Effective Public Health Research Articles

Temperature-related mortality burden and projected change in 1368 European regions: a modelling study

Excessively high and low temperatures substantially affect human health. Climate change is expected to exacerbate heat-related morbidity and mortality, presenting unprecedented challenges to public health systems. Since localised assessments of temperature-related mortality risk are essential to formulate effective public health responses and adaptation strategies, we aimed to estimate the current and future temperature-related mortality risk under four climate change scenarios across all European regions. We modelled current and future mortality due to non-optimal temperatures across 1368 European regions, considering age-specific characteristics and local socioeconomic vulnerabilities. Overseas territories were excluded from the analysis. We applied a three-stage method to estimate temperature-related risk continuously across age and spatial dimensions. Age and city-specific exposure-response functions were obtained for a comprehensive list of 854 European cities from the Urban Audit dataset of Eurostat. Regional aggregates were calculated using an aggregation and extrapolation method that incorporates the risk incidence in neighbouring cities. Mortality was projected for present conditions observed in 1991-2020 and for four different levels of global warming (1·5°C, 2°C, 3°C, and 4°C increase) by regions, and subregions using an ensemble of 11 climate models produced by the Coordinated Regional Climate Downscaling Experiment-CMIP5 over Europe, and population projection data from EUROPOP2019. Our results highlight regional disparities in temperature-related mortality across Europe. Between 1991 and 2020, the number of cold-related deaths was 2·5 times higher in eastern Europe than western Europe, and heat-related deaths were 6 times higher in southern Europe than in northern Europe. During the same time period, there were a median of 363 809 cold-related deaths (empirical 95% CI 362 493-365 310) and 43 729 heat-related deaths (39 880-45 921), with a cold-to-heat-related death ratio of 8·3:1. Under current climate policies, aligned with 3°C increase in global warming, it is estimated that temperature-related deaths could increase by 54 974 additional deaths (24 112-80 676) by 2100, driven by rising heat-related deaths and an ageing population, resulting in a cold-to-heat-related death ratio of 2·6:1. Climate change is also expected to widen disparities in regional mortality, particularly impacting southern regions of Europe as a result of a marked increase in heat-related deaths. This study shows that regional disparities in temperature-related mortality risk in Europe are substantial and will continue to increase due to the effects of climate change and an ageing population. The data presented can assist policy makers and health authorities in mitigating increasing health inequalities by prioritising the protection of more susceptible areas and older population groups. We identify the projected areas of heightened risk (southern Europe), where policy intervention aimed at building adaptation and enhancing resilience should be prioritised. European Commission.

Development and Psychometric Validation of Population-Based Knowledge Attitudes and Practices-Questionnaire on Fluoride (PBKAP-QF).

Fluoride exposure is a global public health concern. Understanding the knowledge, attitudes, and practices (KAP) of affected populations is essential for effective community management. This study aimed to develop and validate a KAP questionnaire to assess fluoride and its risk in general population.An extensive literature review and focus group discussions were conducted to construct the questionnaire. Content validity was assessed using the Content Validity Index (CVI) based on expert feedback. Factor analysis was performed for final tool validation, and item characteristics were analyzed using IBM SPSS v. 27 and IBM AMOS v. 26.A total of 300 responses were collected. Initially, 41 items were included in the questionnaire, which were reduced to 25 after expert review. The final version included 19 items, with an I-CVI ranging from 0.80 to 1.00, indicating no issues with item difficulty or discrimination. Cronbach's alpha ranged from 0.88 to 0.90, demonstrating good internal consistency. The Kaiser-Meyer-Olkin (KMO) value was 0.848, and Bartlett's test (χ2 = 6860.978, df = 156, p < 0.01) confirmed data suitability for factor analysis. Three constructs were extracted with factor loadings greater than 0.5. Confirmatory factor analysis demonstrated a good model fit.This study developed and validated a robust 19-item KAP questionnaire for assessing knowledge, attitudes, and practices related to fluoride exposure. The tool demonstrated excellent reliability, validity, and internal consistency, supporting its use in guiding effective community-level management and public health interventions in fluoride-endemic areas.

The Pathophysiological Mechanism and Clinical Treatment of Polycystic Ovary Syndrome: A Molecular and Cellular Review of the Literature.

