Contribution Of Individual Factors Research Articles

Decomposition Analysis of Socioeconomic Inequalities in Vaccination Dropout in Remote and Underserved Settings in Ethiopia.

Despite increments in immunization coverage over the past decades, substantial inequality due to wealth status has persisted in Ethiopia. This study aimed to decompose the concentration index into the contributions of individual factors to socioeconomic inequalities of childhood vaccination dropout in remote and underserved settings in Ethiopia by using a decomposition approach. A wealth index was developed by reducing 41 variables related to women's household living standards into nine factors by using principal component analysis. The components were further totaled into a composite score and divided into five quintiles (poorest, poorer, middle, richer, and richest). Vaccination dropout was calculated as the proportion of children who did not get the pentavalent-3 vaccine among those who received the pentavalent-1 vaccine. The concentration index was used to estimate socioeconomic inequalities in childhood vaccination dropout, which was then decomposed to examine the factors contributing to socioeconomic inequalities in vaccination dropout. The overall concentration index was -0.179 (P <0.01), confirming the concentration of vaccination dropout among the lowest wealth strata. The decomposition analyses showed that wealth index significantly contributed to inequalities in vaccination dropout (49.7%). Place of residence also explained -16.2% of the inequality. Skilled birth attendance and availability of a health facility in the kebele (the lowest administrative government structure) also significantly contributed (33.6% and 12.6%, respectively) to inequalities in vaccination dropout. Wealth index, place of residence, skilled birth attendance, and availability of a health facility in the kebele largely contributed to the concentration of vaccination dropout among the lowest wealth strata. Policymakers should address vaccination inequality by designing more effective strategies.

A SHAP-enhanced XGBoost model for interpretable prediction of coseismic landslides

In the coseismic landslide hazard assessment (CLHA), advanced machine learning (ML) models have garnered significant attention due to their effectiveness in handling the complex relationships between landslides and various influencing factors. However, explaining the decision-making mechanisms of machine learning models that predict landslide spatial distribution based on these influencing factors remains challenging. This study compares the predictive performance of four models—XGBoost, RF, LR, and SVM—optimized using the TPE algorithm, and introduces the SHAP algorithm into the XGBoost model to achieve both global and local interpretations of CLHA. In various tests, the optimized XGBoost model demonstrated the best predictive performance, achieving an accuracy of 0.864 and an AUC value of 0.886. Global interpretations indicate that the occurrence of coseismic landslides is primarily influenced by triggering factors such as hypocentral distance and distance from the seismogenic fault. Terrain roughness and elevation, on the other hand, make significant contributions among the conditioning factors. Single-factor dependence plots indicate that the contribution of individual factors to landslides varies across different ranges of their feature values. Analysis of two-factor dependence plots reveals that interactions between factors are also crucial in influencing the occurrence of landslides. Combining field surveys with local interpretations confirms significant variations in the contributions of influencing factors within local ranges. The main innovation of this study lies in the integration of the SHAP algorithm into the CLHA model, revealing the decision-making mechanisms of the model for spatial prediction of coseismic landslides.

Time-Dependent Effects of Clinical Interventions on SARS-CoV-2 Immunity in Patients with Lung Cancer.

