Model Estimates Research Articles

The Impact of Non-Normality on Causal Effect Estimates in Structural Equation Models with Ordered Categorical Variables

The Impact of Non-Normality on Causal Effect Estimates in Structural Equation Models with Ordered Categorical Variables

Value Chain Analysis of Haricot Bean in East Shewa Zone, Oromia Regional State, Ethiopia

The key objectives of the study were identifying haricot bean value chain actors, their functions and relationship among them, examining actor’s performance along the chain and analyzing factors affecting supply to the market and factors affecting market outlet choice in the study area. Both primary and secondary sources were used for data collection. The primary data was collected through interviewing 180 sample household farmers and 40 traders using semi-structured questionnaires. STATA version 15 Software was used for data analyzing. As the descriptive result indicate that, on average farmers allocate 0.72ha of land for haricot bean production, from which 21.72qt/ha of output gained. In the value chain of haricot bean input suppliers, farmers, collectors, traders, transporters and consumers are the major value chain actors who perform various value chain. From the study result, six marketing channels of haricot bean are exhibited in the study areas. Estimates of the multiple linear regression models indicate that the level of haricot bean supplied to the market is determined by educational level of household, haricot bean farming experience, land allocated for haricot bean, market price of haricot bean, TLU, access to credit, and access to market information. Therefore, policy makers should focus more on the abovementioned variables in the future intervention to increase volume of haricot bean supplied to the market. As the multivariate probit model result indicated that the market outlet choice of rural assemblers was positively and significantly influenced by distance to the nearest market center, distance to all weather road, and educational level whereas it’s negatively and significantly influenced by output level where as selling direct to consumers outlet choice was negatively and significantly affected by sex of household, educational level, farm experience of household in haricot bean, and output level. Urban traders/wholesalers market outlet was positively and significantly affected by sex of household, educational level, haricot bean farm experience, access to credit, access to market information, membership of cooperative, and output level whereas it’s negatively influenced by distance to the nearest market center, distance to all weather road and extension service. Based on the findings of this study, some relevant implications can be drawn that can assist to design appropriate intervention mechanisms to improve market outlets choice of haricot bean farmers in the study area.

Global sensitivity analysis of Open Systems Pharmacology Suite physiologically based pharmacokinetic models.

Sensitivity analyses are important components of physiologically based pharmacokinetic (PBPK) model development and are required by regulatory agencies for PBPK submissions. They assess the impact of parametric uncertainty and variability on model estimates, aid model optimization by identifying parameters requiring calibration, and enable the testing of assumptions within PBPK models. One-at-a-time (OAT) sensitivity analyses quantify the impact on a model output in response to changes in a single parameter while holding others fixed. Global sensitivity analysis (GSA) methods provide more comprehensive assessments by accounting for changes in all uncertain or variable parameters, though at a higher computational cost. This tutorial article presents a software package for conducting both OAT and GSA of PBPK models built in the Open Systems Pharmacology (OSP) Suite. The tool is accessible through either an R script or a graphical user interface, and the outputs consist of sensitivity metrics of pharmacokinetic (PK) parameters, such as Cmax and AUC, evaluated with respect to model input parameters. Results are formatted according to regulatory standards. The OAT analysis methods comprise two-way local sensitivity analyses and probabilistic uncertainty analyses, whereas the GSA methods include the Morris, Sobol, and EFAST methods. These analyses can be conducted on single PBPK models or pairs of models for the evaluation of the sensitivity of PK parameter ratiosin drug-drug interaction studies. The practical application of the package is demonstrated through three illustrative case studies.

Physics-guided fault diagnosis method for proton exchange membrane fuel cells based on LSTM neural network

In this paper, we propose a novel hybrid method for fault diagnosis of Proton Exchange Membrane Fuel Cells (PEMFCs) based on the combination of a physics-based model and a long short-term memory (LSTM) neural network. By incorporating the physics-based model in the fault diagnosis algorithm, we can access to several process variables not directly measured through sensors but related to the state of the PEMFC stack. The model estimates are subsequently combined with signals measured on the PEMFC stack and inputted to a LSTM neural network. The performance of the physics-guided LSTM is evaluated on an extensive dataset comprising a thousand hours of operation of a PEMFC stack under dynamic load profiles, proving the enhanced capability of the proposed fault diagnosis method in capturing complex fault patterns. Furthermore, the effectiveness of the proposed method in dealing with PEMFC aging is tested by using data from the initial phase of stack operation for algorithm training and reserving the data from the aged stack operation for the testing phase. The experimental results reveal that the proposed physics-guided LSTM method allows for a significant amelioration over purely data-driven LSTM.

