Assessment of factors related to poly cystic ovarian syndrome - A comparative and correlational study

Polycystic ovarian syndrome (PCOS) is a common endocrine disorder that primarily affects women of reproductive age. It is particularly prevalent among adolescent females who receive an insufficient diagnosis despite having potentially adverse consequences. The use of PCOS screening questionnaires has the potential to aid in the early detection of symptoms. The goal of this study is to observe if a self-administered questionnaire may be useful for a clear cognizance of the associated conditions like mental stress and menstrual characteristics correlated to polycystic ovary syndrome. In this study, we selected women within an age group of 17-40 with and without PCOS based on the modified Rotterdam criteria to fill out a self-administrated questionnaire based on the signs and symptoms of PCOS majorly focusing on mental stress and menstrual characteristics. SPSS software, univariate analyses were employed to elucidate the associations among the components of PCOS, demographic factors, and lifestyle characteristics, hence providing insights into the interrelationships among those variables. 64 women with PCOS and 141 women without PCOS participated in the present study. The present study revealed PCOS is greatly influenced by age at menarche (p-value= .043), typical cycle length (p-value = .000) mental health problems during menstruation (p-value = .032), and body mass index (p-value = .001). Multivariate hierarchical logistic regression analysis showed only 2 variables BMI (a-OR 1.156,95% CI (1.067-1.242), p-value = .000), and typical cycle length (a-OR 2.278, 95% CI (1.079-4.809), p-value = .003) were significant. The present study showed that BMI and menstrual cycle length were most closely associated with the incidence of PCOS, which is important in diagnosing and treating the condition. Considering the high incidence of PCOS among women of reproductive age and its potential for significant health implications, it would be prudent to incorporate inquiries regarding mental health concerns and menstrual patterns into routine medical assessments for this demographic analysis. This approach aims to ascertain whether additional diagnostic evaluations and screenings for PCOS are warranted.

Predictors of divorce and duration of marriage among first marriage women in Dejne administrative town

Divorce is a common occurrence in the marital lives of spouses. Consequently, numerous divorced spouses and their children face various social, economic, physiological, and health problems after breaking their marriage. This study aimed to identify the predictors of divorce and the duration of marriage. We conducted a community-based cross-sectional study among 423 randomly selected residents of Dejen Township in April 2020, of which only 369 respondents met the study inclusion criteria. We used structured questionnaires to collect data. The predictors of divorce and duration of marriage were analyzed using binary logistic regression and the Gompertz regression model, respectively. A p value less than 0.05 was used to express statistical significance. The prevalence of divorce was 21.14% [95% CI (19.01-23.27%)]. Half of these women broke up their marriage after 11 years. A high age difference (7 or more years) between spouses, an early marriage, infertility among women, the presence of third parties, women without formal education, women in the workforce, sexually dissatisfied women, women who did not live together with their husbands at the same address, partner violence, marital control behaviour of husbands, drug-abused husbands, spouses without children, and women who knew multiple sexual partners were the significant predictors of divorce. Partner violence, sexually dissatisfied women, women who made their own marriage decisions, marital control behaviour of husbands, women who did not live together with their husbands at the same address, drug-abused husbands and spouses without children were significant predictors of shorter marriage durations. In this study, the prevalence of divorce was high. Therefore, a community-based, integrated strategy is needed to minimize the divorce rate.

Forest fire vulnerability in Nepal's chure region: Investigating the influencing factors using generalized linear model

The Chure region, among the world's youngest mountains, stands out as highly susceptible to natural calamities, particularly forest fires. The region has consistently experienced forest fire incidents, resulting in the degradation of valuable natural and anthropogenic resources. Despite its vulnerability, there have been limited studies to understand the relationship of various causative factors for the recurring fire problem. Hence, to comprehend the influencing factors for the recurring forest fire problem and its extent, we utilized generalized linear modeling under binary logistic regression to combine the dependent variable of satellite detected fire points and various independent variables. We conducted a variance inflation factor (VIF) test and correlation matrix to identify the 14 suitable variables for the study. The analysis revealed that forest fires occurred mostly during the three pre-monsoon periods and had a significant positive relation with the area under forest, rangeland, bare-grounds, and Normalized Difference Vegetation Index (NDVI) (P < 0.05). Consequently, our model showed that the probability of fire incidents decreases with elevation, precipitation, and population density (P < 0.05). Among the significant variables, the forest areas emerges as the most influencing factor, followed by precipitation, elevation, area of rangeland, population density, NDVI, and the area of bare ground. The validation of the model was done through the area under the curve (AUC = 0.92) and accuracy (ACC = 0.89) assessments, which showed the model performed excellently in terms of predictive capabilities. The modeling result and the forest fire susceptible map provide valuable insights into the forest fire vulnerability in the region, offering baseline information about forest fires that will be helpful for line agencies to prepare management strategies to further prevent the deterioration of the region.

Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure-activity relationship models

Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERβ dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERβ agonists among OH-PCB congeners.

Setting nutrient boundaries to protect aquatic communities: The importance of comparing observed and predicted classifications using measures derived from a confusion matrix

Defining nutrient thresholds that protect and support the ecological integrity of aquatic ecosystems is a fundamental step in maintaining their natural biodiversity and preserving their resilience. With increasing catchment pressures and climate change, it is more important than ever to develop clear methods to establish thresholds for status classification and management of waters. This must often be achieved using complex data and should be robust to interference from additional pressures as well as ameliorating or confounding conditions. We use both artificial and real data to examine challenges in setting nutrient thresholds in unbalanced and skewed data. We found significant advantages to using binary logistic regression over other techniques. However, one of the key challenges is objectively selecting a probability from which to derive the nutrient threshold. For this purpose, the examination of the proportions of matching and mismatching status classifications of nutrients and a biological quality element using a confusion matrix is a key step that should be more widely adopted in threshold selection. We examined a large array of statistical measures of classification accuracy and their performance over combinations of skewness and imbalance in the data. The most appropriate threshold probability is a compromise between maximising overall classification accuracy and reducing mismatches expressed as commission (false positives) without excessive omission (false negatives). An application to a lake type indicated total phosphorus thresholds that would be around 50 μg l-1 lower than the threshold achieved by an 'unguided' approach, indicating that this approach is a very significant development meriting attention from national authorities responsible for water management.

Application of geospatial techniques and binary logistic regression model for analyzing driving factors of urban growth in Bahir Dar city, Ethiopia

Understanding the drivers of urban growth and spatiotemporal land use change is important for rational land use and sustainable urban development. Based on the land use data, GIS data of explanatory variables, experts' knowledge and field observation, the study used a binary logistic regression model (BLRM) to analyze factors that drive rapid urban growth in Bahir Dar city, Ethiopia, using the LOGISTICREG module in IDRISI Selva software. Nine factors were used to reflect the influence of proximity and physical factors on urban growth from 1984 to 2019. This model helped in quantifying and identifying the factors of urban growth, which includes topography (slope, elevation and aspect) and accessibility (Dis. to the main road, Dis. to international airport, Dis. to CBD, Dis. to existing built-up area, Dis. to forest land and Dis. to water body). Furthermore, urban growth probability maps were created based on LRM results, revealing that the biggest urban growth would occur around existing built-up areas along the main roads and near Bahir Dar international airport. The Relative Operating Characteristic (ROC) values of 0.85, 0.90 and 0.93 and PCP values of 96.72 %, 98.46 % and 98.51 % indicate the urban growth probability maps are valid and BLRM had an ideal ability to predict urban growth. So, the study highlighted the relation between urban growth and its drivers in Bahir Dar, giving a decision making framework for better land use management and resource allocation.

Sex-specific association between carotid atherosclerosis and fundus arteriosclerosis in a Chinese population: a retrospective cross-sectional study

OBJECTIVES

Vascular stiffening is highly predictive of major adverse cardiovascular events. It is not clear whether microangiopathy, such as fundus arteriosclerosis, is related to carotid atherosclerosis. Hence, this study was designed to investigate the relationship between carotid atherosclerosis and fundus arteriosclerosis among individuals of different sexes in the Chinese health-examination population.

METHODS

This retrospective cross-sectional study involved 20,836 participants, including 13050 males and 7786 females. All participants underwent a detailed health examination, including medical history assessment, physical examination, assessment of lifestyle factors, fundus photography, Doppler ultrasound examination of the neck, and laboratory examinations. Two trained ophthalmologists analysed fundus arteriosclerosis based on fundus photographs, while carotid atherosclerosis was diagnosed using colour Doppler sonography of the neck. Binary logistic regression was used to analyse the relationship between carotid atherosclerosis and fundus arteriosclerosis.