In patients with lung cancer (LC), understanding factors that impact the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike antibody (SAb) titers over time is critical, but challenging, due to evolving treatments, infections, vaccinations, and health status. The objective was to develop a time-dependent regression model elucidating individual contributions of factors influencing SAb levels in LC patients using a prospective, longitudinal, multi-institutional cohort study initiated in January 2021. The study evaluated 296 LC patients-median age 69; 55% female; 50% stage IV. Blood samples were collected every three months to measure SAb levels using FDA-approved ELISA. Asymptomatic and unreported infections were documented through measurement of anti-nucleocapsid Ab levels (Meso Scale Discovery). Associations between clinical characteristics and titers were evaluated using a time-dependent linear regression model with a generalized estimating equation (GEE), considering time-independent variables (age, sex, ethnicity, smoking history, histology, and stage) and time-dependent variables (booster vaccinations, SARS-CoV-2 infections, cancer treatment, steroid use, and influenza vaccination). Significant time-dependent effects increasing titer levels were observed for prior SARS-CoV-2 infection (p < 0.001) and vaccination/boosters (p < 0.001). Steroid use (p = 0.043) and chemotherapy (p = 0.033) reduced titer levels. Influenza vaccination was associated with increased SAb levels (p < 0.001), independent of SARS-CoV-2 vaccine boosters. Prior smoking significantly decreased titers in females (p = 0.001). Age showed no association with titers. This GEE-based linear regression model unveiled the nuanced impact of multiple variables on patient anti-spike Ab levels over time. After controlling for the major influences of vaccine and SARS-CoV-2 infections, chemotherapy and steroid use were found to have negatively affected titers. Smoking in females significantly decreased titers. Surprisingly, influenza vaccinations were also significantly associated, likely indirectly, with improved SARS-CoV-2 titers.

Predictors of Change in Creative Thinking Abilities in Young People: A Longitudinal Study

ABSTRACTPrevious research has examined how creative potential develops during childhood and has identified peaks, slumps, and bumps during maturation. However, little is known about the causes of such changes. This study employed a longitudinal approach to assess predictors of change in creative ideation over 2 years in young people (N = 76) aged 14–20 years old at the first time point. The analysis employed frequentist and Bayesian methods to evaluate the effect of individual factors, such as openness to experience (assessed using the BFI‐10 short form), and contextual factors, such as pursuing creative hobbies (measured with a leisure questionnaire). Openness at the first time point and change in openness over time predicted change in peak originality. Engagement in creative hobbies at the first time point predicted change in fluency. The findings support the contribution of individual factors, in the form of openness to experience, and contextual factors, in the form of engagement in creative hobbies, in the development of divergent thinking abilities in mid‐adolescence and young adulthood. Altogether, this study suggests that interventions aimed at increasing time spent on creative activities and promoting openness to experience may enhance divergent thinking abilities, which are essential components of creative potential.

Contributing Factors to Adverse Events in Long-Term Care Facilities in Ireland, a Content Analysis

ObjectivesThere is substantial research on contributing factors to adverse events (AEs) in acute settings. Little is known about AEs in long-term care facilities (LTCFs). Our aim was to identify contributing factors to AEs from LTCFs for older persons and people with disability to inform quality improvement. DesignContent analysis of statutory notifications of AEs from LTCFs using a modified version of the Human Factors Analysis and Classifications System (HFACS) applied to health care. SettingA sample of high risk-rated notifications of AEs received by the regulator in 2018 and 2019 was drawn from the Database of Statutory Notifications from Social Care in Ireland (n = 156). MethodsTwo researchers independently analyzed notifications to identify contributing factors using our modified HFACS. The number of factors identified in each level, subcategory, and nanocode of the HFACS was calculated along with percentage representation of factors within notifications and percentage contribution of individual factors to the total number of factors identified. The number and percentage contributions of factors were also calculated, disaggregated by notification type. ResultsContributing factors from all levels of the HFACS were identified. The most common contributing factor was “Resident factors” followed by “Unsafe acts.” No contributing factors were attributed to 68 notifications (43.6%). Multiple contributing factors were attributed to 45 notifications (28.8%). The largest percentage of factors was identified in notifications of serious injuries (27.4%). Conclusion and ImplicationsIt is the responsibility of LTCFs to account for “Resident factors” in system design, risk management, and care plans. Developing systems and processes that identify where residents have higher risk of harm may reduce the occurrence of high-risk events and thus improve resident safety. The large proportion of notifications where no contributing factors were identified suggests the need for improved reflection and reporting from LTCFs and for more specific questions on notification forms.