Dynamics trajectory of patient-reported quality of life and its associated risk factors among hepatocellular carcinoma patients receiving immune checkpoint inhibitors: a prospective cohort study

ObjectiveWe aimed to characterize quality of life (QOL) trajectories among patients with intermediate and advanced hepatocellular carcinoma patients treated with immunotherapy.MethodsBarcelona Clinic Liver Cancer (BCLC) stage B-C HCC patients receiving immunotherapy at Guangxi Medical University Cancer Hospital were included. Trajectories of QOL, assessed using the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep) questionnaire, were identified through iterative estimations of group-based trajectory models. Associations with trajectory group membership were analyzed using multivariable multinomial logistic regression.ResultsThree trajectory groups were identified (n=156): excellent (35.3%), poor (43.6%), and deteriorating (21.1%) QOL. The deteriorating trajectory group reported a mean QOL score of 124.79 (95% CI, 116.58–133.00), but then declined significantly at month-2 (estimated QOL score 98.67 [95% CI, 84.33–113.00]), and the lowest mean score is reached at month-6 (estimated QOL score 16.58 [95% CI, 0–46.07]). Factors associated with membership to the deteriorating group included no drinking (odds ratio [OR] vs yes [95% CI], 3.70 [1.28–11.11]), no received radiotherapy (OR vs yes [95% CI], 8.33 [1.41–50.00]), diabetes (OR vs no [95% CI], 6.83 [1.57–29.73]), and extrahepatic metastasis (OR vs no [95% CI], 3.08 [1.07–8.87]). Factors associated with membership to the poor group also included body mass index ≤24.0 kg/m2 (OR vs no [95% CI], 4.49 [1.65–12.22]).ConclusionsThis latent-class analysis identified a high-risk cluster of patients with severe, persistent post-immunotherapy QOL deterioration. Screening relevant patient-level characteristics may inform tailored interventions to mitigate the detrimental impact of immunotherapy and preserve QOL.

Changes in water regime in the high-mountain region of the Terek River (North Caucasus) in connection with climate change and degradation of glaciation

In this study, we adapted the ECOMAG model of the runoff formation for analysis of the Terek River basin using comprehensive hydrometeorological information as well as data on soils, landscape, and glaciation. To take account of regional characteristics of the glaciation, the additional ice module was used with the model. This improvement has resulted in a satisfactory agreement between the modeled runoff hydrographs and the observed ones. In our simulations we used the updated glacier cover predictions from the- global glaciological model GloGEMflowdebris together with regional climate projections from the CORDEX experiment to determine possible future changes in the Terek River flow in the 21st century. The results show that the runoff will change between −2% and +5% according to the RCP2.6 scenario, and from −8% to +14% in the RCP8.5 scenario. The directedness of the runoff changes in particular subbasins of the River will essentially depend on the altitude position of the snow and glacier feeding zones, that is responsible for the intensity of their degradation. Thus, in the RCP8.5 scenario, the flow of the Chegem River will begin to decrease significantly in the second half of the 21st century. In contrast, the predicted increasing of the runoff in Malka and Baksan rivers, which are primarily fed by meltwater from glaciers and snow on Elbrus and other high-mountain zones, is expected to be continued until the end of the century. But this increase may be caused only by a growth of a part of the snowmelt feeding due to greater winter precipitation. The model estimates confirm the present-day observed trends within the intra-annual runoff distribution, demonstrating the earlier start of the spring flood, a decrease in summer runoff volumes and then its increase in the autumn months. The results of the research may be used for more efficient management of water resources in the North Caucasus in the future, including electricity generation and water supply.