RESULTS

In participants with fundus arteriosclerosis, the incidence of carotid atherosclerosis was higher than that of participants without fundus arteriosclerosis (52.94% vs. 47.06%). After adjustments for potential confounding factors, fundus arteriosclerosis was significantly associated with the risk of carotid atherosclerosis. The OR with 95% CI for fundus arteriosclerosis was 1.17 (1.02, 1.34) with p = 0.0262, and individuals who did not have fundus arteriosclerosis were used as a reference in the total population. Fundus arteriosclerosis was associated with the incidence of carotid atherosclerosis in males (p = 0.0005) but not in females (p = 0.0746).

CONCLUSIONS

Fundus arteriosclerosis was closely associated with carotid atherosclerosis in the Chinese population. This association was found in males but not in females.

Analysis of first responder-involved traffic incidents by mining news reports

Roadside service and incident response personnel face the risk of being killed or severely injured by passing vehicles when performing their duties on or along a road. This study investigated 5,113 responder-involved event news reports to understand the characteristics of first responder-involved incidents. Through text mining, this study examined and compared the characteristics of three types of responder-involved incidents: near-miss incidents, struck-by incidents, and line-of-duty-deaths (LODD). A higher proportion of struck-by and LODD incidents are associated with law enforcement agencies. In terms of the time of day, morning and night incidents are frequently reported in the news. Driving under the influence (DUI) or driving while intoxicated (DWI) is a major cause of LODD incidents. Compared to struck-by incidents, LODD incidents have a larger portion related to out-of-control vehicles. Further, this study built a logistic regression model to relate the incident characteristics to the odds of an incident being a LODD incident. The modeling result shows that tow truck drivers are associated with a greater likelihood of being involved in a news-reported LODD incident than other responders. LODD incidents are more likely to occur on early morning. Compare to entering/leaving/staying at the scene, responders are more likely to be involved in LODD event when assisting. The results offer insights into understanding the characteristics and possible reasons for first responder-involved incidents so that potential countermeasures could be developed to improve responder safety.

Evaluating the ergonomic deficiencies in computer workstations and investigating their correlation with reported musculoskeletal disorders and visual symptoms among computer users in Bangladeshi university

Nowadays, computer users are facing musculoskeletal disorders (MSDs) and visual symptoms. Prolonged sitting in inappropriate, awkward, and static postures on the computer workstation may cause musculoskeletal disorders (MSDs). Similarly, inappropriate placement of monitors, illumination, and other factors such as prolonged usage of computers are related to visual symptoms.

OBJECTIVE

This study aims to evaluate the ergonomic deficiencies of computer workstations and their correlation with MSDs and visual symptoms.

METHODS

This study involved 271 university employees from a Bangladeshi engineering university. Ergonomic deficiencies were evaluated through direct observations and Occupational Safety and Health Administration checklists. In addition, the Nordic Musculoskeletal Questionnaire was used to assess the prevalence of MSDs and visual discomforts. Binary Logistic Regression (BLR) analysis was also used to examine the correlation between musculoskeletal symptoms and ergonomic deficiencies.

RESULTS

Results showed serious deficiencies in workstation setup, seating arrangement, monitor orientations, keyboard orientations, other input device orientations, and accessory setup. Employees reported that the MSDs in different body regions during the last 12 months including lower back (62 %), upper back (53 %), shoulders (47 %), and neck (25 %). Moreover, itchy eyes (69 %), tired eyes (83 %), and unclear vision (56.83 %) were the most common visual discomforts or visual symptoms among the participants. Results also revealed that monitor ergonomics and its orientation deficits were significantly associated with visual discomforts. Gender, job type, age, BMI, work experience, duration of computer work, and beak taking after 2 h were the independent variables reliably predicting the MSDs and visual symptoms.

CONCLUSION

It is evident that MSDs and visual symptoms were associated with computer workstation deficiencies and other work-related factors.

Serum amyloid A as a biomarker for immunoglobulin resistance in Kawasaki disease

BACKGROUND

Intravenous immunoglobulin (IVIG) resistance is of prime importance in Kawasaki disease (KD). In this study, we examined the value and mechanism of serum amyloid A (SAA) level in predicting IVIG resistance in patients with KD.

METHODS

SAA levels were measured in 497 consecutive patients with KD before IVIG therapy in the training set. The patients were divided into two groups (IVIG-responsive and IVIG-resistant) according to the American Heart Association (AHA) definition of IVIG resistance. Demographic, echocardiographic, and laboratory data were also retrospectively analyzed and tabulated to predict IVIG resistance. The predictive value of SAA was validated on test sets of prospective data. Cytokine microarrays were analyzed from 4 patients with resistant to IVIG, 4 patients with responsive to IVIG and 4 healthy volunteers.

RESULTS

During the training set, 409 patients with KD were enrolled, of whom 43 (10.5%) were resistant to initial IVIG treatment and 47 (11.49%) had coronary artery lesions (CALs). Serum levels of SAA were higher in the IVIG resistant group compared to the IVIG responsive group, (380.00 [204.40-547.25] vs 230.85 [105.40-490.00] mg/L; p = .008). The values of total bilirubin, C-reactive protein, neutrophils, alanine aminotransferase, aspartate aminotransferase, interleukin-6(IL-6), and procalcitonin were significantly higher in the IVIG-resistant group than in the IVIG-responsive group (p < .05); however, the lymphocytes, platelets, serum sodium levels, and duration of fever before IVIG therapy were significantly lower (p < .05). There was no significant difference in SAA levels between patients with KD with and without CALs. Binary logistic regression analysis showed that SAA (p = .008), neutrophils (p < .001), total bilirubin (p = .001), platelet count (p = .004), and serum sodium level (p = .019) were independent factors influencing IVIG resistance. The optimal cutoff value of SAA for IVIG resistance prediction was 252.45 mg/L, with a corresponding clinical sensitivity of 69.8% and specificity of 54.4%. Based on receiver operating characteristic (ROC) curve analyses, the area under the curve (AUC) of combined detection with these five indicators was 0.800, clinical sensitivity was 69.8%, and specificity was 76.2%. In the prospective data, the sensitivity, specificity, and accuracy of SAA for identifying IVIG resistance KD were 77.8%,69.0%, and 70.0%, respectively. Compared with IVIG- responsive group and healthy children, the levels of IL-6 was upregulated significantly in IVIG-resistant group through cytokine microarrays.

CONCLUSIONS

SAA may be a potential biomarker for predicting IVIG responsiveness to KD, Combined detection of SAA levels, total bilirubin, neutrophil count, platelet count, and serum sodium levels is superior to that of any other single indicator for predicting IVIG resistance in KD. And elevated SAA may accompany with IL-6 in KD patients, its use in clinical practice may be helpful for treatment management.

The integrated analysis of primary and secondary incident characteristics: Focusing on the impact and scope of the safety service patrol program in Iowa

Secondary incidents are considered a major risk in terms of traffic management due to dangerous ramifications, such as reduced capacity, additional traffic delays, and serious injuries. Therefore, it is necessary to examine what causes a secondary incident to occur after a primary incident and prepare countermeasures to reduce the possible damage to human and property from primary and secondary incidents. In Iowa, a safety service patrol program is being implemented on major highway routes to respond to both types of incident efficiently. However, research on when, where, and under what conditions these incidents occur and how the program can deal with incidents must be conducted to determine the major characteristics of primary and secondary incidents and to estimate the program's performance. Consequently, statistical and spatial analyzes were performed on traffic incidents in a 5-year period (2016-2020) in Iowa. A survival analysis confirmed that the program could decrease the probability of secondary incident occurrences, and 99.9% of secondary incidents occurred within 4 h of the primary incident. Additionally, the binary logistic regression analysis of primary incidents affirmed that a longer incident clearance time and a higher severity of incidents could increase the probability of secondary incidents occurrence. Furthermore, a spatial analysis evaluated that the Iowa DOT safety service patrol program adequately covered areas where primary and secondary incidents are focused. This study is expected to be used to develop countermeasures in both incident cases by identifying the characteristics of secondary and primary incidents in Iowa.

Climate change and livestock herders wellbeing in Pakistan: Does nexus of risk perception, adaptation and their drivers matter?

