Health Indicators Research Articles

GINGIVAL STATUS AND ORAL HYGIENE HABITS IN CHILDREN BETWEEN 10-12 YEARS

Introduction: Children's adolescence is characterized by dynamically changing indicators of periodontal and oral health, related mainly to the eruption of the permanent dentition and stabilization of the periodontal space. This age period is also characterized by an increased incidence of gingival diseases, with the dental biofilm being the main risk factor for the inflammatory process. Aim: The aim of this study is to assess the relationship between gingival status, oral hygiene habits and knowledge on topics related to oral health in children aged 11-13. Material and methods: The subject of the study was 106 children aged 11-13 years. All children overwent a clinical periodontal examination and periodontal risk assessment at 4 levels. An electronic periodontal probe PA ON (Orangedental) with specially designed software was used to assess periodontal and oral hygiene status. The study included a survey method for assessing children's knowledge on topics related to oral health with periodontal focus. The survey consists of 38 questions divided into eight topics. Results: All the children aged 11-13 years had an initial form of gingival inflammation, against the background of plaque accumulation affecting more than 69% of tooth surfaces, unstable and irregular oral hygiene habits, as well as insufficient knowledge of oral/periodontal health. Through appropriate preventive programs, this age is best suited to create consciously motivated behavior for proper oral hygiene and oral health.

Determination of Organic Carbon Content of the Soils within the Greenhouses Built on Pyroclastic Deposits in Isparta Settlement Area

Soil organic carbon (SOC) is an important indication of soil health and helps to sustain soil fertility. As a result, determining its composition and the factors that influence it is critical for long-term soil nutrient management, especially in controlled conditions such as greenhouses. This study utilizes machine learning to classify SOC content in greenhouses built on pyroclastic deposits in the Isparta region. A dataset of 276 samples and eight variables—clay (%), silt (%), sand (%), soil electrical conductivity (EC), pH, elevation, slope, and aspect—were used to model SOC values. SOC content was classified into five classifications: very low (2.3%). In this study, five machine learning models—Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)—were evaluated using cross-validation to determine their classification accuracy, precision, recall, F-score, and ROC area. Random Forest (RF) and Decision Tree (DT) outperformed the other models, with RF achieving the highest overall accuracy (76.4%), precision (77.3%), and AUC (0.904), followed by DT at 75.4% and AUC of 0.874. This study shows the practicality of machine learning models in categorizing SOC content, highlighting their importance for long-term soil health and fertility control in greenhouse conditions. To improve model efficacy, future studies should include more auxiliary variables, such as soil physical and chemical qualities and lithological data, as well as a wider range of soil types.

Impact of tooth loss and patient characteristics on coronary artery calcium score classification and prediction.

This study, for the first time, explores the integration of data science and machine learning for the classification and prediction of coronary artery calcium (CAC) scores. It focuses on tooth loss and patient characteristics as key input features to enhance the accuracy of classifying CAC scores into tertiles and predicting their values. Advanced analytical techniques were employed to assess the effectiveness of tooth loss and patient characteristics in the classification and prediction of CAC scores. The study utilized data science and machine learning methodologies to analyze the relationships between these input features and CAC scores. The research evaluated the individual and combined contributions of patient characteristics and tooth loss on the accuracy of identifying individuals at higher risk of cardiovascular issues related to CAC. The findings indicated that patient characteristics were particularly effective for tertile classification of CAC scores, achieving a classification accuracy of 75%. Tooth loss alone provided more accurate predicted CAC scores with the smallest average mean squared error of regression and with a classification accuracy of 71%. The combination of patient characteristics and tooth loss demonstrated improved accuracy in identifying individuals at higher risk with the best sensitivity rate of 92% over patient characteristics (85%) and tooth loss (88%). The results highlight the significance of both oral health indicators and patient characteristics in predictive modeling and classification tasks for CAC scores. By integrating data science and machine learning techniques, the research provides a foundation for further exploration of the connections between oral health, patient characteristics, and cardiovascular outcomes, emphasizing their importance in advancing the accuracy of CAC score classification and prediction.

