Health Monitoring Program Research Articles

MEASUREMENT OF PERSISTENT ORGANIC POLLUTANTS, PERFLUORINATED COMPOUNDS, AND TOXIC METALS IN THE BLOOD OF HUMBOLDT PENGUINS (SPHENISCUS HUMBOLDTI) AT PUNTA SAN JUAN, PERU USING DRIED BLOOD SPOTS.

The Humboldt penguin (Spheniscus humboldti) population at the Punta San Juan Marine Protected Area in Peru is considered critical to the long-term sustainability of this endangered species in Peru. Exposure of the rookery to environmental toxicants is a mounting concern because of regional growth of industries and human populations. Whole blood samples were collected from 30 free-ranging penguins in 2011 as part of a broader population health monitoring program. Dried blood spots (DBS) containing 50 µl of blood were prepared and analyzed to assess exposure to five groups of environmental contaminants. Concentrations of elements arsenic, cadmium, iron, lead, mercury, selenium, and thallium were analyzed using inductively coupled plasma mass spectrometry. Persistent organic pollutant concentrations were measured using gas chromatography-tandem mass spectrometry to analyze organochlorine pesticides (OCP; p,p'-DDT, p,p'-DDE, β-hexachlorocyclohexane, t-nonachlor, and oxychlordane), polychlorinated biphenyls (congeners 138 and 153), and polybrominated flame retardants (polybrominated biphenyl-153 and polybrominated diphenyl ether congeners 47 and 99). Per- and polyfluoroalkyl substances, including perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid were measured using liquid chromatography-tandem mass spectrometry. Results revealed low levels of exposure to these selected contaminants, at levels not considered to be of concern for wildlife health. DBS methodology was considered effective in a field-based setting for quantification of whole blood concentrations of environmental contaminants in penguins.

Comprehensive Colony Health Management and Emerging Pathogens of the Annual Killifish Species Nothobranchius furzeri.

The Leibniz Institute on Aging has maintained killifish colonies for over 15 y. Our veterinarians, scientists, and animal technicians developed a fish health scoring system and routine colony health surveillance program for our colonies. Over a 4-y period, health data from the African turquoise killifish Nothobranchius furzeri colony were systematically collected and analyzed. The fish health assessment system facilitated categorization of clinical signs and differentiation of fish with mild clinical signs from fish that required euthanasia. This report provides new information on clinical signs and conditions that may occur in young and aged N. furzeri. To be comprehensive, a colony health surveillance program incorporates animal health at both the individual and the population levels. The quarterly routine health monitoring program identified Mycobacterium spp. as the most common agent in our facility and identified the killifish pathogen (Loma acerinae) for the first time. Taken together, these findings demonstrate the importance of a comprehensive colony health management system in a fish research facility. By improving the health and welfare of fish used for research, the scientific community will benefit from less variable and more reliably reproducible research results.

Seasonal Variation in Fungi in Beach Sand in Summertime: Stintino (Italy)

Background: The goal of this study was to monitor the microbial biodiversity in beach sand that is heavily visited by tourists during the summer, and to determinate whether the high presence of bathers (around 5000 per day) can modify sand microbial composition. Methods: Between 2016 and 2020, 150 sand samples were collected from nine different points at La Pelosa beach in Sardinia, Italy. Non-culturing methods were used; DNA extraction and meta-barcode sequencing were performed. All samples were analyzed with sequencing methods for 16S and ITS sequences. Results: Fungal genera differ on the three beaches and in the winter/summer zones. The ITS sequence showed the most common presence of Candida during summer and Paradendryphiella in the winter. The greatest diversity was found in the dune during winter, while in other parts of the beach, there are differences between bacteria and fungi, particularly in the wash zone during the winter, with high diversity for 16S sequences but low diversity for ITS sequences. Conclusions: It appears reasonable that the sands, even on non-urban beaches, should be included in health monitoring programs in addition to the waters, and that access to them should be regulated by limiting the number of bathers with the aim of reducing the presence of pathogenic fungal species.