Rural people, particularly in developing nations, rely on livestock as a key source of income. In Pakistan, rural people depend profoundly on buffalo, cows, sheep, and goats to earn their livelihood. The systems of agricultural production are at risk because of the negative effects of climate change. It badly affects production and quality of milk and meat, animal health, productivity, breeding, feed, and rangelands of livestock production. Climate change risks assessment and adaptation are required to minimize losses from these effects, which are not just technical but also socioeconomically significant. Hence, based on data collected from 1080 livestock herders using a multistage sampling technique in Punjab, Pakistan this study aims to assess perceived impact of climate change on livestock production and to assess coping strategies. In addition, determinants of adaptation strategies and their effects on livestock production was also estimated. Binary Logistic Regression was used to identify the drivers of adaptation strategies. In addition, Multi Group Analysis (MGA) in Partial Least Squares Path Modelling (PLS-PM) was applied to compare adapter and non-adapter of climate change adaptation strategies. Findings indicated that there are spread of various diseases to livestock due to adverse effects of climatic variability. There was reduction in the availability of the livestock's feed. Moreover, competition of water and land resources of livestock was also increasing. Low production efficiency resulted in decline of milk yield and meat production. Likewise, mortality of livestock, increased in still births, reduction in reproductive performance, decline in animal fertility, longevity, and general fitness, decreased birthing rates, rises in age at foremost calving in beef cattle was also prevailing. There were different adaptation policies used by farmers to handle with climate change and these were influenced by several demographic, socioeconomic, and agronomic aspects. Findings indicated that nexus of risk perception, adaptation plans and their determinants are beneficial to reduce the consequences of climatic variability and it improve the wellbeing of the herders. Risk management system may be created to protect livestock against losses caused by extreme weather events by providing awareness regarding influence of climate change on livestock. Easy and cheaper credit should be provided to the farmers to manage with the vulnerabilities of climate change.

Sex estimation of Malaysian sub-adults using craniometrics: A computed tomography study

Sex estimation is crucial in biological profiling of skeletal human remains. Methods used for sex estimation in adults are less effective for sub-adults due to varied cranium patterns during the growth period. Hence, this study aimed to develop a sex estimation model for Malaysian sub-adults using craniometric measurements obtained through multi-slice computed tomography (MSCT). A total of 521 cranial MSCT dataset of sub-adult Malaysians (279 males, 242 females; 0-20 years old) were collected. Mimics software version 21.0 (Materialise, Leuven, Belgium) was used to construct three-dimensional (3D) models. A plane-to-plane (PTP) protocol was utilised to measure 14 selected craniometric parameters. Discriminant function analysis (DFA) and binary logistic regression (BLR) were used to statistically analyze the data. In this study, low level of sexual dimorphism was observed in cranium below 6 years old. The level was then increased with age. For sample validation data, the accuracy of DFA and BLR in estimating sex improved with age from 61.6% to 90.3%. All age groups except 0-2 and 3-6 showed high accuracy percentage (≥75%) when tested using DFA and BLR. DFA and BLR can be utilised to estimate sex for Malaysian sub-adult using MSCT craniometric measurements. However, BLR showed higher accuracy than DFA in sex estimation of sub-adults.

Predicting malignancy in thyroid nodules based on conventional ultrasound and elastography: the value of predictive models in a multi-center study

BACKGROUND

This study aimed to establish predictive models based on features of Conventional Ultrasound (CUS) and elastography in a multi-center study to determine appropriate preoperative diagnosis of malignancy in thyroid nodules with different risk stratification based on 2017 Thyroid Imaging Reporting and Data System by the American College of Radiology (ACR TI-RADS) guidelines.

METHODS

Five hundred forty-eight thyroid nodules from three centers pathologically confirmed by the cytology or histology were retrospectively enrolled in the study, which were examined by CUS and elastography before fine needle aspiration (FNA) and surgery. Characteristics of CUS of thyroid nodules were reviewed according to 2017 ACR TI-RADS. Binary logistic regression analysis was used to develop the prediction models based on the different risk stratification of CUS features and elastography which were statistically significant. Values of predictive models were evaluated regarding the discrimination and calibration.

RESULTS

Binary logistic regression showed that patients' age, taller-than-wider, lobulated or irregular boundary, extra-thyroid extension, microcalcification and the elastic parameter of Virtual touch tissue imaging quantification (VTIQ) max were independent predictors for thyroid malignancy (p < 0.05) in the ACR model and showed the area under the curve (AUC) in training (0.912) and validation cohort (internal and external: 0.877 vs 0.935). Predictive models showed predictors in ACR TR4 and TR5 for malignancy and diagnostic performance of AUC in training, internal and external validation cohort respectively: the VTIQ max (p < 0.001) with AUC of 0.809 vs 0.842 vs 0.705 and the age, taller than wide, VTIQ max variables with AUC of 0.859 vs 0.830 vs 0.906 in validation cohort. All predictive models have better calibration capabilities (p > 0.05).

CONCLUSIONS

Predictive models combined CUS and elastography features would aid clinicians to make appropriate preoperative diagnosis of thyroid nodules among different risk stratification. The elastography parameter of VTIQ max has the priority in distinguishing thyroid malignancy with moderately suspicious (ACR TR4).

Identifying the influencing factors and constructing incentive pattern of residents' waste classification behavior using PCA-logistic regression

With the acceleration of urbanization, domestic waste has become one of the most inevitable factors threatening the environment and human health. Waste classification is of great significance and value for improving urban environmental quality and promoting human well-being. Based on the theory of planned behavior, we added external and socio-economic factors to systematically examine how they affect residents' waste classification behavior (WCB). We collected 661 valid data through a questionnaire survey conducted in Jinan, a pilot city for waste classification in China. Key driving factors were identified by combining binary logistic regression and the principal component analysis. The results showed that the elderly, women, and people with higher education are more likely to participate in waste classification. Attitude, collaborative governance, and institutional pressure positively affect WCB, while subjective norm and infrastructure have a negative effect. Knowledge mastery and degree of publicity are positively and significantly related to WCB, but other perceived behavioral control sub-variables negatively affect WCB. Based on the results and status of waste classification in Jinan, we propose the multi-agent linkage governance pattern from various dimensions to explore a powerful guiding incentive that can enhance WCB and provide a reference for waste management policymakers.

Mental Health Service Urgency in Children's Mental Health: Factors Impacting the Need for Expedited Services

Delayed access to mental health services for children and adolescents has been linked to an increased risk of harm and nonattendance to scheduled appointments. While studies suggest that the lack of standardized assessments for prioritizing individuals has contributed to long wait times, the inconsistent use of assessments across service sectors in Ontario continues to persist. This has contributed to a paucity of information surrounding which children and adolescents may require urgent mental health services. Using a large secondary data set, this study examined whether service sector (e.g., school), and other individual client characteristics (e.g., age, sex, legal guardianship, interpersonal and school conflict) predicted greater mental health service urgency in 61,448 children and adolescents assessed using the interRAI Child and Youth Mental Health Screener. Binary logistic regression revealed that all predictors, except for sector, showed a significant effect on service urgency. Findings are instrumental in prioritization, reducing the likelihood that children with acute needs remain on waitlists.

Environmental factors and spatiotemporal distribution characteristics of the global outbreaks of the highly pathogenic avian influenza H5N1

The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.

Identification of active or inactive agonists of tumor suppressor protein based on Tox21 library

Exposure of cells to xenobiotic human-made products can lead to genotoxicity and cause DNA damage. It is an urgent need to quickly identify the chemicals that cause DNA damage, and their toxicity should be predicted. In this study, recursive partitioning (RP), binary logistic regression, and one machine learning approach, namely, random forest (RF) classifier, were used to predict the active and inactive compounds of a total 5036 data based on the assay conducted by a β-lactamase reporter gene under control of the p53 response element (p53RE) from Tox21 library. Results show that the binary logistic regression model with a threshold of 0.5 has a high accuracy rate (83%) to distinguish active and inactive compounds. The RF classifier method has satisfactory results, with an accuracy rate (84.38%) approximately higher than that of binary logistic regression. The models established can identify compounds that induce DNA damage and activate p53, and provide a scientific basis for the risk assessment of organic chemicals in the environment.

Medicine before and after David Cox

Herein we recount the legacy of Sir David Roxbee Cox (15 July 1924 - 18 January 2022) from the perspective of practicing clinicians. His-pioneering work in developing the logistic and Cox proportional hazard regression models revolutionized the analysis and interpretation of categorical and time-to-event survival outcomes in modern medicine. This legacy is an inspiration for all those who follow on Sir David Cox's path.

Derivation of "Egyptian varices prediction (EVP) index": A novel noninvasive index for diagnosing esophageal varices in HCV Patients

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Esophageal Varices is one complication of chronic liver disease that leads to deaths globally due to hemorrhage.

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The prediction of presence the Esophageal Varices is essential to avoid bleeding for patients.

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Now the only diagnostic method for Esophageal Varices by the upper gastrointestinal endoscopy but it has many disadvantages.

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Only ten variables are the most significant for diagnosing the varices: PLT, Stiffness, PC, liver texture, spleen, HCV-RNA, Albumin, gender, Total bilirubin, and PV diameter.

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We Evaluated the effectiveness of several noninvasive markers for predicting Varices.

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We Introduced a novel (EVP) index with acceptable performance for diagnosing Varices and compared with the exist, it could save operating the upper endoscopic by nearly 46.5%.