Quantitative Evaluation of Reinforced Concrete Slab Bridges Using a Novel Health Index and LSTM-Based Deterioration Models

The Health Index (HI) serves as an essential tool for assessing the structural and functional condition of bridges, calculated based on the condition of structural components and the serviceability of the bridge. Its primary purpose is to identify the most deteriorated structures in an asset inventory and prioritize those in most urgent need of repair. However, a frequently cited issue is the lack of accurate and objective data, with the determination of the HI often being heavily reliant on expert opinions and engineering judgment. Furthermore, the HI systems used in most countries are dependent on the current state of bridge components, making it challenging to use as a proactive indicator for factors such as the rate of bridge aging. To address this issue, this study introduces a novel HI as a quantitative evaluation metric for reinforced concrete slab bridges and details the process of deriving the HI based on deterioration models. The deterioration models are derived by preprocessing the deterioration data of reinforced concrete (RC) slab bridges, wherein the relationship between time and deterioration is directly employed for training a long short-term memory model. The HI was validated through a case study involving six RC slab bridges, wherein accuracies of >93% were achieved, confirming that the proposed quantitative evaluation methodology can significantly contribute to maintenance decisions for bridges.

Optimising bearing degradation prediction: a bayesian vector autoregressive-assisted DeepAR model approach

Abstract Accurate prediction of the remaining useful life of bearings is crucial for the maintenance of rotating machinery. Current research often involves extracting multidimensional features from vibration signals to construct health degradation indicator, and health indicator is directly used for degradation prediction. However, constructed health indicator exhibit complex nonlinearity and fail to utilize the correlations with the original multidimensional features. To address this limitation, we propose an integrated prediction framework, known as the bayesian vector autoregression-assisted DeepAR method. The proposed two step prediction framework combines the advantages of covariance prediction and temporal correlation prediction, and realizes the full utilization of multidimensional features correlation information. Additionally, the proposed approach utilizes the intrinsic properties of statistical models to constrain improve the prediction results of deep learning methods, achieving nonlinear modeling of the degradation indicator while maintaining the stability of the prediction results. This process of prediction, re-estimation and adaptive adjustment achieves a balance in model performance. Finally, experimental studies using the PRONOSTIA and XJTU-SY datasets validate the effectiveness of the proposed method in improving the prediction accuracy of bearing health degradation indicators and enhancing the stability of multistep prediction in deep model.

Identifying slow-growing commercial pigs using growth performance and health indicators.

This study aimed to evaluate the growth performance and health status of commercial pigs in different body weight (BW) groups and develop methods for identifying slow-growing pigs. The research observed 79 commercial pigs grouped from 104 to 202 days of age, collecting data on BW, feed intake, and body condition score (BCS) from caliper. Results showed that BCS were highly correlated with BW (r > 0.85, P < 0.001), providing a simple method for assessing commercial pig BW. Commercial pigs with slaughter BWs below 114kg at approximately 200 days old were identified as slow-growing individuals. White blood cell count, insulin-like growth factor binding protein-3, and blood urea nitrogen were identified as potential biomarkers for identifying slow-growing pigs. Economic analysis revealed that profitability across all BW groups was highly sensitive to market conditions, with feed costs being the most significant factor. Low BW groups performed better in low-price markets but reach break-even points later under medium to high-price scenarios.

Effects of the CALM intervention on cancer-related fatigue and heart rate variability in NSCLC: a randomized trial.

To evaluate the effects of CALM intervention on cancer-related fatigue (CRF), quality of life (QOL), and heart rate variability (HRV) in non-small cell lung cancer (NSCLC) patients. A total of 153 NSCLC patients were enrolled in the study. Participants were randomly assigned to the CALM group and usual care (UC) group. Patients in the CALM group were received 6 CALM intervention sessions over a 12-week period and evaluated using CRF, QOL and HRV at baseline (T0) and after 2, 4, and 6 intervention sessions. The CALM group showed significant differences in total CRF, behavioral/daily life CRF, emotional/affective CRF, sensory/physical CRF, cognitive CRF and QOL before and after CALM intervention sessions (F = 643.47, F = 208.34, F = 354.52, F = 285.69, F = 334.29, F = 245.77, p < 0.001). The CALM group showed significant increases in standard deviation of normal R-R interval (SDNN) and high-frequency power (HF) (z = -4.94, p < 0.001,z = -4.30, p < 0.001). Significant negative correlations were observed between CRF and SDNN, HF and QOL across all participants. The CALM intervention had a positive impact on overall physical and mental health, fatigue reduction, quality of life and autonomic dysfunction in NSCLC patients. Heart rate variability may serve as an observational indicator of physical and mental health.