Blood lead concentrations in exposed forecourt attendants and taxi drivers in parts of South Africa

BackgroundLeaded fuel was banned in South Africa in 2006, in order to improve human health and reduce environmental pollution. Lead (Pb) has been suggested to contribute to the development of neurodegenerative disorders, and the role of respiratory exposure to Pb from petrol fumes should not be neglected in this context. In addition to Pb, petrol contains various harmful chemicals including other neurotoxic metals and hydrocarbons. Objectives and MethodsHere, we investigated concentrations of Pb and other metals in blood from petrol station forecourt attendants (n = 38), taxi drivers (n = 21), and unexposed controls (n = 36). Taxi drivers and forecourt attendants were divided into three groups each, based on number of years worked. A questionnaire was designed to investigate the health status of the participants. Blood samples were collected by medical professionals and analyzed for metal concentrations by ICP-MS. ResultsA positive correlation between number of years worked and Pb blood concentrations was found. The highest Pb concentration (60.2 µg/L) was observed in a forecourt attendant who had worked 11–20 years, and the average Pb concentration in this group (24.5 µg/L) was significantly (p < 0.05) higher than in forecourt attendants who had worked 2–5 years (10.4 µg/L). Some individuals had elevated concentrations of manganese, arsenic, cadmium, chromium and cobalt, yet not significantly elevated at the group level. The blood levels of arsenic appeared to be related to smoking. Mood swings, dizziness, headaches and tiredness were reported by the workers. ConclusionBlood Pb concentrations in petrol station forecourt attendants and taxi drivers exposed to leaded petrol are elevated and correlate to exposure time. A health monitoring program should be erected for all individuals working in these industries, and preventive measures should be implemented to eliminate metal exposure from petrol.

Effectiveness of a home health monitoring and education program for complex chronic patients, led by primary care nurses.

The challenge of chronicity has led developed countries to design strategies to respond to the new needs of complex chronic patients (CCP). There is evidence supporting better beneficial effects and more efficient care for CCP when home-base care programs are provided by Primary Health Care professionals. The main objective of the present study was to assess the effectiveness of a nursing intervention program of home visits for CCP analyzing the use of health services in terms of hospital admissions, emergency care unit visits, and mortality rate. A quasi-experimental study was designed to retrospectively evaluate the effectiveness of a 3-year proactive, individualized nursing intervention in improving health outcomes measured by health service utilization (hospitalization, emergency care, and nursing home visits) in these patients. Of the 344 complex chronic patients participating in the study, 93 were assigned to the intervention group (IG) and 251 to the control group (CG). Along the period of study, the number of home visits in the IG almost tripled in relation to the CG (14.29 ± 4.49 vs. 4.17 ± 2.68, p < 0.001). Admissions in the first and second year of the study period were lower in the intervention group p = 0.002 and p < 0.001 respectively. All the participants in the control group were admitted at least once during the study period. In contrast, 29.0% of the participants in the intervention group never had a hospital admission during the 3-years study period. The number of ED visits to the emergency department was significantly lower in the IG during the 3 years of the study periods. The cumulative number of emergency visits in the IG was half that in the CG (5.66 ± 4. vs. 11.11 ± 4.45, p < 0.001, Cohen'd,1.53). A total of 35.5% of the participants in the intervention group visited the emergency department on three or fewer occasions compared to 98% of the subjects in the control group who visited the emergency department on more than six occasions (p < 0.001). The 3-year overall mortality rate was 23.5% in the control group and 21.6% in the nursing home visit program. These differences were not statistically significant. The program demonstrated its effectiveness in reduction of hospital admissions and visits to the emergency department. The program had no impact on mortality rate. This program of home visits reinforces the role of primary care nurses in advanced competencies in chronicity.

Comparative study of a newly proposed machine learning classification to detect damage occurrence in structures