Introduction

Esophageal Varices (EVs) is one of the major dangerous complications of liver fibrosis. Upper Gastrointestinal (UGI) Endoscopy is necessary for its diagnosis. Repeated examinations for EVs screening severely burden endoscopic units in terms of cost and other side implications; moreover, the lack of public health resources in rural areas and primary hospitals should be considered, particularly in developing countries. So, an accurate noninvasive marker for EV is highly needed for liver disease patients.

Objectives

This study sought to evaluate the values of several indices to determine how adequate are they in predicting EV and build a novel accurate prediction index.

Methods

Five thousand and thirteen patients were enrolled. The laboratory tests, abdominal ultrasonography, liver stiffness measurement using Fibro-scan, and UGI endoscopy were performed. Ten common indices: Fib-4 score, AST-to-platelet ratio index, Fibrosis index, AST/ALT ratio Varices Prediction Rule, Baveno VI, APRI-Fib4 Combo, King score, “Model for End-Stage Liver Disease”, and Lok Score were calculated. The significant predictors for EVs were identified by using “P-value Correlation-based Filter Selection” method, where a novel Egyptian Varices Prediction (EVP) index was developed using binary logistic regression. The diagnostic performance was evaluated by some parameters and the Area Under Curve (AUC).

Results

EVP Index was correlated to EVs at 0.5; it achieved higher performance (AUC 0.788, accuracy 73.3%, and sensitivity 78%) than the other indices at a cutoff point of 0.423.

Conclusion

EVP Index was a good noninvasive predictor. It had an acceptable performance for diagnosing EVs and it was only required regular laboratory tests and imaging data. It can provide a tool for classifying or arranging the patients according to the degree pre-emptive for selective endoscopy and the degree of severity. Also, it will enable clinicians to concentrate on one marker instead of a wide set of parameters.

Health impact assessment of auto rickshaw and cab drivers due to exposure to vehicular pollution in Delhi: an integrated approach

Vehicular emission is an important contributor to air pollution in the urban environment and impacts the health of commuters as well as drivers. The in-vehicle concentration of pollutants is known to be higher than the ambient environment and varies with the mode of transport. Thus, this study attempts to assess the health impacts of air pollution exposure on auto rickshaws and cab drivers. The study was conducted in Delhi using a triangular approach involving a health perception survey, lung function test and in-vehicle monitoring of particulate matter (PM₁, PM_2.5, PM₁₀) concentration to assess the health impacts of air pollution on auto rickshaw and cab drivers. A total of 150 respondents (75 from each occupation) were surveyed, and spirometry was performed for 40 respondents (20 from each occupation). Binary logistic regression showed auto rickshaw drivers were exposed to significantly higher in-vehicle PM concentrations in summers and winters and, thus, had a significantly higher risk of developing respiratory, ophthalmic and dermatological health symptoms (p< 0.05 and relative risk >1). Pulmonary function test showed obstructive lung impairment was reported only among auto rickshaw drivers (6%) and restrictive lung impairment was also more prevalent among auto rickshaw drivers (48%) than cab drivers (33%), suggesting a greater vulnerability of auto rickshaw drivers to respiratory health issues. Lung function impairment was associated with age (p= 0.002). The health and well-being of individuals is a matter of global concern, also highlighted in sustainable development goal no. 3. However, it was observed that neither auto rickshaw drivers nor cab drivers used formal/standard protective measures mainly due to unawareness or unaffordability. The study suggests increasing awareness and formulating guidelines to highlight the use of proper protective measures by these vulnerable groups and specific policy measures to protect outdoor workers like auto rickshaw drivers.

Perception and adaptation of pastoralists to climate variability and change in Morocco's arid rangelands

Since the late 1970s, extensive livestock production in the high plateaus of Eastern Morocco, particularly of small ruminants, has been seriously threatened by climate change (CC). Negative impacts include reduction in rangeland forage production and water availability, increased poverty and inequality, and increased degradation of rangelands. Different categories of pastoralists have adopted different combinations of adaptation strategies, but the factors influencing adoption have not to date been investigated. This paper aims to identify the perceptions of pastoralists on CC, to analyze the adaptive responses of different wealth categories, and to determine the factors affecting the adoption of adaptation measures. The Mann-Kendall, Pettitt and Buishand tests and the standardized precipitation index were used to analyze the climate data. Data on adaptation were examined using the chi-square homogeneity test, Kruskal-Wallis test and binary logistic regression.

The observed climate trends perfectly corroborated pastoralists' perceptions of significant changes in their local climate since the 1970s: a considerable decrease in annual rainfall and an increase in temperature and frequency of droughts and high winds. There were significant differences (Chi square = 7.603, p = 0.022, df = 2) between small, medium and large pastoralists in the frequency adoption of adaptation strategies, especially between small and large pastoralists (U statistic = 16.000, p = 0.009). The distribution of most adaptation actions also differed significantly between these two groups. Wealthier pastoralists have adopted a greater range of strategies, while poorer pastoralists have less diverse adaptation portfolios, and are more likely to adopt less advantageous strategies such as casual labor. The adoption of adaptation practices was significantly influenced by equipment, educational level, household size, herd size, training received, CC perceptions and agroecological setting. Public interventions to improve the adaptive capacity of pastoralists in Morocco's arid rangelands should be geared towards addressing these determinants and should prioritise small-scale pastoralists.

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Pastoralists' perceptions of climate change accord with observed climate trends.

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The nature and number of pastoralists' adaptive responses vary by wealth.

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Poor herders are more likely to adopt problematic strategies such as casual labor.

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Pastoralists' adaptive responses are specific to local agro-ecological conditions.

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Perceptions, adaptations and socioeconomics are significantly correlated.

Determinants of time-trends in exclusivity and continuation of breastfeeding in India: An investigation from the National Family Health Survey

Breastfeeding is an essential key factor for reducing child mortality and the risk of disease. Therefore, to ensure health and wellbeing for all (Sustainable Development Goal 03), and to reduce inequalities in child health status (SDG 10), understanding the determinants of breastfeeding is essential. Our research aims to investigate determinants of different breastfeeding practices from 2005-06 (NFHS-3) to 2015-16 (NFHS-4), and changes in different breastfeeding practices among various demographic, social, religious, and economic groups during this decade. We have used a multivariate binary logistic regression model to estimate significant determinants of different breastfeeding practices. The results show an 8.4% hike in Exclusive Breastfeeding (EBF), along with a 3.3% and 1% drop in the Continuation of Breastfeeding (CBF) up to one and two years respectively during this decade in India. Significant variation has been found in EBF, CBF1 and CBF2 among various categories of child's birth interval, place of residence, social groups, economic communities, and geographic regions. Maternal nutritional status and anemia levels had significant influence on CBF1 and CBF2, and gender of the children on EBF and CBF2 from 2005-06 to 2015-16. There was no significant variation in CBF1 and CBF2 among children born in different birth orders, whereas EBF was significantly less among higher birth orders children. A dramatic improvement in EBF was found among first birth ordered children, teenage and/or highly educated mother, rich families, and in north India, and CBF2 was significantly raised only among higher educated mothers. The estimation of determinants throughout the decade is essential for improving government policies related to supporting and encouraging breastfeeding. The study suggests more initiatives and investment for the promotion, support and improvement of CBF for one and two years, and reduction of inequality among various demographic and socio-economic groups and geographic regions.

Individual outcome prediction models for patients with COVID-19 based on their first day of admission to the intensive care unit

Background

Currently, good prognosis and management of critically ill patients with COVID-19 are crucial for developing disease management guidelines and providing a viable healthcare system. We aimed to propose individual outcome prediction models based on binary logistic regression (BLR) and artificial neural network (ANN) analyses of data collected in the first 24 hours of intensive care unit (ICU) admission for patients with COVID-19 infection. We also analysed different variables for ICU patients who survived and those who died.

Methods

Data from 326 critically ill patients with COVID-19 were collected. Data were captured on laboratory variables, demographics, comorbidities, symptoms and hospital stay related information. These data were compared with patient outcomes (survivor and non-survivor patients). BLR was assessed using the Wald Forward Stepwise method, and the ANN model was constructed using multilayer perceptron architecture.

Results

The area under the receiver operating characteristic curve of the ANN model was significantly larger than the BLR model (0.917 vs 0.810; p<0.001) for predicting individual outcomes. In addition, ANN model presented similar negative predictive value than the BLR model (95.9% vs 94.8%).

Variables such as age, pH, potassium ion, partial pressure of oxygen, and chloride were present in both models and they were significant predictors of death in COVID-19 patients.

Conclusions

Our study could provide helpful information for other hospitals to develop their own individual outcome prediction models based, mainly, on laboratory variables. Furthermore, it offers valuable information on which variables could predict a fatal outcome for ICU patients with COVID-19.