Investigating the mental health of university students during the COVID-19 pandemic in a UK university: a machine learning approach using feature permutation importance

Mental wellbeing of university students is a growing concern that has been worsening during the COVID-19 pandemic. Numerous studies have gathered empirical data to explore the mental health impact of the pandemic on university students and investigate factors associated with higher levels of distress. While the online questionnaire survey has been a prevalent means to collect data, regression analysis has been observed a dominating approach to interpret and understand the impact of independent factors on a mental wellbeing state of interest. Drawbacks such as sensitivity to outliers, ineffectiveness in case of multiple predictors highly correlated may limit the use of regression in complex scenarios. These observations motivate the underlying research to propose alternative computational methods to investigate the questionnaire data. Inspired by recent machine learning advances, this research aims to construct a framework through feature permutation importance to empower the application of a variety of machine learning algorithms that originate from different computational frameworks and learning theories, including algorithms that cannot directly provide exact numerical contributions of individual factors. This would enable to explore quantitative impact of predictors in influencing student mental wellbeing from multiple perspectives as a result of using different algorithms, thus complementing the single view due to the dominant use of regression. Applying the proposed approach over an online survey in a UK university, the analysis suggests the past medical record and wellbeing history and the experience of adversity contribute significantly to mental wellbeing states; and the frequent communication with families and friends to keep good relationship as well as regular exercise are generally contributing to improved mental wellbeing.

Changes in European fire weather extremes and related atmospheric drivers

Recent destructive fire seasons around the world have increased concern that climate change is escalating the frequency, intensity, and extent of wildfires. While the contributions of individual factors can be debated, the scientific literature concludes that fire weather is one prominent driver of fire activity. Our analysis of the relationship between fire weather extremes and burned area in Europe revealed that the number of extreme fire weather days per year correlates positively with the annual burned area over most of the study domain. The evidence presented in our study strongly suggests that fire weather extremes in Europe have become more frequent in recent decades while affecting increasingly larger areas and occurring earlier and later in the fire season. Increases in fire weather extremes occurred abruptly in several parts of Europe, as evidenced by the detection of statistically significant change-points in the time series of the annual extreme fire weather days. Our analysis revealed that, at the regional scale, significant change-points occurred around the late 1990s and mid-2000s in the Mediterranean, marking an abrupt rise in the median of extreme fire weather days per year. We found that high-latitude blocking and sub-tropical ridging drive fire weather extremes in northern and southern Europe, respectively. The results of our study suggest that fire weather extremes are today more likely to occur than in the past, and the increasing occurrence of extreme fire weather could lead to rapidly reaching adaptation limits, thereby reversing currently stagnating or even declining trends reported for fire activity in Europe. Corresponding with extreme fire weather years reported here, recent destructive fire seasons exemplify the limits of fire suppression and the need to adapt to the reality of more frequent extreme fire weather.

The impacts of meteorological factors on the incidence of hand, foot, and mouth disease in China: An interactive perspective

Hand, foot, and mouth disease (HFMD) is a contagious illness and a matter of public health concern globally. It has a widespread impact in China, where it poses a significant risk to the health of young children. Previous research has discovered that the occurrence and spread of HFMD are closely linked to meteorological factors, but the specifics of these intricate nonlinear relationships and how they interact with the incidence of HFMD are still uncertain. In the present study, we filled this gap in knowledge by first obtaining data on hand, foot, and mouth disease from the National Health Commission of China alongside real-time surveillance data from meteorological stations. We then used Spearman's correlation analysis to identify the variables significantly related to HFMD, investigating the existence of co-curvilinearity among the contributing factors by using the independent effects model in the generalized additive model to analyze both the contribution of individual factors and their combined effects on the incidence of HFMD. The interactions between meteorological factors were then modeled to investigate their influence on HFMD. The results showed that, in descending order of effect size, air temperature (AT), sunshine duration (SD), wind speed (WS), and barometric pressure (BP) had a significant nonlinear impact on the incidence of HFMD, which displayed a fluctuating increase in line with rising temperatures. The interactions of (1) high temperature and low air pressure and (2) low wind speed and poor sunshine had the largest influence on HFMD. In descending order, the strengths of the interaction effects between factors were as follows: AT ∼ SD > AT ∼ WS > AT ∼ BP > WS ∼ BP > WS ∼ SD > BP ∼ SD. Our findings will inform policy initiatives contributing to the Healthy China strategy and assist in the prevention and control of HFMD by enabling the prediction and early warning of disease outbreaks.