Over the past two decades, an increasing number of large-scale structures have been built around the world. Constructing these structures has been a time consuming and highly expensive process. Thus, providing a structural health monitoring system to guarantee their proper functionality is important. In recent years, the advancement of technology and artificial intelligence methods based on signal processing and machine learning has attracted the attention of researchers. The challenges currently exist in the field of structural health monitoring to identify and classify damages to achieve high accuracy in a health-monitoring program. The presence of noise in measurement, various exciting load types, and varying environmental conditions cause difficulty in the practical identification and classification of damage in structures. Recent studies have employed finite element modeling to test the effectiveness of proposed methods for identifying damages in structures. However, detecting damage in real-world structures as mentioned above, presents unique difficulties, and the effectiveness of the proposed methods for damage detection in real-world structures remains uncertain. In order to improve the performance of damage detection methods and increase the accuracy of these methods as much as possible, the most important action is to identify damage sensitive data in the structure. The next challenge is to choose a high performance algorithm for damage identification and classification. One of the advanced algorithms, which has a very high ability to extract the desired features from the measured data, is the XGBoost algorithm. This algorithm has recently attracted the attention of researchers and has been used in different fields. So far, the ability of this algorithm has not been examined in the field of damage detection in order to extract desirable features. This article deals with the identification, classification, and severity of damages in the SMC benchmark bridge, which is an existing megastructure in the real world, as well as the IASC-ASCE benchmark structure, whose responses were taken under applied loads in the laboratory environment. First, using the XGBoost algorithm, the importance of the features extracted from the sensors' data is evaluated, and then the features, which are effective in the damage detection process, are selected. The results of this algorithm indicate that only by selecting 6 features from a large volume of data, the best performance can be achieved and selecting more does not help increase efficiency. In the next step, the Stacking method, which is a hybrid machine learning algorithm for damage classification, is evaluated and compared with some conventional machine learning algorithms that have been used in previous studies. The Stacking method stands out as the top performer with an average accuracy rate of 93.1%, leading to the conclusion that it is the most effective approach. Finally, by applying the presented algorithm to the two mentioned structures, its validation is appraised.

Рискориентированный подход к мониторингу здоровья лабораторных кроликов

Rabbits are recognized as classic laboratory animals and are widely used in many different researches. Laboratory rabbits are used both as independent test systems and as additional (duplicate) test systems with other small rodents. For the past few years, laboratory rabbits have been ranked 3rd among mammalian laboratory animals in terms of the number of uses for research and testing, for scientific and educational purposes after rats and mice. In order for the test system to be appropriate, it is necessary to create condi-tions for keeping, feeding and drinking for animals that meet their needs. It is also necessary to control their microbiological status and to do good veterinary care. The first question that arises with planning the health monitoring program of laboratory rabbits, is the question of the list of pathogens for these animals. Because of Russia does not have a recommended list of pathogens for laboratory animals, local preclinical centers are forced to use international recommendations (in particular, FELASA), which consist of data from different regions of Europe and do not include data of infectious diseases of the Russian Federation. At the same time, the authors of FELASA note the need to take into account the epidemiological and epizootological characteristics of each of the regions, depending on the geographical location. To substantiate the list of controlled pathogens of laboratory rabbits used in Russia, we applied a risk-based approach. For analysis, we used the FMEA/FMECA method (Failure Mode Effect Analysis/Failure Mode Effect Criticality Analysis) — Analysis of the types and consequences of potential inconsistencies. This article considers a list of pathogenic microorganisms for control in laboratory rabbits used in Russia. The results can be used for the health monitoring of laboratory rabbits in local research centers.

An In-House ELISA for Treponema Antibodies in Bulk Milk as Part of a Monitoring Tool for Claw Health in Dairy Herds.

Digital dermatitis (DD) is a painful inflammation at the coronary band of the claws, a major cause of lameness in cattle and associated with infections with several Treponema spp. Clinical inspection of the feet is the best way to diagnose DD, but this is laborious and stressful for cattle. A simple diagnostic tool was developed to monitor DD prevalence at the herd level. An antibody ELISA based on antigens from four different Treponema spp. has been developed and validated in two field studies. In one study, bulk milk and individual milk samples of seven dairy herds, of which clinical claw scores were obtained, were tested. In the second study, bulk milk was tested from 110 herds of which clinical scores were obtained. A weak correlation between clinical scores of cows and the ELISA results in individual milk samples was observed. The ELISA response in bulk milk was higher in herds with higher mean clinical scores. Using the ELISA results in bulk milk, herds with a low or high proportion of cattle with DD lesions could be distinguished. This ELISA is useful to obtain insight into the DD status at the herd level, and is nowadays being used in a claw health monitoring program for dairy cattle in the Netherlands.