How government health insurance coverage of novel anti-cancer medicines benefited patients in China - a retrospective analysis of hospital clinical data

Background

China started to cover novel medicines for the treatment of major cancers, such as trastuzumab for breast cancer by the government health insurance programs since 2016. Limited data have been published on the use of cancer medications and little is known about how government health insurance coverage of novel anti-cancer medicines benefited patients in the real world. This study aimed to generate evidence to inform the health security authorities to optimize the government health insurance coverage of novel anti-cancer medicines as a more inclusive and equal policy, through which each of the needed patient can get access to the novel anti-cancer medicines regardless of the ability to pay.

Methods

The study targeted one of the government health insurance newly covered novel medicines for breast cancer and the breast cancer patients. The analyses were based on the data collected from one tertiary public hospital in Fujian province of China. We conducted interrupted time series analysis with a segmented regression model and multivariate analyses with a binary logistic regression model to analyze the impact of the government health insurance coverage on medicines utilization and the determinants of patient’s medication choice.

Results

The average proportion of patients who initiated medication with novel medicines increased from 37.4% before the government health insurance coverage to 69.2% afterwards. Such an increase was observed in all patient sub-groups. The monthly proportion of patients who initiated medication with novel medicines increased sharply by 18.3 % (95 %CI,10.4-34.0 %, p = 0.01) in September 2017, the afterwards trend continuously increased (95 %CI,1.03–3.60, p = 0.02). The critical determinants of patient's medication choice were mostly connected with the patient's health insurance benefits packages.

Conclusions

The government health insurance coverage of novel anti-breast-cancer medicines benefited the patients generally. The utilization of novel medicines such as trastuzumab continuously increased. The insurance coverage benefited well the patients in the high-risk age groups. However, rural patients, patients enrolled in the “resident program”, and patients from low-income residential areas and non-local patients benefited less from this policy. Improving the benefits package of the low-income patients and the “resident program” beneficiary would be of considerable significance for a more inclusive and equal health insurance coverage of novel anti-cancer medicines.

Psychological and physical abuse towards four-year-old children as reported by their parents: A national Finnish survey

BACKGROUND

In Finnish society, child maltreatment is a health and social problem with harmful consequences. Identifying families at risk may help preventing child maltreatment recurrence.

OBJECTIVE

The aim of this nationwide retrospective cross-sectional study was to describe the child- and family-related risk factors associated with physical and psychological abuse experienced by 4-year-old children.

METHODS

This study analyzed nationwide survey data collected by the Finnish Institute for Health and Welfare. Overall, 17,009 parents (46%) visiting at a child health clinic filled out the survey consent form. For 8720 children, one or both parents completed the questionnaire (24%). Analyses were carried out using χ² tests and binary logistic regression.

RESULTS

Of the 4-year-olds, 44% had experienced at least one form of psychological abuse and 14% physical abuse. These forms of violence co-occurred in 25% of the reported cases (p < 001). Intimate partner violence (IPV) and child maltreatment co-occurred in 19.6% of psychological abuse (p < .001) and 22.5% of physical abuse cases (p < .001). Parents exposed to IPV was the risk factor most likely to predict an increased risk for both psychological abuse (OR 4.01, CI 3.41-4.72; p < .001), and physical abuse (OR 2.19, CI 1.81-2.64; p < .001). Approving of hair-pulling or pinching the child (i.e., using corporal punishment) was most likely to predict an increased risk of physical abuse (OR 13.70, CI 11.69-16.06; p < .001).

CONCLUSIONS

The findings emphasize the importance of preventing all forms of child maltreatment by identifying families at risk and supporting parenthood according to families' needs.

Impact of urban expansion to peri-urban smallholder farmers' poverty in Tigray, North Ethiopia

Urban expansion in African and other developing countries occurred and happening by encroaching fertile agricultural land. As a result, close to fifteen million people are displaced every year. This paper examines the consequences of urban expansion on peri-urban farmers' poverty. Data was generated from 341 (101 treated, 240 control) smallholder Peri-urban farmers'. Inferential statistics, propensity score matching (PSM), econometric models, and poverty measurements were applied and analyzed using Stata version 14 software. The impact assessment estimation showed the prevalence of poverty was higher by 5% poverty than non-displaced households. The intensity and incidence of poverty were also found higher among displaced households. Displaced and non-displaced households were deprived 49.4% and 46.5% of weighted indicators respectively, while the incidence of poverty was found 69% and 59% respectively. In sum, 34.1% of displaced and 27.4% of non-displaced households were poor. Besides the overall percentage of contribution of education, health, and living standard dimensions were 15.4%, 28.8%, and 55.8% among displaced households respectively. While percentage contribution of education, health, and living standard among non-displaced households were 15.1%, 26%, and 58.9% respectively. Therefore, the prevalence of poverty was higher in displaced households than in non-displaced households. The binary logistic regression also revealed out of the 13 covariates only four variables were found statistically significant. Of the four variables, except treatment effect or being displaced the rest variables family size, farming experience, and market distance were negatively affect household poverty. In conclusion, in all aspects, the result indicated that the prevalence of poverty was higher among evicted farmers. Therefore, government bodies should design strong strategies to avert the impact of urban expansion on peri-urban farmers. A separate department should be established which follow and provide training on time, and horizontal urban expansion should be changed to vertical expansion.

Does worriedness among the rural adults promote COVID-19 related awareness in Bangladesh?

People living in urban areas are usually more aware of their health issues due to the availability and accessibility of health care facilities. Several studies have illustrated anxiousness, attitudes, and perceptions among urban people during COVID-19. This research attempted to assess how worriedness among rural adults may promote COVID-19 related awareness in Bangladesh. A cross-sectional online survey of 311 respondents aged 18 or greater was conducted through Facebook focusing only on the people living in rural areas. The survey included a consent form and requested demographic as well as pandemic related information in a three-section questionnaire from the respondents. We used the chi-square test statistic for bivariate analysis and the binary logistic regression model along with some tools to validate the model to analyze the impact of worriedness on awareness. The bivariate result showed a significant association among regular hand washing (p=.007), knowledge about the proper amount of time for washing one's hands effectively (p=.004), rules of social distancing (p=.00), and education level (p=.046) with our outcome variable worriedness. From our binary logistic regression model fitting, it emerged that the females (p=.032, OR=.729) who regularly wash their hands (0R=.393, p=.023), know the rules of social distancing for “yes” (0R=14.525,p< .01), and “no” groups (0R= 5.518, p< .01), and age groups (18–27, 28 to 37, 38 to 47) were more worried. Results from our modeling justify an accuracy of 73.08%, a sensitivity of 93.71%, and a specificity of 29.33% with Cohen's kappa statistic = .2716, suggesting a fair model fitting. This study shows that the current COVID-19 situation created awareness among females and adults aged between 18 to 47 years in rural Bangladesh.

A cross-sectional study to determine factors affecting dental and medical students' preference for virtual learning during the COVID-19 outbreak

Virtual “online” teaching has been adopted by most universities around the world during the COVID-19 outbreak. This study aims to investigate the factors that might affect students’ preference for virtual learning. Since a second wave of such pandemic is expected to occur, professors and teaching assistants may want to be prepared and aware to create an effective virtual learning environment for students.

Using an online survey questionnaire, a total of 488 students in their basic science years of study (first to the third year) who are enrolled in dental and medical college responded to the online survey. The authors utilized a binary logistic regression model to estimate the impact of the nine explanatory variables (gender, student's year of study, accessibility of online tools, class engagement in virtual classes, GPA change during COVID-19 outbreak, class attendance in virtual vs. in-person lectures, type of study material, time saving for virtual classes, and anxiety level during the COVID-19 outbreak) on the students' preference for virtual learning. The analysis of variance showed that three out of the nine variables were not significant to the model: gender, study level, and study material. In addition, to understand the behavioral intention for the students during such pandemic, the online survey questionnaire captured students' voice on their willingness to wear masks, wash their hands, or both as well as their acceptance to take the vaccine once it is available. The results showed that 7.02 % of the students did not change simple health behaviors and 18.43% are not interested in taking the vaccine. This implies the importance of enacting new laws for reopening universities, applying high fines for violators, and obligating students to take the vaccine since university settings have high levels of social contact with populations from different communities and countries.