Polycystic ovary syndrome (PCOS) is a prevalent metabolic disorder among women of reproductive age, characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovaries. The pathogenesis of PCOS involves a complex interplay of genetic and environmental factors, including insulin resistance (IR) and resultant hyperinsulinemia. Insulin receptors, primarily in skeletal muscle, liver, and adipose tissue, activate downstream signaling pathways like PI3K-AKT and MAPK-ERK upon binding. These pathways regulate glucose uptake, storage, and lipid metabolism. Genome-wide association studies (GWASs) have identified several candidate genes related to steroidogenesis and insulin signaling. Environmental factors such as endocrine-disrupting chemicals and lifestyle choices also exacerbate PCOS traits. Other than lifestyle modification and surgical intervention, management strategies for PCOS can be achieved by using pharmacological treatments like antiandrogens, metformin, thiazolidinediones, aromatase inhibitor, and ovulation drugs to improve insulin sensitivity and ovulatory function, as well as combined oral contraceptives with or without cyproterone to resume menstrual regularity. Despite the complex pathophysiology and significant economic burden of PCOS, a comprehensive understanding of its molecular and cellular mechanisms is crucial for developing effective public health policies and treatment strategies. Nevertheless, many unknown aspects of PCOS, including detailed mechanisms of actions, along with the safety and effectiveness for the treatment, warrant further investigation.

Transmission chains and molecular characterizations of extended-spectrum β-lactamase producing Enterobacteriaceae at a veterinary hospital in Chengdu, China

The rapid emergence of Extended-spectrum β-lactamase producing Enterobacteriaceae (ESBL-E) is a major global public health concern. Previous studies have identified that intensive medical care of dogs and cats in veterinary hospitals have accelerated the infections and spread of ESBL-E. To investigate the spread of ESBL-E in a veterinary hospital, a total of 202 samples including hospitalized animals, veterinary healthcare workers and environment were collected from a veterinary hospital in Chengdu, China. ESBL-E were identified by antimicrobial susceptibility testing and 16 s rRNA sequencing and were further conducted on ESBL gene detection and multilocus sequence typing (MLST). At last, strains with transmission potential were analyzed by whole genome sequencing (WGS). Our results showed that the overall prevalence of ESBL-positive isolates was 34.7% (70/202), with 55.3% (26/47) in animals, 29.3% (12/41) in healthcare workers and 28.1% (32/114) in environment swabs. Twenty diverse MLST types were detected, with ST744, ST231 as the most prevalent ones. Transmission chains of two ESBL-E.coli (ST744 blaCTX-M-18, blaTEM-1) from cat_21 to cat_14, and two ESBL-Kp (ST231 blaCTX-M-27, blaTEM-1, blaSHV-1) from cat_20 to cat_37 were further confirmed by WGS. Furthermore, interdisciplinary investigation and cooperation of AMR are needed to better limit the transmissions of high-risk strains and to implement effective public health interventions.

Impact of Atmospheric Parameters on Human Respiratory Tract Infections (RTIs)

Respiratory tract infections (RTIs) are a significant public health issue globally, with varying degrees of severity and implications. Environmental factors, particularly atmospheric parameters, can exacerbate these health conditions. Understanding the relationship between atmospheric conditions and RTIs is crucial for effective public health interventions. This systematic literature review analyzes research on the impact of atmospheric parameters on RTIs through a systematic search across multiple electronic databases using different keyword combinations. The retrieved articles were categorized based on atmospheric parameters investigated, such as temperature, humidity, air pollution, and rainfall. The review findings indicate that various atmospheric factors, including wind speed, air pollution (PM10, PM2.5, CO, and O3), air temperature, atmospheric pressure, and humidity, influence the occurrence and severity of respiratory infections. Low wind speed and high air pollution were identified as risk factors for severe acute respiratory infection (SARI). In children, meteorological factors like air temperature, atmospheric pressure, rainfall, sunlight hours, wind speed, and relative humidity were associated with lower respiratory tract infections. Pollutants like PM2.5, carbon monoxide, sulfur dioxide, and tropospheric ozone directly impact pediatric respiratory infections. Climate variables affect the seasonal activity of pathogens like respiratory syncytial virus (RSV). These findings underscore the importance of considering atmospheric parameters in understanding and preventing RTIs. Addressing these challenges requires a comprehensive approach integrating environmental and public health interventions. This approach can better mitigate the effects of atmosphere-related risks on respiratory health.