Source identification and potential health risks of fluoride and nitrate in groundwater of a typical alluvial plain

A comprehensive understanding of the role of natural and anthropogenic factors in groundwater pollution is essential for sustainable groundwater resource management, especially in alluvial plains with intensive anthropogenic activities. Numerous studies have focused on the contribution of individual factors on groundwater pollution in alluvial aquifers, but distinguishing the effects of natural and anthropogenic factors is limited. In this study, 64 wells were sampled in different seasons from the Yellow River alluvial plain in China for hydrochemical and isotopic analysis to investigate the spatiotemporal distribution, sources and health risks of fluoride and nitrate in alluvial aquifers. Results showed that fluoride contamination was widely distributed without significant seasonal variation, and 78.1 % of the dry season samples and 65.6 % of the wet season samples showed fluoride concentrations above the permissible limit (1.5 mg/L). High-F− groundwater was generally accompanied by Na-HCO3 and Na-HCO3·SO4 water types. Fluoride was from a natural origin mainly associated with mineral dissolution, competitive adsorption, cation exchange, and evaporation. Groundwater nitrate contamination was spatially sporadic and showed significant seasonal differences. Only 13.6 % of the dry season samples and 3.2 % of the wet season samples had NO3− concentrations exceeded the permissible limit of 50 mg/L. The hydrochemical phase evolved from bicarbonate or sulfate type to chloride type with increasing nitrate concentration. Manure and sewage attributed to agricultural activities contributed the most nitrogen to groundwater, followed by soil organic nitrogen and chemical fertilizers, revealing the anthropogenic origin of nitrate. Nitrification was the dominant nitrogen transformation process in the wet season, and denitrification was prevalent in the dry season. Oral ingestion of high fluoride groundwater was a major threat to human health, especially for infants. This study provided a significant reference for water resources management in alluvial aquifers.

EPH187 Inequities in Healthcare Expenditures for HIV Care in India: An Evidence from the Nationwide Cross-Sectional Data

The National AIDS Control Organization estimates that around 2.14 million people with HIV/AIDS live in India. The burden of HIV is particularly high among the socially isolated. With the lack of financial protection mechanisms in India, people face an increasing burden of out-of-pocket health expenditures (OOPHE) and catastrophic health expenditures (CHE) while paying for healthcare. The objective of this study is to examine the wealth-based inequities associated with the healthcare expenditures for HIV care and to analyze the income related inequalities, mapping the determinants and contribution of individual factors in driving these inequalities.

Dynamic Aspects of Pre-Soft Contact Lens Tear Film and Their Relation to Dry Eye: Basic Science and Clinical Relevance.

Soft contact lens (SCL) perturbs the intimate connection between the pre-lens tear film (PLTF) and the ocular surface in various ways, i.e., (i) decrease in tear meniscus radius and aqueous tear thickness, (ii) attenuation of tear film lipid layer spread, (iii) limited wettability of SCL surface, (iv) increased friction with eyelid wiper, etc. This often results in SCL-related dry eye (SCLRDE) manifested as PLTF instability and contact lens discomfort (CLD). In this review, the individual contributions of factors (i-iv) to PLTF breakup patterns (BUP) and CLD are considered via the tear film-oriented diagnosis framework adopted by the Asia Dry Eye Society from a clinical and basic science perspective. It is shown that SCLRDE (due to aqueous deficiency, increased evaporation, or decreased wettability) and BUP of PLTF classify within the same types as the ones observed for the precorneal tear film. The analysis of PLTF dynamics reveals that the inclusion of SCL enhances the manifestation of BUP associated with (i) decreased thickness of PLTF aqueous layer and (ii) limited SCL wettability as shown by the rapid expansion of BUP area. PLTF thinness and instability result in increased blink-related friction and lid wiper epitheliopathy as major contributor to CLD.