Effects of targeted clinical examination based on alerts from automated health monitoring systems on herd health and performance of lactating dairy cows

Our objectives were to compare the proportion of lactating dairy cows diagnosed with health disorders (HD) and herd performance when using a health monitoring program designed to rely primarily but not exclusively on alerts from automated health monitoring (AHM) systems or a health monitoring program based primarily on systematic clinical examinations, milk yield monitoring, and visual observation of cows. In a clinical trial, at ∼30 d before expected parturition, nulliparous and parous Holstein cows, stratified by parity and days in gestation, were randomly assigned to the high-intensity clinical monitoring (HIC-M; n = 625) or automated monitoring (AUT-M; n = 624) treatment. Cows were fitted with a neck-attached rumination and physical activity monitoring tag, and individual daily milk yield data were collected from parlor milk meters. For cows in HIC-M, clinical examination was conducted daily until 10 d in milk (DIM) and then in response to milk yield reduction alerts or visual observation of clinical signs of HD over the course of 21 DIM. For cows in AUT-M, clinical examination until 21 DIM was because of health index (HI) score alerts and reduced milk yield alerts. The HI score alerts used were generated based on the manufacturer's settings for the system for the last 2-h period before cows were selected for examination. Visual observation of clinical signs of HD was used for identifying cows potentially missed by automated alerts. Binomial and quantitative data were analyzed by logistic regression and ANOVA with repeated measures, respectively. The percentage of cows diagnosed with at least 1 HD during the experimental treatments risk period tended to be greater and the incidence rate ratio of HD diagnosed was greater in the HIC-M than in the AUT-M treatment. We found no difference between treatments for cows that exited the herd up to 60 or 150 DIM, but more cows tended to exit the herd from 61 to 150 DIM in the HIC-M than in the AUT-M treatment. No differences were detectable between treatments in daily or total milk yield to 21 DIM or in weekly mean milk yield and total milk yield to 150 DIM. More cows were inseminated in estrus for first service if in the HIC-M treatment and had no HD diagnosed than if in the HIC-M treatment but with HD diagnosed, or in the AUT-M treatment and had no HD diagnosed. Cows in the AUT-M treatment with HD diagnosed did not differ from other groups. No differences between treatments were observed in pregnancies per artificial insemination or pregnancy loss for first service. Despite a reduction in the risk of diagnosis of HD, no evidence indicated that a health monitoring program that relied on AHM system alerts to select cows for clinical examination reduced herd performance compared with a more intensive program that included systematic clinical examinations of all cows for the first 10 DIM, reduced milk yield alerts, and visual observation. However, to obtain the same herd performance as with the HIC-M treatment, the AUT-M treatment required use of visual observation. In conclusion, a health monitoring program designed to rely primarily on targeted clinical examination based on alerts from automated health monitoring systems might be a feasible alternative to programs that rely more on clinical examination, provided that visual observation is used to identify cows not detected by automated alerts.

Hubungan riwayat sarapan pagi, tingkat kecukupan gizi makro dan status gizi dengan prestasi belajar anak di SD Inpres Lili Kabupaten Kupang

Breakfast is a very important mealtime, representing 15-25% of the total daily energy intake or 15-30% of the total daily nutrient intake to achieve a healthy, active, and intelligent life. Nutritional status has a significant influence on the level of intelligence and understanding of lessons in the classroom; if a child's nutritional status is insufficient or even poor, it affects the incompleteness of subject understanding and weakens learning outcomes. The purpose of this research is to analyze the history of breakfast, adequacy of macronutrients, and nutritional status with the academic achievement of children at Inpres Lili Elementary School, Kupang Regency. This type of research is an analytical observational study using a cross-sectional approach. The research sample was determined using purposive sampling method, which consisted of 137 students from Inpres Lili Elementary School, Kupang Regency. Statistical tests used were Chi-Square test and multiple logistic regression. Bivariate analysis results show a significant relationship between breakfast (p=0.000), level of macronutrient adequacy (p=0.000), and nutritional status (p=0.000) with academic achievement. Multivariate analysis results show that the history of breakfast, adequacy of macronutrients, and nutritional status are significantly related to academic achievement. Therefore, schools should implement routine nutritional and health monitoring programs and provide counseling or training on balanced nutritious food consumption.

The Silent Victims of the Pandemic: Children During the Coronavirus Disease 2019 Crisis.