Quantifying residents' exposure to agricultural pesticides using new geospatial approaches

A better understanding of environmental exposure to agricultural pesticides is crucial for public health, regulatory and management purposes. Residents in close vicinity to agricultural fields are likely to be more exposed to pesticides. In that context, an innovative geospatial approach for mapping estimates of agricultural pesticide exposure was developed in this study.Data on pesticide application rates, high-resolution annual datasets of the geographic distribution of crops, and the 100x100m grid population dataset were utilized to complete this analysis in Wallonia (Belgium) over the period 2015-2019. Pesticide exposure metrics were estimated using a buffer-based exposure model by conducting neighborhood analysis within a 1000m buffer radius. Subsequently, a population weighted method was used to ‘up-scale’ the exposure data to administrative levels. In a limited validation effort, model estimates were compared with pesticide measurements in air collected at 12 stations during the period 2015-2016.The results present the first modeling map of environmental exposure to agricultural pesticides for the whole of the Walloon region. The northern part of the Sambre-Meuse axis demonstrates more intensive agriculture and the highest pesticide weighted exposure indices. The majority of the population resides near agricultural areas, with only 4% living in regions that lack crops within a 1000m radius, primarily in the central areas of large cities. A positive trend association between pesticide measures in air and the index was observed at the different stations, nevertheless further validation efforts are needed to accurately compare the same active ingredients.This work gives a valuable basis for research and environmental health actions in Belgium. Maps highlight areas where human biomonitoring and epidemiological studies should be implemented to investigate the impact of potential environmental exposure to pesticides. This information helps policy-makers to take measures to control and reduce the load of agricultural pesticides in the environment.

Association of the Life's Essential 8 cardiovascular health score with periodontitis among US adults.

To investigate whether cardiovascular health (CVH) is associated with periodontitis. We used data from the 2009 to 2014 National Health and Nutrition Examination Survey. We quantified CVH using Life's Essential 8, which includes four health behaviours (diet, smoking, physical activity and sleep) and four health factors (body mass index, blood cholesterol, glucose and pressure). We categorized scores as low (0-49), moderate (50-79) and high (80-100). We calculated subscores of health behaviours and factors and categorized them as low, moderate and high. We used logistic regression to assess the association of CVH with periodontitis, adjusting for age, gender, race/ethnicity, education, poverty index, marital status and health insurance. We computed odds ratios (ORs) and 95% confidence intervals (CIs). This study included 9296 adults ≥30 years old. Multivariable-adjusted models showed that subjects with moderate (OR, 0.62; 95% CI: 0.52-0.74) or high (OR, 0.43; 95% CI: 0.33-0.57) CVH had significantly lower odds of periodontitis compared to those with low CVH. These results were consistent in the health behaviours model, but the estimates in the health factors model were not significant. Improving CVH may help prevent periodontitis. Longitudinal studies are needed to confirm our findings.

Toxicity of selected pharmaceuticals and their mixtures to the aquatic indicators Daphnia magna and Aliivibrio fischeri.

Despite the benefits derived from the use of pharmaceuticals, these compounds are currently considered contaminants of emerging concern because of their presence and persistence in the environment. This study aimed to determine the toxicity of 27 pharmaceuticals and the interaction effects of binary mixtures of selected compounds towards two model organisms: the microcrustacean Daphnia magna and the bacterium Aliivibrio fischeri (Microtox test). Six compounds, namely polymyxin B, polymyxin E, fluoxetine, diphenhydramine, clenbuterol and ketoprofen exhibited moderate toxicity towards D. magna. Additionally, three compounds (cefotaxime, polymyxin B, polymyxin E) also showed a moderate toxic effect on A. fischeri. The comparison of such results with model estimations showed inaccuracy in the predicted data, highlighting the relevance of experimental ecotoxicological assays. The assayed mixtures contained four selected drugs of high-hazard according to their reported concentrations in wastewater and surface water (diphenhydramine, trimethoprim, ketoprofen, and fluoxetine); data revealed interactions only in the fluoxetine-containing mixtures for D. magna, while all mixtures showed interactions (mostly synergistic) for Microtox. Chronic effects on the reproduction of D. magna were observed after exposure to fluoxetine and diphenhydramine, although higher sensitivity was determined for the latter, while the mixture of these compounds (which showed acute synergy in both models) also affected the reproduction patterns. Nonetheless, all the effects described at the acute or chronic level (for individual compounds or mixtures) were determined at concentrations higher than commonly reported at environmental levels. This work provides valuable ecotoxicological information for the risk assessment of pharmaceuticals and their mixtures in the environment.