The gender labor productivity gap across informal firms

This study uncovers a gender labor productivity gap among informal firms in 14 developing economies. The mean labor productivity of women-owned informal firms is approximately 15.6 percent (0.17 log points) lower than that of men-owned informal firms. The difference in productivity is larger at the lower quantiles of the labor productivity distribution than at the higher quantiles. The Kitagawa-Oaxaca-Blinder and quantile decomposition methods are used to estimate the aggregate “endowment” vs “structural” effects, and individual factors’ contribution to the productivity gap. Several policy-relevant findings are revealed. First, the labor productivity gap at the mean is significantly larger due to lower education, prior work experience, capitalization, and less protection from crime among women than men owners of informal firms. The smaller size of the women-owned firms and their higher return from operations under contracts narrow the mean productivity gap. Second, the productivity gap at the mean and different labor productivity quantiles can be substantially narrowed by providing more resources to women owners of informal firms, such as education, managerial experience, and physical capital, without improvements in their returns to women-owned informal businesses. Third, overall, there is evidence of “sticky floors” for women owners, but not “glass ceilings”. Fourth, there is heterogeneity in the contribution of individual factors to the productivity gap at different quantiles of the labor productivity distribution. Targeting policies to the relevant quantiles will improve their effectiveness. Fifth, there are important similarities and differences between groups of countries in low-income Africa, middle-income Africa, and Latin America, as far as the gender labor productivity gap and its drivers are concerned. Thus, an eclectic policy approach is needed, combining the broader findings of the literature with the prevailing local conditions. Last, the data do indicate that a majority of women- and men-owned informal businesses would like to formalize.

Possible Physiological Mechanisms of Leaf Photodamage in Plants Grown under Continuous Lighting

Unlike the natural photoperiod that includes the alternation of day and night in the diurnal cycle, continuous (24 h a day) lighting provides uninterrupted supply of light energy required for photosynthesis, permanently promotes photooxidative processes, implies continuous signaling to the photoreceptors, and desynchronizes the internal circadian biorhythms from the external light/dark cycle (circadian asynchrony). The leaves of many plant species grown under constinuous lighting are prone to characteristic and potentially lethal interveinal chlorosis and necrosis. The photodamage of plant leaves exposed to long photoperiods, including daily 24-h illumination was described more than 90 years ago, but the causes of this phenomenon are still not entirely clear. Biological bases underlying this phenomenon are theoretically and practically important, because growing plants under a 24-h photoperiod at a relatively low photon flux density is seemingly an effective way to save resources and increase plant productivity in greenhouses and plant factories with artificial lighting. This review of available literature compiles and evaluates the arguments both supporting and confronting the hypothesis that carbohydrate accumulation, specifically the hyperaccumulation of starch in leaves, is the main cause of photodamage to plants grown under continuous lighting or long photoperiods. The analysis of a large number of studies indicates that the accumulation of carbohydrates is neither the main nor the only cause of leaf injuries in plants grown under a 24-h photoperiod, although the role of this factor in photodamage cannot be ruled out. The appearance and development of photodamage under a 24-h photoperiod is presumably due to several simultaneously acting factors, such as photooxidation, stress-induced senescence, and circadian asynchrony. The contribution of individual factors to photodamage may vary substantially depending on environmental conditions and biological properties of the object (plant species and variety, plant age, and the stage of development).

Influence of various parameters on the vegetable raw material pelleting process and pellets quality (review)