The rural mortality penalty in U.S. hospital patients with COVID-19

BackgroundThe COVID-19 pandemic brought greater focus to the rural mortality penalty in the U.S., which describes the greater mortality rate in rural compared to urban areas. Although it is widely thought that issues such as access to care, age structure of the population, and differences in behavior are likely drivers of the rural mortality penalty, few studies have attempted to tie delayed access to care in rural populations to healthcare outcomes quantitatively. Therefore, it is critical to try and understand these factors to enable more effective public health policy.MethodsWe performed a cross-sectional analysis of a population of patients with COVID-19 who were admitted to hospitals in the United States between 3/1/2020 and 2/26/2023 to better understand factors leading to outcome disparities amongst groups that all had some level of access to hospital care. Nevertheless, it is widely thought that rural populations often experience delayed access to care, due to transportation and other constraints. Therefore, we hypothesized that deteriorated patient condition at admission likely explained some of the observed difference in mortality between rural and urban populations.ResultsOur results supported our hypothesis, showing that the rural mortality penalty persists in this population and that by multiple measures, rural patients were likely to be admitted in worse condition, had worse overall health, and were older.ConclusionsAlthough the pandemic threw the rural mortality penalty into sharp relief, it is important to remember that it existed prior to the pandemic and will continue to exist until effective interventions are implemented. This study demonstrates the critical need to address the underlying factors that resulted in rural-dwelling patients being admitted to the hospital in worse condition than their urban-dwelling counterparts during the COVID-19 pandemic, which likely affected other healthcare outcomes as well.

Prevalence of hypertension in pregnancy and its associated sociodemographic factors among mothers aged 15–49 years old in Malaysia

BackgroundHypertensive disorders of pregnancy (HDP) pose a substantial public health concern, ranking among the primary contributors to maternal and perinatal morbidity and mortality, impacting around 5–10% of pregnancies. This study aimed to determine the prevalence of HDP and its associated factors among mothers aged 15–49 who recently gave birth within the last two years, throughout Malaysia, informing effective public health and primary care interventions.MethodsThis study was a part of the national survey on maternal and child health (MCH) also known as the National Health and Morbidity Survey (NHMS) 2022: MCH. This was a cross-sectional study using two stage stratified random sampling design. Data of mothers aged 15–49 years old who recently gave birth within the last two years were selected in this study. This survey utilised a set of structured validated questionnaires administered via face-to-face interviews (using a mobile device). Multiple logistic regression analysis was employed to identify the associated factors for hypertension.ResultsAmong 6 335 participants recruited for this study with an estimated population of 782, 550, the prevalence of HDP among Malaysian mothers aged 15–49 years old who recently gave birth within the last two years was 6.5% (95% CI: 5.76, 7.37). Multiple logistic regression showed that maternal age and ethnicity were significantly associated with hypertension. Advanced maternal age had higher odds of hypertension, with an aOR of 2.18 (95% CI = 1.75, 2.71). In addition, Other Bumiputera had higher odds of hypertension (aOR = 2.71, 95% CI = 1.25, 5.87).ConclusionThis study reveals the prevalence of HDP among Malaysian women with children under 2 years old, emphasizing advanced maternal age (above 35) and ethnicity as notable risk factors. It improves understanding of the epidemiology of HDP in Malaysia, offering valuable insights for the development of effective public health strategies and clinical interventions that can help with the control of HDP.

Assessment of heavy metal(oid) pollution and related health risks in agricultural soils surrounding a coal gangue dump from an abandoned coal mine in Chongqing, Southwest China