Agricultural innovation for climate change: limited but positive impacts of commercialized drought-tolerant corn

Abstract The extent to which new technologies can countervail the risks posed by climate change is a critical element for designing adaptation strategies. This study uses new experimental data spanning 17 US states from 2008 to 2023 to examine the potential impact of recently commercialized drought tolerant (DT) traits on both yield and yield resilience in US corn production. We find that there is no yield advantage for DT hybrids under average weather conditions, but they improve yield resilience, particularly with respect to precipitation. These effects are spatially heterogeneous, such that DT has a positive yield impact in the droughty, western US, but a small, or even negative, impact in the central and eastern US. In addition, the presence of DT reduces yield variance and kurtosis, and increases skewness, all of which imply a reduction in yield risk. Using the statistical model estimates, we project the impact of DT on corn yields under future climate conditions obtained from 20 general circulation models with two representative concentration pathways. The projected ensemble means of yield gains are 6.34 bu/acre and 5.39 bu/acre under moderate and extreme warming scenarios, respectively, by the mid-twenty-first century. These gains compensate for 23% and 13.5% of total yield loss due to climate change. Our results indicate that current commercial DT hybrids reduce yield risk, improve resilience with respect to precipitation, and have the potential to offer moderate benefits under climate change warming scenarios.

Serological Markers of Clinical Improvement in MuSK Myasthenia Gravis.

In this retrospective longitudinal study, we aimed at exploring the role of (a) MuSK-immunoglobulin G (IgG) levels, (b) predominant MuSK-IgG subclasses, and (c) antibody affinity as candidate biomarkers of severity and outcomes in MuSK-MG, using and comparing different antibody testing techniques. Total MuSK-IgGs were quantified with radioimmunoassay (RIA), ELISA, flow cytometry, and cell-based assay (CBA) serial dilutions using HEK293 cells transfected with MuSK-eGFP. MuSK-IgG subclasses were measured by flow cytometry. SAffCon assay was used for determining MuSK-IgG affinity. Forty-three serum samples were obtained at different time points from 20 patients with MuSK-MG (median age at onset: 48 years, interquartile range = 27.5-72.5; women, 16/20), with 9 of 20 (45%) treated with rituximab. A strong correlation between MuSK-IgG levels measured by flow cytometry and RIA titers was found (rs = 0.74, 95% CI 0.41-0.89, p = 0.0003), as well as a moderate correlation between CBA end-point titers and RIA titers (rs = 0.47, 95% CI 0.01-0.77, p = 0.0414). A significant correlation was found between MuSK-IgG flow cytometry levels and disease severity (rs = 0.39, 95% CI 0.06-0.64, p = 0.0175; mixed-effects model estimate: 2.296e-06, std. error: 1.024e-06, t = 2.243, p = 0.032). In individual patients, clinical improvement was associated with decrease in MuSK-IgG levels, as measured by either flow cytometry or CBA end-point titration. In all samples, MuSK-IgG4 was the most frequent isotype (mean ± SD: 90.95% ± 13.89). A significant reduction of MuSK-IgG4 and, to a lesser extent, of MuSK-IgG2, was seen in patients with favorable clinical outcomes. A similar trend was confirmed in the subgroup of rituximab-treated patients. In a single patient, MuSK-IgG affinity increased during symptom exacerbation (KD values: 62 nM vs 0.6 nM) while total MuSK-IgG and IgG4 levels remained stable, suggesting that affinity maturation may be a driver of clinical worsening. Our data support the quantification of MuSK antibodies by flow cytometry. Through a multimodal investigational approach, we showed that total MuSK-IgG levels, MuSK-IgG4 and MuSK-IgG2 levels, and MuSK-IgG affinity may represent promising biomarkers of disease outcomes in MuSK-MG.

Coefficient Shape Alignment in Multiple Functional Regression

In multivariate functional data analysis, different functional covariates often exhibit homogeneity. The covariates with pronounced homogeneity can be analyzed jointly within the same group, offering a parsimonious approach to modeling multivariate functional data. In this paper, a novel grouped multiple functional regression model with a new regularization approach termed “coefficient shape alignment” is developed to tackle functional covariates homogeneity. The modeling procedure includes two steps: first aggregate covariates into disjoint groups using the new regularization approach; then the grouped multiple functional regression model is established based on the detected grouping structure. In this grouped model, the coefficient functions of covariates in the same group share the same shape, invariant to scaling. The new regularization approach works by penalizing differences in the shape of the coefficients. We establish conditions under which the true grouping structure can be accurately identified and derive the asymptotic properties of the model estimates. Extensive simulation studies are conducted to assess the finite-sample performance of the proposed methods. The practical applicability of the model is demonstrated through real data analysis in the context of sugar quality evaluation. This work offers a novel framework for analyzing the homogeneity of functional covariates and constructing parsimonious models for multivariate functional data.