Determining the regularities of the process of pelleting vegetable raw materials is relevant for the improvement of technologies and technical equipment in order to reduce energy intensity and improve the quality of pellets. The generalization of the results of the research aimed at studying the influence of various parameters on the process of pelleting vegetable raw materials and the quality of feed and biofuel pellets is the purpose of the research. A selection and systematic review of the scientific literature on the subject of the study for the period of 2007-2022 has been carried out. The analysis has proved that heat pre-treatment and moistening of vegetable raw materials, as well as their composition and particle size are the factors that have the greatest impact on the quality of feed and biofuel pellets. Increasing the pressure in the range of 20...200 MPa results in increasing the pellets durability. A die temperature of around 100°C is optimum for obtaining dense pellets of highquality from vegetable raw materials. The design parameters of the pelletizer play an important role in obtaining high-quality pellets when processing vegetable raw materials. The design of the inlet in the form of a tapering cone helps to reduce energy consumption and pelleting pressure. An increase in the ratio of the die channel length to its diameter exponentially increases the pelleting pressure and its energy intensity. The interplay between the physical processes occurring in the pelletizer makes it difficult to interpret the impact of each parameter on the pelleting process, so different authors have different assessments of the contribution of individual factors in producing high-quality pellets. Therefore, the interaction between the individual pelleting parameters and their influence on the results of the process should be examined more precisely.

Measuring effects of packaging on willingness-to-pay for chocolate: Evidence from an EEG experiment

Willingness to pay (WTP) for food may be influenced by multiple custom and product characteristics, including food packaging. Thus, evaluating WTP for food and the contributions of individual factors, affecting it, remains a puzzling question for many fields, such as food science, economics and marketing. In fast-growing and high-volume markets, such as the market of chocolate and chocolate confectionery products, this issue becomes of special practical importance. However, reliable evaluation of WTP, using traditional approaches, such as surveys, requires large samples to be tested, making such investigations rather costly and time-consuming. Furthermore, such approaches assume that all participants can clearly express their preferences, detect subtle differences in product characteristics, and they are not biased, providing socially desirable responses. These circumstances prompt the development of new, more elaborated and less subjective approaches for WTP estimation, including those based on participants' neurophysiological reactions. Electroencephalography (EEG) is a relatively cheap and efficient method to evaluate neurophysiological reactions. Thus, the goal of the present work is to test whether using EEG neurometrics may improve evaluation of the WTP for food, compared to models based only on the stated perceived taste quality. Indeed, the obtained results suggested that both perceived taste and EEG beta power at frontal electrode F4 strongly but independently correlate with the WTP for chocolate, making it reasonable to combine both factors for better WTP modeling. The suggested model for WTP was applied to investigate the impact of chocolate packaging. This was achieved by comparing the WTP stated by respondents, when they were exposed to chocolate packaging, with WTP estimates based on the model that was created using data from “blind” experiments, when the respondents were not aware of the product properties. The revealed difference is likely to reflect packaging effects on chocolate WTP and confirms strong packaging effects for well-known brands.

Predictors of Metformin Failure: Repurposing Electronic Health Record Data to Identify High-Risk Patients.

Metformin is the first-line drug for treating diabetes but has a high failure rate. To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure. A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (<7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure. In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P < .0001). Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.

Knowledge Sharing Model Based on Individual and Organizational Factors Related to Faculty Members of University

This study presents the knowledge-sharing model based on individual and organizational factors related to faculty members. To achieve this goal, individual and organizational factors were presented through qualitative research in the form of open codes, axial, and selective observations; then, the final model was obtained using a structural equation model. Participants included 1,719 faculty members of the university in Iran. The samples related to the qualitative survey included 25 faculty members and the samples for the quantitative survey included 326 faculty members selected by multistage cluster sampling. A 72-item questionnaire was used to measure the quantitative variables. The results showed that the status of knowledge sharing is moderate in universities. Individual factors influencing knowledge sharing included the sharing of educational materials, perception, confidence and knowledge self-efficiency, and organizational factors influencing knowledge sharing included structural social capital, cognitive social capital, social capital relations, organizational communication, organizational structure, organizational culture, IT infrastructure and systems of rewards. Finally, it was found that the contribution of individual factors on knowledge sharing was more than organizational factors; therefore, a model was presented in which the contribution of individual and organizational factors was determined.