The coal gangue dump may introduce heavy metal(oid)s (HMs) into surrounding agricultural soils, posing potential health risks to nearby communities. This study evaluated heavy metal(oid) pollution in agricultural soils adjacent to a gangue dump at an abandoned coal mine in Chongqing, Southwest China. The concentrations of HMs (As, Cd, Cr, Cu, Ni, Pb, and Zn) were quantified using ICP-MS, and the contamination status was assessed using the Geoaccumulation Index (Igeo), Contamination Factor (CF), Pollution Load Index (PLI), and Potential Ecological Risk Index (RI). Heavy metal(oid) contamination was detected in soils across a depth of 0–30 cm, particularly pronounced in the topsoil layer (0–10 cm and 10–20 cm depths). Cu emerged as the predominant contaminant across all examined depths, with average Igeo values of 1.20, 1.21, and 1.16 for the 0–10 cm, 10–20 cm, and 20–30 cm depths, respectively, indicating moderate contamination. The CF for Cu was 3.55, 3.55, and 3.50 for these respective depths, classifying it as considerable contamination. The PLI values ranged from 1.61 to 2.50, with a mean value of 2.12, indicating overall contamination. The ecological risk assessment indicated that the soil's ecological risk was low at all depths. Cd was the major contributor to the RI, accounting for 48%, 47%, and 42% at 0–10 cm, 10–20 cm, and 20–30 cm depths, respectively. Health risk assessments revealed significant non-carcinogenic risks to children (mean HI = 1.30) and unacceptable carcinogenic risks to both adults and children (mean TCR = 3.26 × 10–4 and 1.53 × 10–3, respectively). This study underscores the critical need for comprehensive risk assessments using multiple indicators to prioritize remediation efforts for HMs, providing a scientific basis for effective environmental management and public health protection in the Three Gorges Reservoir Area.

A Paper-based Loop-Mediated Isothermal Amplification (LAMP) Assay for Highly Pathogenic Avian Influenza.

Avian influenza outbreaks have had significant economic and public health consequences worldwide. Therefore, prompt, reliable, and cost-effective diagnostic devices are crucial for scrutinizing and confining highly pathogenic avian influenza viruses (HPAIVs). Our study introduced and evaluated a novel paper-based loop-mediated isothermal amplification (LAMP) test for diagnosing the H5 subtype of the avian influenza virus (AIV). We meticulously designed and screened LAMP primers targeting the H5-haemagglutinin (H5-HA) gene of AIV and fine-tuned the paper-based detection assay for best performance. The paper-based LAMP assay demonstrated a detection limit of 500 copies per reaction (25 copies/µL). This inexpensive, user-friendly point-of-need diagnostic tool holds great promise, especially in resource-limited settings. It only requires a water bath for incubation and enables visual detection of results without special equipment. Overall, the paper-based LAMP assay provides a promising method for rapidly and reliably detecting the H5 subtype of AIV, contributing to improved surveillance and early intervention strategies.

Development and evaluation of two educational sessions on take-home lead exposure prevention for construction workers and their families.

Take-home exposures occur when workers accidentally bring workplace contaminants home. Regular job responsibilities may expose construction workers to lead, which extends to their households via the take-home pathway. The present study aimed to develop and evaluate 2 educational sessions addressing take-home lead exposure tailored to construction workers and their families. Educational materials on take-home lead exposure and prevention strategies were designed following guidance from US government institutions and experts on construction work, lead exposure, and educational interventions. The educational materials were pilot-tested with construction workers and their family members during in-person or online sessions in English or Spanish. Changes in knowledge of take-home lead exposure were assessed through pre- and post-testing and open-ended feedback was collected from both participants and session facilitators. The study sample comprised 44 participants, including 33 workers and 11 family members. Among all participants, 81% were male, 46% were Hispanic or Latino, and the average age was 29 years. Post-test scores (µ = 93%, SD = 10%) were higher than pre-test scores (µ = 82%, SD = 19%), and younger participants (<30 years) were more likely to have a lower pre-test score compared to older participants (≥30 years). Overall, feedback from participants and facilitators was positive, indicating appropriate duration, appealing visuals, and ease of engagement through the training activities. Effective public health education for lead-exposed construction workers and their families is needed to reduce lead exposure disparities, especially among children of workers. Interventions must recognize that take-home exposures are not isolated to occupational or home environments.

Age-Related Blood Pressure Gradients Are Associated With Blood Pressure Control and Global Population Outcomes.

