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- Ljungman, S., et al.
(author)
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Factors associated with time to first dialysis-associated peritonitis episode: Data from the Peritonitis Prevention Study (PEPS)
- 2023
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In: Peritoneal Dialysis International. - 0896-8608. ; 43:3, s. 241-251
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Journal article (peer-reviewed)abstract
- Introduction: Peritonitis remains a potentially serious complication of peritoneal dialysis (PD) treatment. It is therefore important to identify risk factors in order to reduce the incidence of peritonitis. The aim of the present analysis was to identify factors associated with time to first peritonitis episode. Methods: Incident PD patients from 57 centres in Europe participated in the prospective randomised controlled Peritonitis Prevention Study (PEPS) from 2010 to 2015. Peritonitis-free, self-care PD patients >= 18 years were randomised to a retraining or a control group and followed for 1-36 months after PD initiation. The association of biochemical, clinical and prescription data with time to first peritonitis episode was studied. Results: A first peritonitis episode was experienced by 33% (223/671) of participants. Univariable Cox proportional hazard regression showed a strong association between the time-updated number of PD bags connected per 24 h (PD bags/24 h) and time to first peritonitis episode (HR 1.35; 95% confidence interval (CI) 1.17-1.57), even after inclusion of PD modalities in the same model. Multivariable Cox regression revealed that the factors independently associated with time to first peritonitis episode included age (HR 1.16 per 10 years; 95% CI 1.05-1.28), PD bags/24 h (HR 1.32; 95% CI 1.13-1.54), serum albumin >35 g/L (HR 1.39; 95% CI 1.06-1.82) and body weight per 10 kg (HR 1.10; 95% CI 1.01-1.19). Conclusion: This study of incident PD patients indicates that older age, greater number of PD bags connected/24 h, higher body weight and hypoalbuminaemia are independently associated with a shorter time to first peritonitis episode.
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- Lim, Ah-Young, et al.
(author)
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A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk
- 2023
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In: BMC Infectious Diseases. - : BioMed Central (BMC). - 1471-2334. ; 23:1
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Journal article (peer-reviewed)abstract
- Background: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used.Methods: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.).Results: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002–2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures.Conclusions: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.
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| 4. |
- Lim, Ah-Young, et al.
(author)
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A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk.
- 2023
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In: BMC Infectious Diseases. - : BioMed Central (BMC). - 1471-2334. ; 23:1
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Journal article (peer-reviewed)abstract
- Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used.We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.).We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures.Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.
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| 5. |
- Mounzer, Wissam, et al.
(author)
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A follow-up study of early intensive behavioral intervention program for children with Autism in Syria
- 2023
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In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 13:1
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Journal article (peer-reviewed)abstract
- We examined the sustained effects of early intensive behavioral intervention (EIBI) on 66 children with autism spectrum disorder who participated in the Future Center’s EIBI program. Children were assessed using the childhood autism rating scale (CARS), autism behavior checklist (ABC), and adaptive behavior scale (ABS-Arabic) 3 years after leaving the program. Continued positive effects were observed in several areas, including adaptive behavior and autism symptoms. However, participants’ social skills might have declined on the ABS-Arabic after service withdrawal. Additionally, a significant negative association (p < .001) was found between participants’ performance on the CARS and the number of weekly trials. This is encouraging, given the lack of EIBI services and regional instability. Future research should increase the sample size and use a more rigorous design.
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