The coronavirus disease-19 pandemic has adversely affected children's health since its inception. Apart from the mortality and morbidity due to infection, child health monitoring, vaccination, and nutrition programs, especially in newborns and young children, have been disrupted. Measures such as school closures and curfews to prevent the spread of infection brought along many physical and mental health problems by causing disruption of education, social isolation, and closure of children at home. The possible long-term effects of the delayed implementation of the Sustainable Development Goals in Health have also deeply affected children, who are the most vulnerable victims of the coronavirus disease-19 pandemic.

Sensing Mechanism and Real-Time Bridge Displacement Monitoring for a Laboratory Truss Bridge Using Hybrid Data Fusion

Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements.

First Report of &lt;i&gt;Achaea Serva&lt;/i&gt;(F .)Defoliating &lt;i&gt;Pouteria Obovatain&lt;/i&gt; Vietnam

In 2021, widespread defoliation of Pouteria obovata occurred in the Co To Island, Vietnam. The damage incidence from defoliation ranged from 23 to 82%, and the average damage index was from 0.4 to 3.1. Based on morphological and molecular data, this paper establishes Achaea serva(F.)as a serious new pest of P. obovata.This pest should be included in future forest health monitoring programs in coastal Vietnam to determine its spread, host range and pest status.

Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques

Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a good solution to this challenge. Despite proper studies on data normalization using structural responses/features acquired from contact-based sensors, this issue has not been explored properly via new features, such as displacement responses from remote sensing products, including synthetic aperture radar (SAR) images. Hence, the main aim of this work was to eliminate environmental variability, particularly thermal effects, from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges. For this purpose, we conducted a comprehensive comparative study to investigate two supervised and two unsupervised data normalization algorithms. The supervised algorithms were based on Gaussian process regression (GPR) and support vector regression (SVR), for which temperature records acquired from contact temperature sensors and structural displacements retrieved from spaceborne remote sensors produce univariate predictor (input) and response (output) data for the regression problem. For the unsupervised algorithms, this paper employed principal component analysis (PCA) and proposed a deep autoencoder (DAE), both of which conform with unsupervised reconstruction-based data normalization. In contrast to the GPR- and SVR-based data normalization algorithms, both the PCA and DAE methods only consider the SAR-based displacement (output) data without any requirement of the environmental and/or operational (input) data. Limited displacement sets of long-span bridges from a few SAR images of Sentinel-1A, related to long-term SHM programs, were considered to assess the aforementioned techniques. Results demonstrate that the proposed DAE-aided data normalization is the best approach to remove thermal effects and other unmeasured environmental and/or operational variability.

Agreement Study between Total Leukocyte Count Methodologies in South American Sea Lions (Otaria byronia) and Peruvian Fur Seals (Arctocephalus australis).

South American sea lions (Otaria byronia) and Peruvian fur seals (Arctocephalus australis) are sympatric species inhabiting the coastal Peruvian marine ecosystem. Declining abundance has prompted population health monitoring programs, including temporal monitoring of blood parameters. Several methods exist to determine total leukocyte count, yet no studies have evaluated agreement between methods in pinnipeds. We assessed agreement between total leukocyte counts determined by blood film estimate, Leuko-TIC, HemoCue, and UNOPETTE methods by using archival results from pinnipeds at Punta San Juan, Peru. Blood film estimates were prospectively performed, and resulting data were compared with retrospective leukocyte counts obtained from both species between 2009 and 2019 by using the other methodologies. Agreement in hematologic counts between methods was evaluated using Passing-Bablok regression and Bland-Altman plots (α=0.05). In total, 295 individuals (201 A. australis and 94 O. byronia) were included in the analysis. The blood film estimate method resulted in the highest leukocyte values (P<0.0001). Leuko-TIC counts were significantly higher than HemoCue counts (P<0.0001). Constant and proportional error was present in the agreement between the blood film estimate method and the other methods. Given the variation demonstrated between the different methodologies, additional research is needed to further evaluate agreement between these methodologies. The results underscore the importance of maintaining consistency in leukocyte count methodology for monitoring trends in population health over time. Method consistency may be the more important clinical consideration for assessing changes in leucocyte count over time and avoiding apparent changes depending on the methodology used.

Harmonisation of welfare indicators for macaques and marmosets used or bred for research.