Research on the Relationship Between Attachment Styles and Mental Health (Depression and Anxiety) among Chinese University Students

Abstract: Mental health problems are becoming more serious globally. Relevant literature suggests that insecure attachment has the potential to lead to a range of mental health problems. And increasing attachment security can greatly improve psychological resilience and overall mental health. In this study, 103 Chinese university students were selected to investigate the relationship between their attachment styles and their mental health, especially depression and anxiety. A structured questionnaire based on the Experiences in Close Relationships Scale (ECR-R) was used to assess participants' attachment-related anxiety and avoidance, and the PHQ-9 and GAD-7 were used to assess mental health indicators. The findings reveal that significant differences in depression and anxiety levels between attachment types. Individuals with secure attachment styles exhibit significantly lower levels of depression and anxiety compared to those with insecure attachment styles, such as preoccupied, dismissive. In addition, attachment-related anxiety and avoidance scores could predict depression and anxiety levels. These results underscore the protective nature of secure attachment and highlight the importance of considering attachment styles in mental health interventions.

DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction

Cardiovascular Disease (CVD) is one of the main causes of death in recent years. To overcome the challenges faced during diagnosing CVD at an early stage, deep learning has been used. With advancements in technology, the clinical practice in the health care industry is likely to transform significantly. To predict CVD, we constructed two models: Dense Belief Network (DB-Net) and Deep Vanilla Recurrent Network (DVR-Net). Proximity Weighted Random Affine Shadow sampling balancing technique is used for balancing the highly imbalanced Heart Disease Health Indicator dataset. SHapley Additive exPlanations exhibits each feature’s contribution. It is used to visualize features contribution to the output of DB-Net and DVR-Net in CVD prediction. Furthermore, 10-Fold Cross Validation is performed for evaluating the proposed models performance. Cross-dataset evaluation is also conducted on proposed models to see how well our proposed models generalize on unseen data. Various evaluation measures are used for assessment of models. The proposed DB-Net outperforms all the base models by achieving an accuracy of 91%, F1-score of 91%, precision of 93%, recall of 89%, and execution time of 1883 s on 30 epochs with batch size 32. The DVR-Net beats the state-of-art models with an accuracy of 90%, F1-score of 90%, precision of 90%, recall of 90%, and execution time of 2853 s on 30 epochs with batch size 32.

Short-term effects of a research-scale oyster cage aquaculture system on sediment transport, water quality, and seagrass meadow health in Copano Bay, TX, USA

Competing uses for nearshore coastal space, such as aquaculture, have complex environmental and ecological interactions with surrounding seagrass meadows. Oyster aquaculture is among the fastest-growing aquaculture segments in the United States, and it brings concerns such as increased sedimentation from farm maintenance operations and altered water quality through oyster filtration. Changes in seagrass coverage and growth are common indicators of ecological health used to determine anthropogenic impacts on nearshore environments. This study characterized the effects of a research-scale adjustable long-line oyster aquaculture system on the health of adjacent seagrass meadows in Copano Bay, TX. Four Halodule wrightii meadows were identified at various distances from the research site: 100 m upstream (upstream), directly adjacent (0 m), 30 m downstream (30 m), and 60 m downstream (60 m). Sites were monitored for 1) seagrass health, 2) water quality parameters, and 3) sediment deposition. Over the 18-week sampling period, no significant differences (p&amp;gt;0.05) were found for water quality variables or sediment grain-size analysis using a one-way ANOVA. A linear mixed-effects model was used for repeated measures of seagrass data, with no effect of the site found on mean seagrass length, coverage, or maximum length (p&amp;gt;0.05). These results suggest that seagrass health indicators were unaffected by proximity to the oyster system. It was concluded that an oyster research aquaculture system of the type and size at the project location had no major negative or positive impact on seagrass meadow health due to high background variability and the overall minor footprint of the oyster farm across a comparatively large spatial extent.