The influence of business intelligence on the effectiveness of supply chain management in the Nigerian consumer goods sector

Business performance depends on supply chain management (SCM), which can be enhanced through digital transformation to enable quick decisions to increase market share. By enabling informed decision-making, business intelligence (BI) can facilitate this improvement in SCM effectiveness and competitive edge. Real-time production and inventory level monitoring is made possible by BI capabilities, which help organisations adapt to changing market trends and meet customer demands. Particularly, BI technology can increase the SCM of enterprises in the consumer products sector by providing value-added and financial gains, enabling speedy industry adoption and rapid industry growth. The study employed a quantitative methodology for gathering data, and a simple random sampling method was employed to prevent sample bias. The study considered linear curve estimation (LCE) as a method of data analysis. The results of the model estimations showed that BI recorded positive and significant influence on SC visibility, flexibility, and resilience in the Nigerian consumer goods sector. The study concludes that BI increased the effectiveness of supply chain management (SCM) by improving visibility, anticipating demand, increasing resilience, optimising processes, and boosting productivity. SCM effectiveness depends on the efficient handling of disruptions and uncertainty. The study highlights the practical policy implications following the findings of the study.

Excess mortality associated with chikungunya epidemic in Southeast Brazil, 2023

The chikungunya virus (CHIKV) was first detected in Brazil in 2014 and has since caused major epidemics. In 2023, a major chikungunya epidemic occurred in Minas Gerais, one of the most populous states in the country. We analyzed the North and Northeast Health Macroregions of Minas Gerais, with 2.5 million inhabitants. The study used epidemiological data from the Ministry of Health’s laboratory surveillance, disease notification and mortality systems. A robust Poisson regression model was constructed to estimate expected monthly mortality. Excess deaths were calculated by comparing observed deaths with model estimates during the epidemic period. During the epidemic, there were 890 excess deaths attributed to chikungunya, translating into a mortality rate of 35.1/100,000 inhabitants. The excess mortality rate was significantly 60 times higher than the deaths reported by surveillance, with only 15 confirmed deaths. The correlation between excess deaths and laboratory-confirmed chikungunya cases was strong, while the correlation with dengue and COVID-19 was not statistically significant. The results highlighted the serious underestimation of chikungunya mortality by epidemiological surveillance. During the same year, only 420 chikungunya deaths were reported by all PAHO member countries. Routine epidemiological surveillance systems cannot capture the full impact of this disease. Excess mortality is a key measure for understanding the impact of epidemics, as demonstrated by COVID-19 and influenza pandemics. The study highlights the need for complementary tools to traditional surveillance to better assess impacts on morbidity and mortality and support priority setting in public health interventions.

Simultaneous Coefficient Clustering and Sparsity for Multivariate Mixed Models

In many applications of multivariate longitudinal mixed models, it is reasonable to assume that each response is informed by only a subset of covariates. Moreover, one or more responses may exhibit the same relationship to a particular covariate for example, if they are capturing the same underlying aspect of an individual physical, mental, and emotional health. To address the above challenges, we propose a method for simultaneous clustering and variable selection of fixed effect coefficients in multivariate mixed models. We achieve this in a computationally scalable manner via a composite likelihood approach: separate mixed models are first fitted to each response, after which the model estimates are combined into a single quadratic form resembling a multivariate Wald statistic. We then augment this with fusion- and sparsity-inducing penalties based on broken adaptive ridge regression. Simulation studies demonstrate that the proposed composite quadratic estimator is similar to or better than several existing techniques for fixed effects selection in (univariate) mixed models while being computationally much more efficient. We apply the proposed method to longitudinal panel data from Australia to quantify how an individual’s overall health, assessed via a set of eight composite scores, evolves as a function of various demographic and lifestyle variables.

Parameter estimation in vapor-liquid equilibrium modeling of compounds related to biodiesel production using opposite point-based differential evolution algorithm.