Background: Accurate assessment of the welfare of non-human primates (NHPs) used and bred for scientific purposes is essential for effective implementation of obligations to optimise their well-being, for validation of refinement techniques and novel welfare indicators, and for ensuring the highest quality data is obtained from these animals. Despite the importance of welfare assessment in NHP research, there is little consensus on what should be measured. Greater harmonisation of welfare indicators between facilities would enable greater collaboration and data sharing to address welfare-related questions in the management and use of NHPs. Methods: A Delphi consultation was used to survey attendees of the 2019 NC3Rs Primate Welfare Meeting (73 respondents) to build consensus on which welfare indicators for macaques and marmosets are reliable, valid, and practicable, and how these can be measured. Results: Self-harm behaviour, social enrichment, cage dimensions, body weight, a health monitoring programme, appetite, staff training, and positive reinforcement training were considered valid, reliable, and practicable indicators for macaques (≥70% consensus) within a hypothetical scenario context involving 500 animals. Indicators ranked important for assessing marmoset welfare were body weight, NHP induced and environmentally induced injuries, cage furniture, huddled posture, mortality, blood in excreta, and physical enrichment. Participants working with macaques in infectious disease and breeding identified a greater range of indicators as valid and reliable than did those working in neuroscience and toxicology, where animal-based indicators were considered the most important. The findings for macaques were compared with a previous Delphi consultation, and the expert-defined consensus from the two surveys used to develop a prototype protocol for assessing macaque welfare in research settings. Conclusions: Together the Delphi results and proto-protocol enable those working with research NHPs to more effectively assess the welfare of the animals in their care and to collaborate to advance refinement of NHP management and use.

Prediction of Chronic Stress and Protective Factors in Adults: Development of an Interpretable Prediction Model Based on XGBoost and SHAP Using National Cross-sectional DEGS1 Data.

Chronic stress is highly prevalent in the German population. It has known adverse effects on mental health, such as burnout and depression. Known long-term effects of chronic stress are cardiovascular disease, diabetes, and cancer. This study aims to derive an interpretable multiclass machine learning model for predicting chronic stress levels and factors protecting against chronic stress based on representative nationwide data from the German Health Interview and Examination Survey for Adults, which is part of the national health monitoring program. A data set from the German Health Interview and Examination Survey for Adults study including demographic, clinical, and laboratory data from 5801 participants was analyzed. A multiclass eXtreme Gradient Boosting (XGBoost) model was constructed to classify participants into 3 categories including low, middle, and high chronic stress levels. The model's performance was evaluated using the area under the receiver operating characteristic curve, precision, recall, specificity, and the F1-score. Additionally, SHapley Additive exPlanations was used to interpret the prediction XGBoost model and to identify factors protecting against chronic stress. The multiclass XGBoost model exhibited the macroaverage scores, with an area under the receiver operating characteristic curve of 81%, precision of 63%, recall of 52%, specificity of 78%, and F1-score of 54%. The most important features for low-level chronic stress were male gender, very good general health, high satisfaction with living space, and strong social support. This study presents a multiclass interpretable prediction model for chronic stress in adults in Germany. The explainable artificial intelligence technique SHapley Additive exPlanations identified relevant protective factors for chronic stress, which need to be considered when developing interventions to reduce chronic stress.

Prevalence and trends of anemia among pregnant women in eight provinces of China from 2016 to 2020

This study analyzed the anemia status and change trend of 219 835 pregnant women in eight provinces from 2016 to 2020 in the Maternal and Newborn Health Monitoring Program(MNHMP). The results showed that from 2016 to 2020, the anemia rate of pregnant women in eight provinces was 41.27%, and the rates of mild, moderate and severe anemia were 28.56%, 12.59% and 0.12% respectively; the anemia rates in eastern, central and western regions were 41.87%, 36.09% and 44.63% respectively, and the anemia rates in urban and rural areas were 39.87% and 42.23%. From 2016 to 2020, the anemia rate of pregnant women decreased from 44.93% to 38.22%, with an average annual decline of 3.86% (95%CI:-5.84%, -1.85%). The anemia rate among pregnant women of the eastern region (AAPC=-6.16%, 95%CI:-9.79%, -2.38%) fell faster than that among pregnant women of the central region (AAPC=0.71%, 95%CI:-6.59%, 8.57%) and western region (AAPC=-1.53%, 95%CI:-5.19%, 2.28%). From 2016 to 2020, the moderate anemia rate in pregnant women decreased from 14.98% to 10.74%, with an average annual decline of 8.72% (95%CI:-12.90%, -4.34%), with a statistically significant difference (P<0.05); AAPC for mild and severe anemia in pregnant women was 1.56% (95%CI: 3.44%, 0.36%) and 18.86% (95%CI: 39.88%, 9.52%), respectively, without statistically significant difference (P>0.05).

