and around one in ten consumed sweet snacks or soft drinks (10.3% and 9.4%, respectively); however, there were large between-country differences. This paper highlights an urgent need to create healthier food and drink environments, reinforce health systems to promote healthy diets, and continue to support child nutrition and obesity surveillance.These activities were partially funded through a grant from the Russian Government in the context of the WHO European Office for the Prevention and Control of Noncommunicable Diseases. The authors gratefully acknowledge support from the Danish Ministry of Health, the Italian Ministry of Health and Italian National Institute of Health, the National Institute for Health Development in Estonia, the Health Service Executive in the Republic of Ireland, the Ministry of Health in Bulgaria, the Poland National Health Program (grant no. 6/1/3.1.12/NPZ/2016/106/1401, the Czech Republic (grants AZV MZCR 17-31670 A and MZ ˇ CR—RVO E ˇ Ú 00023761), and the Ministry of Health in Latvia. The Spanish study was funded by the Spanish Agency for Food Safety and Nutrition (AESAN). COSI Austria was supported by a grant from the Federal Ministry of Social Affairs, Health, Care and Consumer Protection, Republic of Austria. COSI Turkey gratefully acknowledges the World Bank for the survey credit. COSI Lithuania gratefully acknowledges the WHO representative in Lithuania, Ingrida Zurlyte, for printing the COSI questionnaires. The study in Kazakhstan was funded by the Ministry of Health of the Republic of Kazakhstan within the scientific and technical program.info:eu-repo/semantics/publishedVersio

Socioeconomic disparities in physical activity, sedentary behavior and sleep patterns among 6- to 9-year-old children from 24 countries in the WHO European region

Physical activity, sedentary behavior, and sleep are important predictors of children's health. This paper aimed to investigate socioeconomic disparities in physical activity, sedentary behavior, and sleep across the WHO European region. This cross-sectional study used data on 124,700 children aged 6 to 9 years from 24 countries participating in the WHO European Childhood Obesity Surveillance Initiative between 2015 and 2017. Socioeconomic status (SES) was measured through parental education, parental employment status, and family perceived wealth. Overall, results showed different patterns in socioeconomic disparities in children's movement behaviors across countries. In general, high SES children were more likely to use motorized transportation. Low SES children were less likely to participate in sports clubs and more likely to have more than 2 h/day of screen time. Children with low parental education had a 2.24 [95% CI 1.94-2.58] times higher risk of practising sports for less than 2 h/week. In the pooled analysis, SES was not significantly related to active play. The relationship between SES and sleep varied by the SES indicator used. Importantly, results showed that low SES is not always associated with a higher prevalence of "less healthy" behaviors. There is a great diversity in SES patterns across countries which supports the need for country-specific, targeted public health interventions