Assessing the exposure-response relationship of sleep disturbance and vibration in field and laboratory settings

Exposure to nocturnal freight train vibrations may impact sleep, but exposure-response relationships are lacking. The European project CargoVibes evaluated sleep disturbance both in the field and in the laboratory and provides unique data, as measures of response and exposure metrics are comparable. This paper therefore provides data on exposure-response relationships of vibration and sleep disturbance and compares the relationships evaluated in the laboratory and the field. Two field studies (one in Poland and one in the Netherlands) with 233 valid respondents in total, and three laboratory studies in Sweden with a total of 59 subjects over 350 person-nights were performed. The odds ratios (OR) of sleep disturbance were analyzed in relation to nighttime vibration exposure by ordinal logit regression, adjusting for moderating factors common for the studies. Outcome specific fractions were calculated for eleven sleep outcomes and supported comparability between the field and laboratory settings. Vibration exposure was significantly associated to sleep disturbance, OR = 3.51 (95% confidence interval 2.6–4.73) denoting a three and a half times increased odds of sleep disturbance with one unit increased 8 h nighttime log10 Root Mean Square vibration. The results suggest no significant difference between field and laboratory settings OR = 1.37 (0.59–3.19). However, odds of sleep disturbance were higher in the Netherlands as compared to Sweden, indicating unexplained differences between study populations or countries, possibly related to cultural and contextual differences and uncertainties in exposure assessments. Future studies should be carefully designed to record explanatory factors in the field and enhance ecological validity in the laboratory. Nevertheless, the presented combined data set provides a first set of exposure response relationships for vibration-induced sleep disturbance, which are useful when considering public health outcomes among exposed populations

Hair cortisol-a method to detect chronic cortisol levels in patients with Prader-Willi syndrome

Background: Prader-Willi syndrome (PWS) is a multisymptomatic, rare, genetic, neurodevelopmental disorder in adults mainly characterized by hyperphagia, cognitive dysfunction, behavioral problems and risk of morbid obesity. Although endocrine insufficiencies are common, hypocortisolism is rare and knowledge on long-term cortisol concentrations is lacking. The aim of this study was to evaluate long-term cortisol levels in PWS by measurements of hair cortisol. Methods: Twenty-nine adults with PWS, 15 men and 14 women, median age 29 years, median BMI 27 kg/m2 , were included. Scalp hair samples were analyzed for cortisol content using liquid-chromatography tandem-mass spectrometry. In addition, a questionnaire on auxology, medication and stress were included. For comparison, 105 age- and sex-matched participants from the population-based Lifelines Cohort study were included as controls. The mean hair cortisol between the groups were compared and associations between BMI and stress were assessed by a generalized linear regression model. Results: In the PWS group large variations in hair cortisol was seen. Mean hair cortisol was 12.8 ± 25.4 pg/mg compared to 3.8 ± 7.3 pg/mg in controls (p = 0.001). The linear regression model similarly showed higher cortisol levels in patients with PWS, which remained consistent after adjusting for BMI and stress (p = 0.023). Furthermore, hair cortisol increased with BMI (p = 0.012) and reported stress (p = 0.014). Conclusion: Long-term cortisol concentrations were higher in patients with PWS compared to controls and increased with BMI and stress, suggesting an adequate cortisol response to chronic stress. Hair cortisol demonstrate promising applications in the context of PWS treatment and disease management

A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk

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

Operational guide: Early Warning and Response System (EWARS) for dengue outbreaks

This publication is designed to provide programme managers with a user-friendly tool that can: (i) analyse and draw conclusions from historic dengue datasets; (ii) identify appropriate alarm indicators that can sensitively and specifically predict forthcoming outbreaks at smaller spatial scales; and (iii) use these results and analyses to predict and build an early warning system to detect dengue outbreaks in real-time. Together, these three components build technical capacity and provide a standardized methodology for predicting dengue outbreaks in countries where skills and resources are currently constrained. This guide was produced by TDR together with the World Health Organization’s Neglected Tropical Diseases department and regional offices, in the context of a European Union-financed research programme, the International Research Consortium on Dengue Risk Assessment, management and Surveillance (IDAMS), to develop an evidence-based early warning system for outbreak detection and management of dengue fever outbreaks

Exploring Sick Leave in Integrative Care - Retrospective Observations and Future Study Recommendations

© 2019, Mary Ann Liebert, Inc., publishers 2019. To describe and contrast the prevalence and trends of sick leave in patients with pain or stress disorders referred to inpatient care that integrates conventional and complementary therapies, that is, integrative care (IC). Methods: County council and social insurance data were used to retrospectively observe cross-sectional sick leave prevalence at four time points: 1 year before the first registered inpatient visit with the target diagnosis, after referral at index, and at 1 and 2 years after index. To contrast the IC findings, observations of patients with similar background characteristics referred to conventional care (CC) were used. Results: The sick leave prevalence of IC pain patients and IC stress patients increased from the preceding year to peak at index, where after it decreased back toward preindex levels over 2 years. Overall sick leave prevalence was higher in IC than in CC, where analogous but lower prevalence trends of sick leave changes were observed. Conclusions: Observed sick leave prevalences, which were higher in IC than in CC, gradually decreased over time following IC or CC referral. While natural recovery or other reasons for change of sick leave cannot be excluded, future prospective and randomized clinical trials are recommended

Early warning and response system (EWARS) for dengue outbreaks: Recent advancements towards widespread applications in critical settings

BACKGROUND: Dengue outbreaks are increasing in frequency over space and time, affecting people's health and burdening resource-constrained health systems. The ability to detect early emerging outbreaks is key to mounting an effective response. The early warning and response system (EWARS) is a toolkit that provides countries with early-warning systems for efficient and cost-effective local responses. EWARS uses outbreak and alarm indicators to derive prediction models that can be used prospectively to predict a forthcoming dengue outbreak at district level. METHODS: We report on the development of the EWARS tool, based on users' recommendations into a convenient, user-friendly and reliable software aided by a user's workbook and its field testing in 30 health districts in Brazil, Malaysia and Mexico. FINDINGS: 34 Health officers from the 30 study districts who had used the original EWARS for 7 to 10 months responded to a questionnaire with mainly open-ended questions. Qualitative content analysis showed that participants were generally satisfied with the tool but preferred open-access vs. commercial software. EWARS users also stated that the geographical unit should be the district, while access to meteorological information should be improved. These recommendations were incorporated into the second-generation EWARS-R, using the free R software, combined with recent surveillance data and resulted in higher sensitivities and positive predictive values of alarm signals compared to the first-generation EWARS. Currently the use of satellite data for meteorological information is being tested and a dashboard is being developed to increase user-friendliness of the tool. The inclusion of other Aedes borne viral diseases is under discussion. CONCLUSION: EWARS is a pragmatic and useful tool for detecting imminent dengue outbreaks to trigger early response activities.</p