Biodiesel stands out as the most favorable alternatives to fossil-derived diesel, offering a multitude of environmental benefits. At various stages of biodiesel production, the separation processes necessitate thermodynamic models with the capability to correlate and predict phase equilibria of mixtures. In this study, application of the classical differential evolution (DE) and its new enhanced version, the OPDE algorithm, for modeling vapor-liquid equilibrium (VLE) associated with components related to biodiesel production is presented. The algorithms were analysed and contrasted in terms of their performance, in estimating parameters for Wilson, NRTL and UNIFAC models. Additionally, classical least-squares (LS) and error-in-variable (EIV) approaches were examined and compared. Also, VLE datasets for a specific system have been merged, and parameters have been estimated. The findings suggest that parameters obtained through LS approach align with those reported in literature, indicating faster convergence in all problems. In contrast, the EIV approach achieved a higher objective function value compared to the LS approach, exhibiting low deviation. OPDE outperformed DE in terms of performance. The enhancement in RMSTD value has been found within range 91%-99% for EIV approach. Further, novel findings derived from some of the studied VLE datasets are presented.

Cost-effectiveness of respiratory syncytial virus adjuvanted vaccine in Portuguese aged ≥ 60 years

Abstract Background Respiratory syncytial virus (RSV) infections are one of the leading causes of lower respiratory tract disease among adults aged ≥60 years in Europe. This study aims to assess the potential public health impact of the RSVPreF3 OA adjuvanted vaccine, the first approved preventive intervention for the older adult population in Portugal. Methods A monthly-cycle static Markov model was developed to assess RSVPreF3 OA adjuvanted vaccine impact on adults aged 60 years and older in Portugal. A single dose of RSVPreF3 OA adjuvanted vaccine was compared to no-vaccination over a three-year time-horizon. The model captures lower respiratory tract disease (LRTD) RSV cases, hospitalizations, deaths, and healthcare resource utilization. Data inputs were based on local published literature, supplemented by the best available data when local information was lacking. Results Based on preliminary analysis considering 75% vaccine coverage, model outputs estimate that RSVPreF3 OA adjuvanted vaccine can prevent 71,433 LRTD cases requiring medical attendance, 11,067 hospitalizations, 1,793 intensive care unit admissions and 1,003 deaths. The model estimates that 32 adults need to be vaccinated to avoid one RSV LRTD case, 207 adults need to be vaccinated to avoid one hospitalization and 2,283 adults need to be vaccinated to avoid one death. The incremental cost-effectiveness ratio was estimated at 9,541€, hence the RSVPreF3 OA adjuvanted vaccine is cost-effective compared to no vaccination. Conclusions The results demonstrate a strong preventive impact of the RSVPreF3 OA adjuvanted vaccine on health outcomes and healthcare resource utilization in adults ≥60 years, leading to a significant public health benefit. This result emphasizes the importance of preventing RSV to preserve the health and well-being of Portugal’s older adults. In addition, data from ongoing studies may enhance precision and extensive sensitivity analyses validate the robustness of the model’s input assumptions. Key messages • This study shows the robust evidence of RSVPreF3 OA adjuvanted vaccine to improve public health by preventing thousands of hospitalizations and deaths among ≥60 years, in Portugal. • Vaccinating adults aged ≥ 60 years with RSVPreF3 OA adjuvanted vaccine in Portugal is a cost-effective strategy.

Determinants of health among Ukrainian refugees in Germany: comparison with other refugee groups

Abstract Background 6.5 million Ukrainian refugees have been displaced globally since 2022, with 1 million seeking asylum in Germany. Little is known about the health determinants for this group, limiting the evidence base for policy intervention. Methods We use a nationally representative survey of adult Ukrainian refugees in Germany (IAB-BiB/FReDA-BAMF-SOEP study; n = 5943), conducted in 2023, to analyse the effect of sociodemographic, pre-migration (experienced war intensity, traumatic experiences) and post-migration factors (time since arrival, accommodation, social networks) on self-rated health using multiple logistic regression. We contrast these findings with estimates of identical models obtained from a subset of the IAB-BAMF-SOEP-sample of non-Ukrainian refugees within one year of arrival (n = 1195). Results In the Ukrainian sample, post-migration factors are particularly critical for health, with those experiencing discrimination (OR 1.83; 95%CI: 1.51,2.21) and social isolation (OR: 2.70; 95%CI: 2.23, 3.28) affected by ill health, while those with high German proficiency (OR 0.34; 95%CI: 0.18, 0.64) and contact with Germans (OR: 0.66; 95%CI: 0.53,0.83) are protected. Pre-migration factors do not affect self-rated health. Among refugees from other countries of origin, post-migration factors are not associated with health within the first year of arrival, with exception of social isolation (OR: 2.03; 95%CI: 1.32,3.13). Discussion Despite the EU Temporary Protection Directive, the health of Ukrainian refugees in Germany is shaped by adverse post-migration circumstances. While other refugee groups may have had riskier escape journeys and worse arrival conditions, this is unrelated to self-rated health in the first year. This underscores the importance of considering pre-migration contexts when evaluating subjective health. Outreach services, communication campaigns and the expansion of medical interpreting services are needed to ensure access to the health system. Key messages • Despite the EU Temporary Protection Directive, the health of Ukrainian refugees in Germany is shaped by adverse post-migration circumstances. • Outreach services, communication campaigns and the expansion of medical interpreting services are needed to ensure access to the health system.