The strong relationship between blood pressure (BP) and age is well known. Limited evidence suggests that a steeper age-BP slope may be associated with an increased risk of adverse outcomes. The May Measurement Month campaign enables an investigation of geographic, socioeconomic, and sex differences in age-related BP gradients and their association with public-health outcomes. Cross-sectional, annual global BP May Measurement Month screening data were analyzed. Average systolic BP and age-related BP slopes across different age groups were calculated to assess regional, socioeconomic, and sex-stratified variations. The association of BP slopes derived from adjusted linear regression models with country-level health metrics was investigated. Age-related systolic BP gradients differed distinctly across global geographic regions, income levels, and between sexes. The steepest age gradients of BP were observed in populations from Africa and Europe. Women had lower BP levels than men at younger ages (20s and 30s) but subsequently experienced more pronounced age-related BP gradients. Geographically divergent age-related BP gradients were significantly associated with major national public health indicators. Globally, steeper age-related BP slopes were associated with poor BP control, increased disability-adjusted life years, and death rates. A steeper population age-BP slope of 1 mm Hg per 10 years was associated with a decrease in life expectancy of 3.3 years in this population (95% CI, -5.1 to -1.4; P=0.0007). Age-related BP gradients vary considerably across global populations and are associated with variability in BP-related risks and adverse outcomes across regions. Effective public health strategies may require region-specific targeting of adverse BP gradients to improve health outcomes.

Study protocol: perinatal mood treatment study

Perinatal depression (PND) affects up to 20% of women and is associated with significant impairment and disability in affected women. In addition, perinatal depression is associated with broader public health and multigenerational consequences. Innovative approaches are needed to reduce the burden of perinatal depression through identification, tracking, and treatment of depressive symptoms during the perinatal period. This study is a randomized clinical trial comparing the relative efficacy of a multi-tiered system of care, Screening and Treatment of Anxiety and Depression (STAND) to perinatal care delivered by a reproductive psychiatrist in reducing symptoms of depression and anxiety. A sample of 167 individuals was randomized between week 28 of pregnancy and 6 months postpartum. A secondary aim compares the original online therapy intervention used in the first half of the study to a newer online therapy program used in the second half of the study for individuals assigned to the STAND treatment. The study measures, intervention groups, and analysis methods are described, as well as expected implications. The findings from this study may improve the methods for tracking symptom changes over time, monitoring treatment response, and providing personalized care for individuals with PND. As such, this study may improve the lives of patients with PND and their families and lower the related health care costs to society.Trial registration NCT: 9/24/2021NCT direct link: https://www.clinicaltrials.gov/study/NCT05056454?term=NCT05056454&rank=1&a=1.

Urgent Response Needed: Addressing the Dengue Crisis in the Andean and Southern Cone Latin American Regions.

The dengue crisis in the Latin American region is currently intensifying, exacerbated by heavy rains, widespread flooding, and the onset of the El Niño-Southern Oscillation. The indirect consequences of the COVID-19 pandemic, which weakened healthcare systems, have further compounded the situation. Comparing the first 15 weeks of 2023 with the same period in 2024, we observed a significant average increase of 600% in the number of new cases. This translates to a 536% rise in the composite rate per 100,000 inhabitants across all countries. Brazil experienced a staggering surge from 1,425,000 cases in the initial 15 weeks of 2023 to 5,177,989 cases in the corresponding period of 2024. Similarly, Paraguay witnessed a notable escalation, with cases soaring from 12,497 in 2023 to more than 240,000 thus far in 2024, marking an increase of more than 1,825%. Bolivia, however, witnessed a reduction in cases, though the cause remains unclear. Urgent action is imperative to address this escalating crisis. Strengthening surveillance systems, enhancing vector control programs, and implementing effective public health campaigns are critical. Immediate and coordinated action by regional governments and health authorities is essential to mitigate the growing dengue crisis and safeguard public health in the region.

Assessing the Intention to Use a First-Generation Vaccine against COVID-19 Using Quantile Regression: A Cross-Sectional Study in Spain

Understanding the factors that influence the intention to use vaccines is crucial for implementing effective public health policies. This study examined the impact of various cognitive, affective, normative, and sociodemographic variables on the intention to be vaccinated against COVID-19 with the first-generation AstraZeneca vaccine. A survey of 600 residents of Spain was used to assess the influence and hierarchy of the drivers of the intention to vaccinate via least-squares and quantile regressions. The most significant factors were the perceptions of efficacy and social influence, both of which had positive impacts (p &lt; 0.0001). The positive influence of fear of COVID-19 and the negative influence of fear of the vaccine were also significant in shaping the central tendency toward vaccination. However, these fear-related variables, particularly the fear of COVID-19, lost importance in quantile adjustments outside the central tendency. Among the sociodemographic variables, only the negative impact of income was statistically significant. These results are valuable for the development of vaccination policies because they measure the sensitivity of attitudes toward vaccination to exogenous variables not only in the central values, as is common in similar studies, but also across the entire range of responses regarding the intention to vaccinate. This additional analysis, which is not commonly performed in studies on vaccine acceptance, allows us to distinguish between variables which are consistently related to the intention to vaccinate and those that influence only expected responses.