Gender differences in periodontal pocket microflora in patients with chronic generalized periodontitis

Background: Periodontal disease, the primary cause of tooth loss, is a critical public health concern with a considerable impact on quality of life. It is of interest to explore gender differences in the course of inflammatory periodontal diseases, as well as the prospects of their personalized treatment and prevention in males and females. Aim: To assess the dental health and quantitative composition of periodontal pocket microflora in periodontitis patients, taking into account gender differences. Materials and Methods: The study included 39 patients with moderate chronic generalized periodontitis aged 40–60 years. The diagnosis was made using both primary and secondary dental diagnostic tools. The study had two groups: males (41%, n=16) and females (59%, n=23). The following parameters were assessed: oral hygiene index (OHI-S), severity of gingival inflammation (PMA), and quantitative composition of periodontal pocket microflora (DENTOSCREEN, “Lytech” Co. Ltd., Russia), taking into account gender differences. The study findings were statistically processed using Microsoft Office Excel 2016 with Data Analysis and AtteStat 12.0.5 add-ins. The Shapiro–Wilk test was used. The data are presented as median and interquartile range: Me [Q1; Q3]. The frequency of microorganisms is presented as percentage of the mean and standard error of the mean. The confidence interval was calculated using the Wald method, according to the formula for proportions and frequencies. For small values, the Wald method with Agresti–Coull correction was used. Samples were compared using the following tests: the Mann–Whitney U test was used for quantitative data and Fisher’s angular transformation (φ*emp) for frequency (nominal) attributes. Significant differences were defined as a probability 95% (p 0.05). Results: The quantitative composition of periodontal pocket microflora in patients with moderate periodontitis revealed significant gender differences, as demonstrated by an increase in the count of Fusobacterium nucleatum and Prevotella intermedia, as well as the frequency of Porphyromonas gingivalis, in males compared to females (φ*emp=1.736; p 0.05). Dental health indices confirmed the hypothesis of significant differences between males and females in terms of plaque index (OHI-S) and РМА. In males, poor oral hygiene was reported in 93.7% cases, which was significantly higher (φ*emp=2.04; p 0.05) than in females (69.6%). Moderate gingival inflammation was more prevalent in males, while mild gingival inflammation was more prevalent in females (p 0.05). Conclusion: The study findings emphasize the importance of oral hygiene in the prevention of chronic periodontitis and can be used to improve complex treatment algorithms for patients with periodontal diseases. This includes more frequent dental check-ups and professional oral care sessions, as well as oral hygiene training and monitoring, in males compared to females. Diagnosis by polymerase chain reaction allows for the identification and quantitative determination of DNA from periodontopathogenic bacteria at low concentrations, as well as the justification of treatment algorithms and prediction of treatment outcomes.

Alcohol-attributable deaths in Thai people from 2015 to 2021 using the comparative risk assessment approach.

The alcohol-attributable mortality rate is an important health indicator for surveillance of health-related impacts of alcohol consumption. This study aimed to estimate the annual number and rate of alcohol-attributable deaths among the Thai population aged 15 years and over during 2015-2021. Mortality data were drawn from the National Death Registry based on ICD-10. We used the standard methodology of comparative risk assessments for alcohol within the general framework of the Global Burden of Disease Studies and used alcohol-attributable fractions, derived from exposure, and relative risk compared to lifetime abstainers as the counterfactual. Age-standardization was used to adjust mortality rates which were calculated by cause, age group, and sex. The estimated annual number of alcohol-attributable deaths was 20,039 (men: 17,726 [6.50% of total annual deaths of the Thai population] and women: 2312 [1.11%]). The age-standardized alcohol-attributable mortality rates continuously increased from 33.8 to 37.5 deaths per 100,000 population from 2015 to 2019 and slightly decreased to 34.5 and 35.3 in 2020 and 2021, respectively. The three leading causes of death attributed to alcohol consumption were road injuries, cirrhosis and other liver diseases, and other unintentional injuries. Alcohol remains an important preventable cause of death among Thais. The alcohol-attributable mortality rate increased from 2015 to 2019 but declined in 2020 and 2021, possibly due to the coronavirus pandemic and lockdown measures. Culturally appropriate, cost-effective interventions should be used to control alcohol accessibility, particularly among young people who frequently sustain injuries from external causes and have high mortality rates.