Urban Exposome and Health

Abstract Background The environment in which we live is thought to explain 70% of the non-communicable disease burden. The aim of EXPANSE project (funded from the European Union Horizon 2020 research and innovation programme under grant agreement No 874627) is to evaluate the optimal strategies for maintaining health in the context of contemporary urban environments. As a component of the project, Urban Labs allow deep phenotyping of the urban exposome and assessing its use and perception. The Urban Labs aim to improve exposure assessment, identify mediating and modifying lifestyle factors, and link the urban exposome to intermediate endpoints relevant for cardiometabolic and pulmonary health. Methods By monitoring the activities of 4,000 individuals in five distinct urban areas, Urban Labs provide a comprehensive understanding of the urban exposome, which can be compared with residential address-based model estimates. The participants complete a baseline questionnaire, repeated bi-monthly questionnaires and exposure-specific questionnaires over a period of 2 years using mobile phone application (ExpanSeeker). Two-week personalized measurements including spatial-temporal activity patterns, sensor-based environmental and behavioral measurements, and minimally invasive biological sample collection are performed covering different seasons. Results The Baseline Questionnaire (BQ) and two-weeks measurement campaigns (MC) were completed in European cities as follows: Athens BQ = 379, MC = 232; Barcelona BQ = 1040, MC = 303; Basel BQ = 731, MC = 655; Lodz BQ = 634, MC = 100; The Netherlands (multicity) BQ = 1033, MC = 326. Data collection process is ongoing. Conclusions The collection of novel data on individuals activities and perceptions within temporal and geographical contexts in Urban Labs will contribute to a more comprehensive understanding of how the urban dwellers are exposed to and interact with their environment, thereby supporting healthier urban engineering and policy making. Key messages • Urban Labs will provide a more comprehensive understanding of the impact of the urban environment on individual’s behavior, their exposome, enabling more accurate prediction of non-communicable risk. • Urban Labs will provide a base to support healthier urban engineering and policy making.

Africa Health Research Institute (AHRI) Household Contact study: a study protocol, statistical analysis and modelling plan

Household case-contact studies are an established method to estimate the relative infectiousness of people with pulmonary tuberculosis. Testing of child household contacts with a QuantiFERON TB Gold plus (QFT) assay provides a proxy measure of M. tuberculosis infection in exposed household members. This study aims to generate data about the effect of antiretroviral treatment (ART) and of tuberculosis symptoms on risk of intra-household M. tuberculosis transmission to children and investigate the conditions under which symptom-agnostic screening should be considered, which could provide evidence to support a paradigm shift in tuberculosis screening strategy. Index people diagnosed with bacteriologically-confirmed pulmonary tuberculosis who have been identified by routine health services and starting tuberculosis treatment at one of the 12 health facilities serving the population of the Africa Health Research Institute health and demographic surveillance area will be eligible for recruitment. For each consenting index participant, we will enroll all household members aged 2-14 years who will undergo a symptom screen and provide a blood sample for a QFT and HIV test. We will compare the proportion of child contacts who are QFT-positive in households of (i) index adults who are living with HIV and taking ART vs not taking ART and (ii) index adults engaged in ART care who report no symptoms vs those who report symptoms. We will use a novel mathematical model, incorporating new insights into TB natural history and transmission, to determine the circumstances under which screening for TB, regardless of reported symptoms, would have most impact on tuberculosis incidence and mortality, and explore comparative cost-effectiveness of interventions along the diagnostic pathway at different levels of the health system. Estimates of relative risk of intra-household M. tuberculosis transmission, combined with healthcare utilisation and cost data, will allow us to generate mathematical model estimates of the potential epidemiological impact and relative cost-effectiveness of different approaches to tuberculosis case-finding including symptom-agnostic tuberculosis screening at different levels of the health system.