Forecasting infection fatality rate of COVID-19: measuring the efficiency of several hybrid models

The main goal of this paper is to delve into a crucial epidemiological metric the daily infection fatality rate in the context of the ongoing COVID-19 pandemic. The significance of understanding this metric lies in its potential to provide insights into the severity and impact of the virus on a daily basis. Methods: To achieve this overarching objective, we employ a comprehensive approach by applying various hybrid models that hybridize both machine learning and statistical techniques. In our pursuit of a deeper understanding, we leverage advanced machine learning algorithms, including Support Vector Machine and Random Forest. These techniques allow us to capture intricate patterns and relationships within the data, contributing to a more nuanced analysis of the infection fatality rate. The application of machine-learning models in epidemiological studies has gained prominence due to their ability to adapt to complex and evolving patterns inherent in infectious disease dynamics. Complementing our machine-learning arsenal, we integrate traditional statistical models such as ARIMA (AutoRegressive Integrated Moving Average), fractional ARIMA, and BATS (Bayesian Structural Time Series). Results. To assess the performance of these models, we employ key evaluation metrics, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). These metrics serve as critical benchmarks, allowing us to quantify the accuracy and reliability of our models in predicting the daily infection fatality rate. A meticulous evaluation of model performance is crucial for ensuring the validity and of our findings. According to these measures, we see that hybrid models performed well especially ARIMA-RF model RMSE: 0.29, MSE: 0.084, MAE: 0.215 for the horizon 60 and for horizon 120 ARIMA-RF still the best performance, RMSE: 0.268, MSE: 0.071, MAE: 0.183, we get these results due to the capacity of this approach to handle complex patterns contrarily to other model ARIMA, BATS, RF and SVM. Conclusion. This work adopted this approach in order to build a model to predict infection fatality rate, we aspire to provide a nuanced understanding of the factors influencing the severity of the virus, ultimately contributing to the ongoing discourse on effective public health interventions and mitigation strategies.

Urinary anatalline and nicotelline cut-points to distinguish between exclusive and dual use of tobacco products

Objective This study measured anatalline and nicotelline, two minor tobacco alkaloids, to discriminate between exclusive smokeless tobacco (SLT) use, exclusive electronic nicotine delivery systems (ENDS) use, exclusive cigarette use, dual SLT and cigarette use, and dual ENDS and cigarette use. Methods N = 664 urine samples from participants in the Population Assessment of Tobacco and Health Study were analyzed for anatalline and nicotelline. Geometric means and 95% confidence intervals were calculated for biomarker levels and their ratios. Non-parametric Receiver Operating Characteristic analyses were used to determine optimal cut-points of natural log-transformed biomarker ratios for distinguishing between tobacco use groups. Results The anatalline/nicotelline ratio distinguished exclusive cigarette from exclusive SLT use (threshold = 18.1, sensitivity = 89.3%, specificity = 86.4%, AUC = 0.90), and exclusive SLT from exclusive ENDS use (threshold = 12.8, sensitivity = 96.4%, specificity = 76.3%, AUC = 0.90) very well, but had reduced sensitivity and specificity when distinguishing exclusive cigarette from exclusive ENDS or any dual use with cigarettes. Conclusions This research fills a gap in understanding the public health consequences of SLT and ENDS use by providing objective measures that can signal use of these products alone or in combination with cigarettes.

Early and Rapid COVID-19 Diagnosis Using a Symptom-Based Machine Learning Model

The COVID-19 pandemic has resulted in a significant global health crisis, claiming over 6.3 million lives. Rapid and accurate detection of COVID-19 symptoms is essential for effective public health responses. This study utilizes machine learning algorithms to enhance the speed and accuracy of COVID-19 diagnosis based on symptom data. By employing the Spearman feature selection algorithm, we identified the most predictive features, thereby improving model performance and reducing the number of features required. The decision tree algorithm proved to be the most effective, achieving an accuracy of 98.57%, perfect sensitivity of 1, and high specificity of 0.97. Our results indicate that combining various symptoms with AI-based machine learning techniques can accurately detect COVID-19 patients. These findings surpass previous studies, demonstrating superior performance across multiple evaluations. The integration of feature selection with advanced machine learning models offers a practical and efficient tool for early COVID-19 diagnosis, improving patient management and public health responses. This approach holds significant promise for enhancing pandemic management and healthcare delivery.