Bearing life prediction method based on novel health indicators and improved similarity curve matching

Abstract To tackle the issues of low accuracy in similarity curve matching and the challenges in measuring the similarity of degraded curve segments in rolling bearing life prediction methods, we propose a novel bearing remaining useful life (RUL) prediction method utilizing new health indicators and improved similarity curve matching techniques. Initially, time and frequency domain features are extracted from the bearing vibration signals. Subsequently, the multi-dimensional features are meticulously screened using monotonicity, Spearman's correlation, and uncertainty metrics to construct the health indicator (HI) and the health indicator library for the bearing. Subsequently, the support vector data description (SVDD) combined with the PID search algorithm is employed to ascertain the initial prediction time of the bearing and to construct the corresponding health indicator library. Following this, both global and local feature capture are conducted using dual dynamic time warping (DDTW) in conjunction with a sliding window to predict the remaining service life of rolling bearings.&amp;#xD;The proposed method is validated on the PHM2012 and XJTU-SY datasets and benchmarked against the latest research. The results demonstrate that our method significantly enhances the prediction accuracy of the remaining useful life of bearings, underscoring the effectiveness and superiority of the proposed approach.and superiority of the proposed approach.&amp;#xD;

Assessment and role of physical activity as a factor affecting subjective perception of students' satisfaction

The paper aims to provide analysis of Slovak university students’ physical activity during state-ordered distance learning in the academic year 2021/2022 (the COVID-19 pandemic). The data were obtained in form of a questionnaire survey. In the analytical part, the focus is on weekly time that students devoted to regular exercise and quantification of physical activity versus subjective perception of selected physical and psychological satisfaction and health indicators. To meet the paper’s objectives, a two-sample t-test was used to test statistical significance of differences between men and women, and parametric and non-parametric correlation analysis methods. In terms of age structure majority of students falls into young adults’ category, in which physical activity has only a small positive effect on the subjective perception of satisfaction. Majority of students exercised on average up to 50 minutes per week, which is less than weekly physical activity during regular face-to-face lessons of Physical Education.

Different lines of camelina (Camelina sativa (L.) Crantz) in broiler quails’ diets: effects on meat physicochemical traits and sensory profile

The present study investigated the effect of Camelina sativa cake dietary inclusion on quail meat fatty acids (FA), amino acids (AA), and sensory characteristics. To this, 480 broiler quails were allocated to four dietary treatments (12 replicated cages/treatment): a control diet (Control) and three diets with 15% camelina cake, containing a commercial cultivar (Calena) and two improved lines (Pearl: low linoleic acid; Alan: low glucosinolates). After slaughter, breast meat FA and AA profiles and contents were analysed, alongside a sensory evaluation by trained panellists. The dietary inclusion modified breast meat FA profile and contents (p < .001), notably increasing α-linolenic acid (C18:3 n-3), which improved overall n-3 PUFA and reduced the n-6/n-3 ratio to recommended levels. Health indices improved in a line-dependent manner: atherogenicity lowered in Alan compared to Control and Calena (p < .01), while thrombogenicity reduced in all camelina treatments than the Control (p < .001). Camelina-fed groups showed an AA profile in line with the Control one, and Pearl displayed a higher essential AA content compared to Alan (p < .05). Sensory results indicated no substantial changes in meat attributes across treatments, except for tenderness and animal fat flavour: the first lowered in Alan meat compared to the Control (p < .05), while the latter was higher in the Control than in Alan and Calena groups (p < .01). In conclusion, the 15% dietary inclusion of different camelina cakes in quail diets positively influenced meat FA, enhancing product healthiness without negatively impacting its nutritional quality and sensory attributes. Findings indicated that camelina cake is an effective feedstuff to improve quail meat quality. HIGHLIGHTS Once fed to quails, all tested camelina cakes increased meat n-3 PUFA and reduced the n-6/n-3 by about 5 and 7 times, respectively. Meat atherogenicity and thrombogenicity indices of camelina-fed quails improved, indicating enhanced product healthiness. Camelina inclusion did not alter quail meat sensory attributes compared to the control group.