Applying latent class analysis to investigate rural highway single-vehicle fatal crashes in China

Rural highways are an important component of highway networks in developing countries. The high fatality rates of single-vehicle crashes in these highways recently attracted increasing attention. Given that most studies on the factors that affect the severity of single-vehicle crashes in rural highways were conducted in developing countries, the present study investigated this issue in a Chinese setting by analyzing the single-vehicle crash data of rural highways in Anhui Province, China from 2014 to 2017. First, in consideration of the unobserved heterogeneity of crash data, a method that combines latent class analysis (LCA) and binary logistic regression (BLR), which is called LC-BLR, was applied to identify the significant factors that affect the severity of single-vehicle crashes in rural highways. Second, the goodness-of-fit and prediction accuracy of the LC-BLR model and the BLR model were compared. Results revealed that the performance of the former was more satisfactory than that of the latter. Finally, countermeasures were proposed based on the analysis of the main factors that affect each sub-class crash in the LC-BLR model. The LC-BLR model results indicated that collision typewas significant in all three sub-class models considered in the analysis, but the effects on crash severity varied. Several variables (e.g., driving license state, time of week, driver age) demonstrated a significant effect in a specific sub-class model, thereby indicating that these factors were only effective in mitigating the crash severity of one sub-class. The findings of this study can facilitate the development of cost-effective policies or countermeasures for reducing the severity of single-vehicle crashes in rural highways.

Development of a risk-based method for predicting the severity of potential fire accidents in road tunnels based on real-time data

Fire incidents are considered serious events for road tunnel safety because they can evolve into catastrophic accidents. Bearing in mind that tunnels constitute critical infrastructure elements of road systems, risk assessment has been employed to prepare tunnels to deal with such incidents. However, if an incident occurs, an adequate response is also related to the information about the particular event. To this respect, a novel risk-based method is proposed to support tunnel operators in assessing the criticality of potential fire incidents by using real-time data. The structure of the proposed method is as follows. Initially, the backlayering that determines the criticality of an incident is examined and the stochastic parameters of the system that affect backlayering are identified. Subsequently, multiple simulations are performed by changing the examined parameters randomly and thus the relation between backlayering and those parameters arises. As a result, the developed relation provided with real-time data can estimate the potential severity of any incident occurring in real time. The outcome facilitates tunnel operators to predict promptly the potential severity of fires and make better-informed decisions. This will allow a more efficient operation of the control room of the tunnel. An illustrative case is presented to showcase the utilisation of the proposed method.

Watershed degradation and management practices in north-western highland Ethiopia

Watershed degradation in Ethiopia has become a major environmental threat and caused significant damages both in the natural environment and the development of human society. This paper assesses an overview of the extent, causes, and effects of watershed degradation and the management actions in north-western highland Ethiopia, taking the Rib watershed as a case study site. The data were collected from field observations, interviews, and questionnaire surveys administered to 210 farmers. The results show that watershed degradation is a serious and widespread problem in the study site. The major causes of watershed degradation include population growth, uncontrolled grazing, unsustainable land use and management practices, and weak land ownership system. Watershed degradation, mainly in the form of soil erosion, has adversely reduced agricultural production and worsened food insecurity and poverty in the study area. Various watershed management practices that combine structural and biological measures have been practiced to curb the problem. However, the farmers' decisions to adopt and implement the measures have been highly influenced by a range of socioeconomic, biophysical, and institutional factors. Access to support services, size of the farmland, educational level, and plot ownership were found to influence farmers' decisions positively at a statistically significant (P < 0.01) level. It needs efforts to create farmer environmental awareness and develop strong watershed management standards and guidelines. The watershed could benefit from hand in hand efforts of local farmers, concerned governmental agencies, and researchers.

Plasma lipoprotein(a) concentration as an independent predictor of hemodynamic progression of aortic valve stenosis

Calcific aortic valve disease (CAVD) is a common cardiovascular disorder of high social significance. This study aimed to identify independent predictors of hemodynamic progression of CAVD. The relationship between some risk factors, including the rs10455872 polymorphism in the intron 25 of the lipoprotein(a) [Lp(a)] coding region and the plasma Lp(a) concentration, and CAVD severity were prospectively examined in 114 patients. Age (p = 0.023), smoking (p = 0.038), lack of obesity (p = 0.005), triglyceride levels (p = 0.039), and plasma Lp(a) (p < 0.0001) levels were found to be significant determinants of stenosis progression. The rs10455872 polymorphism; however, was not found to be a significant factor for neither the stenosis severity (p = 0.773) nor for plasma Lp(a) levels (p = 0.617). We established a highly significant Lp(a) cut-off concentration (21.2 mg/dL) distinguishing the aortic valve calcification without stenosis from the significant stenosis. Plasma Lp(a) concentration was the only independent predictor of disease progression (p < 0.0001). Moreover, patients with plasma levels of Lp(a) ≥ 21.2 mg/dL were 55 times more likely to develop aortic valve stenosis. We conclude that Lp(a) concentration may prove valuable for more reliable identification of patients at risk of accelerated CAVD development. Future studies are desirable to determine whether plasma Lp(a) levels could be used as a potential biomarker for aortic stenosis progression.

Factors influencing pedestrians' decision to cross the road by risky rolling gap crossing strategy at intersections in Dhaka, Bangladesh

Pedestrian road-crossing strategy is one of the most important pedestrian road-crossing behaviors. The safety of the pedestrians often depends on it. Among the road-crossing strategies, rolling gap crossing strategy is the riskiest one. The objective of this research was to explore the factors that influenced pedestrians' decision to cross the road by rolling gap crossing at intersection. Data regarding road-crossing strategy of the pedestrians, their characteristics, their road-crossing behavior, intersection geometry, and traffic environmental condition were collected through videography survey method, on-site observation, and secondary source from six intersections of Dhaka, Bangladesh. A binary logistic regression model was developed in this study by using the collected data. Results of the developed model showed that seven statistically significant factors strongly influenced pedestrians' decision to cross the road by rolling gap crossing at intersections. These factors were intersection control type, median width, vehicle flow, available gap on the road, age group of the pedestrians, their crossing group size, and their behavior of crosswalk usage. The results of this study would help the policymakers to take proper interventions to alleviate pedestrian safety problems.

Using machine learning to improve our understanding of injury risk and prediction in elite male youth football players

OBJECTIVES

The purpose of this study was to examine whether the use of machine learning improved the ability of a neuromuscular screen to identify injury risk factors in elite male youth football players.

DESIGN

Prospective cohort study.

METHODS

355 elite youth football players aged 10-18 years old completed a prospective pre-season neuromuscular screen that included anthropometric measures of size, as well as single leg countermovement jump (SLCMJ), single leg hop for distance (SLHD), 75% hop distance and stick (75%Hop), Y-balance anterior reach and tuck jump assessment. Injury incidence was monitored over one competitive season. Risk profiling was assessed using traditional regression analyses and compared to supervised machine learning algorithms constructed using decision trees.

RESULTS

Using continuous data, multivariate logistic analysis identified SLCMJ asymmetry as the sole significant predictor of injury (OR 0.94, 0.92-0.97, p<0.001), with a specificity of 97.7% and sensitivity of 15.2% giving an AUC of 0.661. The best performing decision tree model provided a specificity of 74.2% and sensitivity of 55.6% with an AUC of 0.663. All variables contributed to the final machine model, with asymmetry in the SLCMJ, 75%Hop and Y-balance, plus tuck jump knee valgus and anthropometrics being the most frequent contributors.

CONCLUSIONS

Although both statistical methods reported similar accuracy, logistic regression provided very low sensitivity and only identified a single neuromuscular injury risk factor. The machine learning model provided much improved sensitivity to predict injury and identified interactions of asymmetry, knee valgus angle and body size as contributing factors to an injurious profile in youth football players.

Farmers' choices and factors affecting adoption of climate change adaptation strategies: evidence from northwestern Ethiopia

Climate change is a major environmental and socioeconomic challenge in Ethiopia in recent decades. The study site is one of the climate change prone areas affected by climate variability and extreme events. Therefore, a better understanding of area-specific and adaptation is crucial to develop and implement proper adaptation strategies that can alleviate the adverse effects of climate change. Therefore, this work was aimed to identify determinants of farmers' adoption of climate change adaptation strategies in Gondar Zuria District of northwestern Ethiopia. Primary data were collected through semi-structured questionnaires, observation, and interviews. Besides, the secondary data were also obtained from journal articles, reports, governmental offices, and the internet. The Multinomial and Binary logistic regression models with the help of the Statistical Package for Social Sciences (SPSS) (21^th edition) were used to analyze the data. The multinomial logistic regression model was used to estimate the influence of the socioeconomic characteristics of sample households on the farmer's decision to choose climate change adaptation strategies. The result showed that age, gender, family size, farm income, and farm size had a significant influence on the farmers' choice of climate change adaptation strategies. The result also revealed that crop failure, severe soil erosion and shortages of water are major climate change-related problems than others. In order to alleviate these problems, farmers have implemented mixed farming, mixed cropping, early and late planting (changing sowing period), use of drought-resistant crop varieties, application of soil and water conservation techniques, shifting to non-farm income activities and use of irrigation. In contrast, access to climate information, total annual farm income, and market access variables are significant adoption determinants of climate change adaptation strategies by farmers' in the study site. Therefore, we recommend future adaptation-related plans should focus on improving climate change information access, improving market access and enhancing research on the use of rainwater harvesting technology.