Prevalence of human papilloma virus among cervical cancer patients in India: A systematic review and meta-analysis.

Cervical cancer, predominantly caused by the human papillomavirus (HPV), is a major health challenge in India, with high morbidity and mortality rates. Given India's vast geographic and socio-economic diversity, understanding regional variations in HPV prevalence is crucial for developing targeted and effective public health interventions. This systematic review and meta-analysis were conducted to elucidate the prevalence of HPV among cervical cancer patients in India. A literature search was executed across PubMed, EMBASE, and Web of Science up to December 07, 2023. Observational studies reporting HPV prevalence among cervical cancer patients in India are included. A Modified Newcastle-Ottawa scale was used for quality assessment. A random-effects meta-analysis was used to determine pooled HPV prevalence, and heterogeneity was evaluated using the I² statistic. Subgroup and sensitivity analyses were performed to assess result stability and investigate heterogeneity sources. All statistical analyses were performed using R software version 4.3. The meta-analysis included 17 studies with a total of 2529 cervical cancer cases, of which 1977 were HPV-positive. The pooled HPV prevalence was 85% (95% CI: 71-92%), with substantial heterogeneity (I² = 94%). Subgroup analysis by geographic zones showed notable differences: South (88%, 95% CI: 76-95%), North (73%, 95% CI: 1-100%), East (99%, 95% CI: 1-100%), Central (71%, 95% CI: 54-84%), and West (77%, 95% CI: 0-100%). Sensitivity analysis demonstrated the consistency of the results, and a reanalysis, excluding influential studies, yielded a prevalence of 82% (95% CI: 67-91%). Our analysis reveals a high prevalence of HPV in cervical cancer patients in India, with significant regional variations. The observed heterogeneity highlights the complexity of HPV epidemiology in India and necessitates further research to explore underlying causes and regional characteristics. Future studies should aim to expand geographic representation and deepen understanding of the factors contributing to the variability in HPV prevalence.

Effect of homophily on coupled behavior-disease dynamics near a tipping point

Understanding the interplay between social activities and disease dynamics is crucial for effective public health interventions. Recent studies using coupled behavior-disease models assumed homogeneous populations. However, heterogeneity in population, such as different social groups, cannot be ignored. In this study, we divided the population into social media users and non-users, and investigated the impact of homophily (the tendency for individuals to associate with others similar to themselves) and online events on disease dynamics. Our results reveal that homophily hinders the adoption of vaccinating strategies, hastening the approach to a tipping point after which the population converges to an endemic equilibrium with no vaccine uptake. Furthermore, we find that online events can significantly influence disease dynamics, with early discussions on social media platforms serving as an early warning signal of potential disease outbreaks. Our model provides insights into the mechanisms underlying these phenomena and underscores the importance of considering homophily in disease modeling and public health strategies.

Airborne pollen seasonality of Kars province, a high-altitude region in NE Anatolia-Turkey

Aeropalynological studies quantify pollen present in the atmosphere and elucidate their seasonal variations and regional distribution. Utilizing a Hirst-type volumetric 7-day pollen and spore trap during 2012 and 2013, we conducted an aerobiological investigation in Kars province, Turkey, a region characterized by high altitude and severe climate. Across the 2-year study period, an average annual pollen concentration of 14,931 pollen/m3 was recorded, with 39 taxa identified, including 29.31% woody taxa, 70.58% herbaceous, and 0.11% unidentified. The seven most dominant taxa were Poaceae, Pinaceae, Artemisia, Amaranthaceae, Urticaceae, Quercus, and Cupressaceae/Taxaceae. The data indicated a significant risk period for individuals with pollen allergies from June to July. Statistical analyses were performed to investigate the correlation between the daily pollen concentrations of the dominant pollen types and the variables. The results demonstrated that an increase in temperature was positively correlated with higher pollen counts, whereas increased average relative humidity was associated with reduced pollen concentrations. Notably, herbaceous pollen taxa were more prevalent in Kars. Additionally, the main pollen season starts approximately 6 weeks later than that at lower altitudes. These findings underscore the importance of localized aerobiological monitoring for effective public health responses to pollen allergies.