ИЗМЕНЕНИЕ ТЕМПЕРАТУРЫ И ВЛАЖНОСТИ В ЛЕСНЫХ СООБЩЕСТВАХ ВДОЛЬ ГРАДИЕНТА ВЫСОТЫ НА ГОРЕ БОЛЬШОЙ ШАТАК

Due to climate change, there is an increase in mean annual temperatures, which affects the altitudinal distribution of vegetation in the mountains. The purpose of this study was to investigate temperature and humidity changes along the altitudinal gradient on the Bolshoi Shatak mountain range from the foot of the mountain to the upper limit of forest distribution. Temperature and relative humidity data were collected using DS1923-F5 loggers in pine-broadleaf, birch-pine and larch forest located at altitudes of 631, 831 and 966,46 m msl, respectively. No abnormal winter temperatures were observed during the study period. The duration of the frost-free period was 157 days in the pine-broadleaf forest, 151 days in the birch-pine forest and 143 days in the larch forest. In the summer period of 2021 there was a summer drought, which was manifested to an equal extent in the sample plots in pine-birch and larch forest and to a lesser extent in pine-broadleaved forest. In the rainfall-normal year 2022, humidity content was significantly higher in pine-birch forest than in larch forest. Thus, the factors influencing the distribution of broad-leaved species at present may be the humidity regime and shorter frost-free period above the boundary of their current distribution. Further monitoring using loggers is needed to make a final conclusion on the contribution of individual factors to the current distribution of broad-leaved species.

The influence of environmental factors on the prevalence of «thrifty genotypes» as predictors of metabolic disorders

"Thrifty genotypes" are the risk factors for obesity and lipid and energy metabolism disorders. Hence, it is important to assess the contribution of environmental factors that influenced the thrifty genotypes' population distribution. Aim of the study - systematization and critical analysis of published data on population variability, relationship with climatic and environmental characteristics, association with traditional types of lifestyles, and nutrition for the «thrifty genotypes» of APOE, UCP1, UCP3, and FTO genes. Material and methods. The selection of publications from the last 20-25 years presented in the PubMed database (https://pubmed.ncbi.nlm.nih.gov) was carried out by the keywords of the generalizing rank (thrifty genotype, thrifty phenotype, drifty genotype), then narrowed down to the APOE, UCP, FTO. The final set includes publications that consider the association of genotypes with the ecological conditions of the population. Results. Our analysis of publications has confirmed the ethnic and geographical variability in the allele distribution of APOE, UCP1, UCP3, and FTO genes. However, the nature of this variability hasn't been studied sufficiently; the contribution of individual factors of the natural and anthropogenic environment remains unclear. The information on the geographical distribution of the APOE gene alleles is quite complete, while the data on the «thrifty genotypes» of UCP and FTO require further study. Conclusion. The frequency of the UCP1 and UCP3 alleles associated with effective non-contractile thermogenesis is increased in populations adapted to low temperatures. However, the population-geographical pattern of the UCP thrifty genotypes' variability as a determinant of increased fat deposition has been studied insufficiently. The carriage of FTO mutant variants increases the adaptability of groups with a traditional lifestyle and diet but is maladaptive in an urbanized environment. The influence of natural and ecological conditions on the formation of the FTO allele geographical distribution requires more attention. The results obtained allow us to propose the included groups' ranking according to the past environmental management and nutrition will facilitate the search for ecological factors that influenced the geographical distribution of genotypes (and, accordingly, populations with different levels of risk of metabolic disorders).

Multi-level Analysis of Health Management Information System (HMIS) Adoption by Healthcare Professionals in the ESIC Main Hospital and Dispensaries in the Tirunelveli Sub-region in Tamil Nadu, India

This study aims to analyse the unique contribution of individual factors on HMIS adoption among healthcare professionals in ESIC healthcare settings, as well as possible interrelations between these factors.. A simple random sampling method was used to select 171 healthcare professionals in the ESIC main hospital and dispensaries in the Tirunelveli sub-region. One-way ANOVA and Post-Hoc test were applied to identify the individual determinants of HMIS adoption in ESIC clinical settings. This study will assess the contribution of individual factors, as well as their interactions, to the implementation of HMIS in ESIC clinical settings. The analysis provides a set of key results making it possible to understand the challenges and opportunities for HMIS adoption by healthcare professionals.