Morphometric research and sex estimation of lumbar vertebrae in a contemporary Spanish population

In the identification and reconstruction of the biological profile of skeletal remains in physical and forensic anthropology, sex estimation is a fundamental step, and is essential to achieve methods that allow the highest allocation accuracy. This study proposes a method to estimate the sex of skeletal remains using lumbar vertebrae. A total of 33 linear measurements for each lumbar vertebra were taken in 94 identified, contemporary, and adult skeletal remains (46 male and 48 female) from the cemetery of San José in Granada. The sexual dimorphism of each variable was analyzed, an intra- and interobserver error analysis was developed to assess the variables with best concordance, and the discriminant equations were obtained through a binary logistic regression analysis. All lumbar vertebrae show statistically significant sexual dimorphism with higher values in males than in females. Equations with an allocation accuracy of between 80% and 94.5% were obtained, showing that the lumbar vertebrae are a useful alternative for sex estimation when other skeletal elements are not well-preserved or available. The differences of biogeographic history between populations could translate into morphometric differences, which means that it is not advisable to use discriminant equations in a universal way. In spite of the high allocation accuracy of the equations developed, they should be applied in adults from populations similar to the present study to obtain reliable results of sexual estimation, until its validation in other populations.

Crash prediction based on traffic platoon characteristics using floating car trajectory data and the machine learning approach

Predicting crash propensity helps study safety on urban expressways in order to implement countermeasures and make improvements. It also helps identify and prevent crashes before they happen. However, collecting real-time wide-coverage traffic information for crash prediction has been challenging. More importantly, previous studies have failed to consider the characteristics of the traffic platoon (vehicle group) that the crash vehicle belongs to before the crash occurs. This paper aims to model crash propensity based on traffic platoon characteristics collected by the floating car method, which provides a time-efficient and reliable solution to collecting traffic information. Crash and floating car data are collected from the Middle Ring Expressway in Shanghai, China. Both the binary logistic model and the support vector machine are applied. A data preparation method, involving crash data filtering, floating car data filtering and data matching on the road network, is introduced for the safety analysis purpose. Results suggest that the traffic platoon information collected from floating cars accompanied works reasonably in predicting crashes on expressways. The support vector machine, with an overall accuracy of 85%, outperformed the binary logistic model which had an overall accuracy of 60%. Results further suggest the application of floating car technologies and the support vector machine in real-time crash prediction. Despite this, the study also concludes the merits of the binary logistic model over the support vector machine model in explaining the impact of different factors that contribute to crash occurrences.

Higher rate of early-onset preeclampsia in pregnancies following oocyte donation according to increasing maternal age

OBJECTIVE

To assess the influence of maternal age on the incidence of early-onset preeclampsia requiring delivery before 34 weeks of gestation in pregnancies obtained after oocyte donation.

METHODS

We carried out a prospective cohort analysis of 431 single and twin pregnancies, admitted to 3 Tertiary Referral Hospital in Northern Italy between 2008 and 2017. The rate of early-onset PE was calculated and stratified according to maternal age (from 30 to 49 years). A reference population of 11,197 single pregnancies collected prospectively at the first trimester of pregnancy in the same geographic area of Italy and in same hospitals was used to calculate the expected incidence of early-onset PE.

RESULTS

In women who delivered after 24 weeks of gestation, the rate of early-onset PE was much higher in oocyte-donation pregnancies, reaching 6.7% (29/431), than the expected rate of 0.5% of the cohort of reference. The mean early PE rate was 4.1% (10/242) in singletons and 10.1% (19/189) in twin pregnancies. According to maternal age, the rate of early PE was 1.16% and 3.12% at 30 years, and 4.98% and 13.14% at 49 years in single and twin pregnancies obtained after oocyte donation, respectively.

CONCLUSION

Pregnancies obtained after oocyte donation delivering after 24 weeks had a higher risk of early-onset PE requiring delivery before 34 weeks of gestation, than the general population. The risk is directly correlated with the increase of maternal age and is also higher in twin pregnancies.

GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches

In arid and semi-arid areas, groundwater resource is one of the most important water sources by the humankind. Knowledge of groundwater distribution over space, associated flow and basic exploitation measures can play a significant role in planning sustainable development, especially in arid and semi-arid areas. Groundwater potential mapping (GWPM) fits in this context as the tool used to predict the spatial distribution of groundwater. In this research we tested four GIS-based models for GWPM, consisting of: i) random forest (RF); ii) weight of evidence (WoE); iii) binary logistic regression (BLR); and iv) technique for order preference by similarity to ideal solution (TOPSIS) multi-criteria. The Shahroud plain located in Iran, was selected to research the water scarcity and over-exploitation of groundwater resources over the past 20 years. In this research, using Iranian Department of Water Resources Management data, and extensive field surveys, 122 groundwater well data with high potential yield of ≥11 m³ h^-1 were selected for GWPM. Specifically, we generated four different models selecting 70% (n = 85) of the wells and validated the resulting GWP maps upon the complementary 30% (n = 37).A total of fifteen ground water conditioning factors to explain the groundwater well distribution over the Shahroud plain were selected. From the Advanced Land Observing Satellite (ALOS), a DEM (30 m resolution) was extracted to calculate a set of morphometric properties which were combined with thematic ones such as land use/land cover (LU/LC) and Soil Type (ST). Results show that in RF (LU/LC), LR (ST), and AHP (Slope) are the most relevant contributors to groundwater occurrence. After that, using the natural break method, final maps were divided into five susceptibility classes of very low, low, moderate, high, and very high. The accuracy of models was ultimately tested using prediction rate (validation data), success rate (training data) and the seed cell area index (SCAI) indicators. Results of validation show that BLR with prediction rate of 0.905 (90.5%) and success rate of 0.918 (91.8%) had higher accuracy than WoE, RF and TOPSIS models with respective prediction rates of 0.885, 0.873 and 0.870 (88.5%, 87.3%, and 87%) and success rate of 0.900, 0.889, and 0.881 (90%, 88.9%, and 88.1%). SCAI results show that all models have acceptable classification accuracy although BLR outperformed the other models in terms of accuracy. Results show that the combination of remote sensing (RS) data and geographic information system (GIS) with new approaches can be used as a powerful tool in GWPM in arid and semi-arid areas. The results of this investigation introduced a potential novel methodology that could be used by decision-makers for the sustainable management of ground water resources.

Bayesian model with application to a study of dental caries

BACKGROUND

Dental caries are a significant public health problem. It is a disease with multifactorial causes. In Sub-Sahara Africa, Ethiopia is one of the countries with a high record of dental caries. This study was to determine the risk factors affecting dental caries using both Bayesian and classical approaches.

METHODS

The study design was a retrospective cohort study in the period of March 2009 to March 2013 dental caries patients Hawassa Haik Poly Higher Clinic. The Bayesian logistic regression procedure was adapted to make inference about the parameters of a logistic regression model. The purpose of this method was generating the posterior distribution of the unknown parameters given both the data and some prior density for the unknown parameters.

RESULTS

From this study the prevalence of natural dental caries was 87% and non-natural dental caries were 13%. The age group of 18-25 was higher prevalence of dental caries than the other age groups. From Bayesian logistic regression, we found out that rural patients, do not clean their teeth, patients from SNNPR and age group 18-25 are statistically significant. The finding from the Bayesian statistics approach is getting popular in data analysis than classical statistics because the technique is more robust and precise.

CONCLUSIONS

Bayesian approach was found to be better than classical method as the value of the standard errors in Bayesian approaches is smaller than that of classical logistic regression. The Bayesian credible interval is smaller than the length of the confidence interval for all significant risk factors. Age, sex, place of residence, region and habit of cleaning teeth was found to have a significant effect on dental caries patients.

Cyclist casualty severity at roundabouts - To what extent do the geometric characteristics of roundabouts play a part?

INTRODUCTION

In general, priority junctions are converted into roundabouts to increase capacity and reduce vehicle accidents. However, previous research has indicated that roundabouts are dangerous for vulnerable users, especially cyclists.

METHOD

This paper investigates which design factors influence cyclist casualty severity at give way (non-signalized) roundabouts with mixed traffic, using the UK STATS19 National dataset of cyclist casualties. First, the correlation matrix was generated to observe the relationship between variables. Second, dimension reduction was applied to geometric design variables in order to reduce the number of variables and generate the factors. Finally, the binary logistic regression method, with serious and slight casualties as dependent variables, was applied in three steps. The first Binary Logistic Regression Model (BLRM) included speed limit, sociodemographic, and meteorological conditions. The variables in the second BLRM consisted of geometric design variables. The third BLRM included the factors that were generated by dimension reduction.