INFLUENCE OF FLUORIDE ON PUBLIC HEALTH AND WORKER’S HEALTH – LITERATURE REVIEW

Dental caries is a multifactorial disease that affects a large part of the world's population. Can be prevented by various means and methods, including the use of fluoridated mouthwashes, topical fluoride, fluoridated toothpaste and fluoridation of public water supplies for human consumption. Fluoride has been shown to be a therapeutic agent that modulates dental caries. Frequent contact between patients and small concentrations of fluoride hinders demineralization and activates remineralization, in addition to inhibiting cariogenic microorganisms. The oral health of workers is directly related to the health of the population, since they are part of this context and suffer from the same problems that occur in the environment in which they live. Since workers are the agents who convert work activity into profits for the employer, a detailed and complete vision addressing prevention is extremely important, as this is a way to reduce absenteeism and keep workers in their jobs, achieving better performance of their activities. This bibliographic review aims to analyze fluoride in its various forms and evaluate its anti-cariogenic power for workers, with the aim of preventing cavities and providing well-being, quality of life and greater productivity to them. Based on the data analyzed, it is concluded that fluoride is an effective and inexpensive measure to prevent the appearance of cavities and that due to the large size of Brazil and its socioeconomic difficulties, fluoridated toothpaste associated with assisted brushing, with topical fluoride applications, prove to be the best measures to prevent cavities and absenteeism, consequently improving public health indices. This is one of the most relevant public policies, which is in fact underestimated and which can change the reality of Brazil, reducing high government costs and promoting the quality of life of the population, which suffers from oral problems, which can affect the entire organism, causing a series of other ailments, which should be avoided by adopting preventive measures on a permanent basis.

A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy.

To identify image biomarkers associated with overall life expectancy from low-dose computed tomography and integrate them as an index for assessing an individual's health. Two categories of CT image features, body composition tissues and cardiopulmonary vasculature characteristics, were quantified from LDCT scans in the Pittsburgh Lung Screening Study cohort(n = 3,635). Cox proportional-hazards models identified significant image features which were integrated with subject demographics to predict the subject's overall hazard. Subjects were stratified using composite model predictions and feature-specific risk stratification thresholds. The model's performance was validated extensively, including 5-fold cross-validation on PLuSS baseline, PLuSS follow-up examinations, and the National Lung Screening Trial (NLST). The composite model had significantly improved prognostic ability compared to the baseline model (p < 0.01) with AUCs of 0.774 (95% CI: 0.757-0.792) on PLuSS, 0.723 (95% CI: 0.703-0.744) on PLuSS follow-up, and 0.681 (95% CI: 0.651-0.710) on the NLST cohort. The identified high-risk stratum were several times more likely to die, with mortality rates of 79.34% on PLuSS, 76.47% on PLuSS follow-up, and 46.74% on NLST. Two cardiopulmonary structures (intrapulmonary artery vein ratio, intrapulmonary vein density) and two body composition tissues (SM density, bone density) identified high-risk patients. Body composition and pulmonary vasculatures are predictive of an individual's health risk; their integrations with subject demographics facilitate the assessment of an individual's overall health status or susceptibility to disease. CT-computed body composition and vasculature biomarkers provide improved prognostic value. The integration of CT biomarkers and patient demographic information improves subject risk stratification.

Heat Index Levels and Heat – Related Risks in the Philippines

Using a systematic literature review, this study determined and establish the Health Index Levels and Heat – Related Health Risks in the Philippines.  Background. The heat index which is also called as the visible temperature is well-defined as a personalized, subjective experience of a person when comparative humidity is correlated with the temperature of the air. The incidence and intensity of life-threatening incidents especially escalated heat index are growing all around the world which ultimately poses a serious risk to the health of people.  Aim. The aim of this review was to explore and deliberate the relationship between the heat index, a measure that combines air temperature and relative humidity to approximate the apparent temperature, and the risk of heat-related health issues in the Philippines from published articles between 2019 – 2024.  Data Sources. A systematic search of primary articles on numerous electronic databases that included Google Scholar and other related engines. Grey literature was also connected based on the references available and bibliographies from the different articles were also included in the pooling of literature of data.  Method. The article included two (2) or more of the key search terms identified: heat exhaustion, heat stroke, health related risks, heat index , and lastly published from years 2019-2024.  Results. Analysis of the research articles were used to answer the effect of the heat index in the risk of heat exhaustion, the effect of the heat index in the risk of stroke and formulation of research - based recommendations on Heat Index Levels and Heat - Related Health Risks in the Philippines.  Acknowledgment: The authors of this systematic literature review thanks and recognize all the writers of the variety of references that were used in this review paper. The proponents these data sources used in this paper are both cited in the text and in the reference lists.  Disclaimer: The beliefs and point of view manifested in this paper are those of the authors and do necessarily indicate the official guidelines or stand of any affiliated institution, organization or agency of the authors.  Conflict of Interest: The authors have not anything to proclaim. This paper is a collaborative effort of the authors mentioned above.