Exposure to ambient air pollution and elevated blood levels of gamma-glutamyl transferase in a large Austrian cohort

Gamma glutamyl transferase (GGT) is related to oxidative stress and an indicator for liver damage. We investigated the association between air pollution and GGT in a large Austrian cohort (N = 116,109) to better understand how air pollution affects human health.Data come from voluntary prevention visits that were routinely collected within the Vorarlberg Health Monitoring and Prevention Program (VHM&PP). Recruitment was ongoing from 1985 to 2005. Blood was drawn and GGT measured centralized in two laboratories. Land use regression models were applied to estimate individuals' exposure at their home address for particulate matter (PM) with a diameter of <2.5 μm (PM2.5), <10 μm (PM10), fraction between 10 μm and 2.5 μm (PMcoarse), as well as PM2.5 absorbance (PM2.5abs), NO2, NOx and eight components of PM. Linear regression models, adjusting for relevant individual and community-level confounders were calculated.The study population was 56 % female with a mean age of 42 years and mean GGT was 19.0 units. Individual PM2.5 and NO2 exposures were essentially below European limit values of 25 and 40 μg/m3, respectively, with means of 13.58 μg/m3 for PM2.5 and 19.93 μg/m3 for NO2. Positive associations were observed for PM2.5, PM10, PM2.5abs, NO2, NOx, and Cu, K, S in PM2.5 and PM10 fractions and Zn mainly in PM2.5 fraction. The strongest association per interquartile range observed was an increase of serum GGT concentration by 1.40 % (95 %-CI: 0.85 %; 1.95 %) per 45.7 ng/m3 S in PM2.5. Associations were robust to adjustments for other biomarkers, in two-pollutant models and the subset with a stable residential history.We found that long-term exposure to air pollution (PM2.5, PM10, PM2.5abs, NO2, NOx) as well as certain elements, were positively associated with baseline GGT levels. The elements associated suggest a role of traffic emissions, long range transport and wood burning.

Iodine intake assessment in the staff of a Porto region university (Portugal): the iMC Salt trial

PurposeIodine deficiency disorder (IDD) is an ongoing worldwide recognized problem with over two billion individuals having insufficient iodine intake. School-aged children and pregnant women are often target groups for epidemiological studies, but there is a lack of knowledge on the general adult population. The aim of this study was to assess the iodine status among a Portuguese public university staff as a proxy for the adult working population.MethodsThe population study covered 103 adults within the iMC Salt randomized clinical trial, aged 24–69 years. Urinary iodine concentration was measured spectrophotometrically using the Sandell–Kolthoff reaction. Iodine food intake was assessed using a 24-h dietary recall. The contribution of discretionary salt to the iodine daily intake was assessed through 24-h urinary sodium excretion (UIE) and potentiometric iodine determination of household salt.ResultsThe mean urine volume in 24 h was 1.5 L. The median daily iodine intake estimated from 24-h UIE was 113 µg/day, being lower among women (p < 0.05). Only 22% of participants showed iodine intake above the WHO-recommended cutoff (150 µg/day). The median daily iodine intake estimated from the 24-h dietary recall was 58 µg/day (51 and 68 µg/day in women and men, respectively). Dairy, including yoghurt and milk products, were the primary dietary iodine source (55%). Iodine intake estimated from 24-h UIE and 24-h dietary recall was moderately correlated (Spearman rank correlation coefficient r = 0.34, p < 0.05). The average iodine concentration in household salt was 14 mg I/kg, with 45% of the samples below the minimum threshold preconized by WHO (15 mg I/kg). The contribution of discretionary salt to the daily iodine intake was around 38%.ConclusionThis study contributes new knowledge about iodine status in Portuguese working adults. The results revealed moderate iodine deficiency, particularly in women. Public health strategies and monitoring programs are needed to ensure iodine adequacy in all population groups.