The association between age at menopause and bone health in Southwest China women: mediation effect of body mass index.

Previous studies have yielded inconsistent findings regarding the association between age at menopause and bone health, with limited exploration of potential mediating factors, particularly in the less-developed muti-ethnic regions of China. Our objective was to analyze the association between age at menopause and bone health among postmenopausal women in southwest China, while also examining the mediating effect of body mass index (BMI) and the moderating effect of years since menopause on this association. The analysis included a total of 15,352 naturally postmenopausal women obtained from the baseline data of the China Multi-Ethnic Cohort (CMEC) Study. Multiple linear regression was used for multivariate analysis. Mediation analysis was conducted to examine the mediating role of BMI in the association between age at menopause and bone health. A significant positive association was observed between age at menopause and bone health index (Quantitative ultrasound index, QUI). Specifically, with each year's delay in age at menopause, there was an increase of 0.260 (95% confidence interval (CI): 0.152-0.368) in QUI. Notably, women with later menopause (menopausal age ≥ 53 years) exhibited a higher QUI (β: 2.684, 95%CI: 1.503-3.865). Additionally, BMI partially mediated the relationship between age at menopause and QUI, accounting for 9.0% of the total effect, with an indirect effect coefficient β(95%CI) was 0.023(0.014, 0.032). Besides, it is worth mentioning that years since menopause moderated the association between age at menopause and bone health as well as the mediating effect of BMI. Naturally postmenopausal women with a later age at menopause demonstrate enhanced bone health. Maintaining a moderately high BMI, without progressing to overweight or obesity, may provide health benefits for postmenopausal women, especially for those with a longer duration since menopause.

SMOG CRISIS IN LAHORE: EVALUATING AIR QUALITY TRENDS AND PUBLIC HEALTH IMPLICATIONS

Seasonal smog renders Lahore, Pakistan, an environment legally with low-quality air and poor health for frequent cases. The smog includes such air pollutants as particulate matter, including PM2.5, PM10, carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2), which worsens respiratory and cardiovascular diseases. Air pollution trends in pre-smog season and during smog season and the related public health consequences of this phenomenon are examined in this research. The study compared the values of air quality parameters, as well as health data on the whole population in Lahore, in the period before smog and the period of maximum smog intensity. The participants consisted of 500 persons, and data about the air quality indicators, including PM2.5, PM10, CO, NO2, and SO2, was obtained. Imagery indices, self-reported symptoms, hospitalization, and cardiovascular events were examined. Hypothesis testing analyzing the perception of air quality and confirmed behavioral change were concerned with a survey regarding the usage of masks, awareness of air quality alerts, and frequency of outdoor trips. The data were analyzed and compared using t-tests with a test of significance set at 0.05 for all the tests done in this research. The results of the present study reveal that air quality parameters such as PM2.5, PM10, CO, NO2, and SO2 had enhanced values during the smog-burst period as compared to the pre-smog condition. The PM2.5 concentrations were raised from 60.52 µg/m³ to 180.34 µg/m³, and those of PM10 from 85.24 µg/m³ to 230.14 µg/m³. Other effects included improvements and worsening health indicators for persons suffering various ailments during peak smog. Hospital admission per 1000 people rose from 20.42±5.83 to 45.79±10.10 respiratory symptoms rose from 3.23±1.17 to 7.84±1.90 and cardiovascular incidents rose from 1.19±0.41 to 3.52±1.27. The number of asthma, bronchitis, and COPD cases also rose during the peak smog period. Asthma increased from 12.52% to 28.74%, bronchitis from 10.10% to 22.30%, and COPD from 4.93% to 12.12%. Knowledge about air quality alerts and wearing a mask during the smog season also changed respectively. The result demonstrated that Lahore recorded the maximum AQI and pollutants compared to other big cities in Pakistan such as Karachi, Islamabad, and Faisalabad. The findings presented in the paper show an alarming increase in air pollution and adverse effects on human health particularly during the period of smog in Lahore. People’s knowledge and changes in their behavior, including mask-wearing and reduced time spent outdoors, indicate that new and efficient preventive measures in public health and the environment should be developed to combat smog impacts on human health.