RESULTS

The correlation matrix revealed that the number of lanes on approach and half width on approach were statistically significantly correlated, while the variables, such as geometric design (entry path radius, number of arms, number of flare lanes on approach, type of roundabout and number of circulating lanes), sociodemographic (casualty gender and age), speed limit and meteorologically related factors (daylight, weather and road surface condition), did not show any statistical significance. From the dimension reduction process, two main factors were identified, including Approach Capacity (Factor 1) and Size of Roundabout (Factor 2), and they were subsequently used as independent variables in the logistic regression analysis. The subsequent BLRMs showed that a higher speed limit reduces the safety for cyclists at roundabouts. The probability of a serious casualty increases by approximately five times (odds ratio 4.97) for each additional number of lanes on approach and by 4% (odds ratio 1.04) with a higher entry path radius. It was also found that Factor 2 (Approach Capacity) increases the casualty severity (odds ratio 1.86) for cyclists at roundabouts. Practical applications: While this research studied roundabouts in the UK, the methodological approach and statistical analysis techniques are applicable to other countries and the findings are likely to be of value to decision makers worldwide.

Impact of timing of breastfeeding initiation on neonatal mortality in India

Background

Neonatal mortality defined as a death during the first 28 days of life and is the most critical phase of child survival. In spite of the strong evidence supporting immediate and long term health benefits of timely initiation of breastfeeding in India, only two-fifths (44%) of children receive breastfeeding within 1 h of birth. This study aims to examine the role of a behavioral factor i.e., timing of initiation of breastfeeding on neonatal deaths.

Methods

Data from India Human Development Survey-II (IHDS-II), 2011–12, a nationally representative, large scale population-based dataset has been used. Sample Registration System (SRS) has been used to examine the rate of change in Neonatal Mortality Rates from the year 2011 to 2015. District Level Household & Facility Survey (DLHS-4), 2012–2013 and Annual Health Survey(AHS), 2012–13 data have been used to show the district wise distribution of women who have breastfed their child within 1 h of birth. Population Attributable fraction has been computed using binary logistic regression model for various scenarios of breastfeeding within first hour of birth.

Results

Less than one fourth (21%) of children were breastfed within 1 h of birth across the different districts of India, which varies from the lowest 15% in Sarasvati of Uttar Pradesh state to the highest 94.6% in Thiruvananthapuram of Kerala state. Findings suggest when women did not breastfeed their newborn within the 1 h after his birth, the odds of neonatal deaths were increased by nearly threefold (OR 2.93; 95% CI 1.89, 4.53) in comparison with those neonates who have breastfed within 1 h of birth. Population Attributable Risk estimates that the risk of the neonatal deaths could be reduced to a maximum of 15% when all babies would expose to early breastfeeding from the present level of breastfeeding.

Conclusions

We found that timely initiation of breastfeeding is beneficial for child survival within the first 28 days of birth, including all causes of mortality. Therefore, efforts in formulating an effective policy focusing on early initiation of breastfeeding are needed.

Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies

Objective

To investigate the importance of controlling confounding factors during binary logistic regression analysis.

Methods

Male coronary heart disease (CHD) patients (n = 664) and healthy control subjects (n = 400) were enrolled. Fourteen indexes were collected: age, uric acid, cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B100, lipoprotein a, homocysteine, total bilirubin, direct bilirubin, indirect bilirubin, and γ-glutamyl transferase. Associations between these indexes and CHD were assessed by logistic regression, and results were compared by using different analysis strategies.

Results

1) Without controlling for confounding factors, 14 indexes were directly inputted in the analysis process, and 11 indexes were finally retained. A model was obtained with conflicting results. 2) According to the application conditions for logistic regression analysis, all 14 indexes were weighed according to their variances and the results of correlation analysis. Seven indexes were finally included in the model. The model was verified by receiver operating characteristic curve, with an area under the curve of 0.927.

Conclusions

When binary logistic regression analysis is used to evaluate the complex relationships between risk factors and CHD, strict control of confounding factors can improve the reliability and validity of the analysis.

Self-medication practice and associated factors among pregnant women in Addis Ababa, Ethiopia

Background

Self-medication which is the act of obtaining and using one or more medicines without medical supervision is a common practice among pregnant women. Unless proper caution is taken, it may result in maternal and fetal adverse outcomes. In Ethiopia, information on self-medication practice during pregnancy is scanty. Hence, this study aimed to assess self-medication practice and associated factors among pregnant women in government health centers in Addis Ababa.

Methods

An institution-based mixed study design using a sequential explanatory approach was employed among 617 pregnant women and nine key informants in Addis Ababa from May 8, 2017, to June 30, 2017. Multi-stage sampling technique was used to select study participants, and purposive sampling technique was used to select the key informants. The quantitative data were collected using a structured interview questionnaire and analyzed using Statistical Product and Service Solutions (SPSS) version 23.0 whereas semi-structured questionnaire was used for in-depth interviews. Binary logistic regression was used for quantitative data analysis, and thematic analysis method was used for qualitative data.

Results

The prevalence of self-medication practice was 26.6%. Previous medication use (Adjusted odds ratio (AOR) = 4.20, 95% CI 2.70-6.53), gestational period (AOR = 0.63, 95% CI 0.41-0.98), education on self-medication (AOR = 0.36, 95% CI 0.21-0.62), previous pregnancy and delivery related problems (AOR = 1.71, 95% CI 1.06-2.76), and knowledge about risk of self-medication (AOR = 0.64, 95% CI 0.42-0.97) were significantly associated with self-medication practice. Lack of attention and priority of program designers, absence of strategies and guidelines; weak screening mechanisms, and regulatory enforcement were cited by the key informants as contributing factors for self-medication practices.

Conclusions

Considerable proportion of pregnant women practiced self-medication, including medicines categorized to have high risks. Gestational period, previous medication use, education on self-medication, previous pregnancy- and delivery-related problems, and knowledge were significantly associated with self-medication practice. In addition, there are correctable gaps in program designing, screening of pregnant women, regulatory enforcement, and strategies and guidelines. Hence, necessary measures at all levels must be taken to reduce risks of self-medication during pregnancy.

Establishment and validation of a two-step screening scheme for improved performance of serological screening of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC), which is closely associated with Epstein-Barr virus (EBV), is one of the most prevalent cancers in southeast China. Most NPC patients are diagnosed at late stage due to inconspicuous symptoms at the early stage, and the prognosis of these patients is poor. The early diagnosis rate of NPC could be significantly increased by serological screening, but the positive predictive value (PPV) is relatively low. A simple two-step serological screening scheme was established to improve the PPV of the screening strategy and was validated by a prospective cohort. Serum antibodies specific for EBNA1, Zta, Thymidine Kinase (TK), EAD, EAR, and VCA were detected by enzyme-linked immunosorbent assay. The combination of EBNA1/IgA and VCA/IgA was used in the first step of screening, and anti-early antigens (EAs) were used in the second step of screening. EAD/IgA was the most prominent marker in the second step of screening, and other anti-EAs were complementary to EAD/IgA. As validated by a prospective cohort including 4200 participants, using the combination of EAD/IgA and TK/IgA in the second step decreased the number of high-risk participants from 128 to 27, and increased the PPV from 4.69% to 18.52%, with only one very early-stage case missed. The two-step screening scheme provides a standardized approach for NPC screening with an improved PPV and may be used in future field studies. With this two-step serological screening method, more people benefit from the screening program without increasing the need for fiberoptic endoscopy.

Development of an empirical model to predict malaria outbreaks based on monthly case reports and climate variables in Hefei, China, 1990-2011

Malaria remains a significant public health concern in developing countries. Drivers of malaria transmission vary across different geographical regions. Climatic variables are major risk factor in seasonal and secular patterns of P. vivax malaria transmission along Anhui province. The study aims to forecast malaria outbreaks using empirical model developed in Hefei, China. Data on the monthly numbers of notified malaria cases and climatic factors were obtained for the period of January 1st 1990 to December 31st 2011 from the Hefei CDC and Anhui Institute of Meteorological Sciences, respectively. Two logistic regression models with time series seasonal decomposition were used to explore the impact of climatic and seasonal factors on malaria outbreaks. Sensitivity and specificity statistics were used for evaluating the predictive power. The results showed that relative humidity (OR = 1.171, 95% CI = 1.090-1.257), sunshine (OR = 1.076, 95% CI = 1.043-1.110) and barometric pressure (OR = 1.051, 95% CI = 1.003-1.100) were significantly associated with malaria outbreaks after adjustment for seasonality in Hefei area. The validation analyses indicated the overall agreement of 70.42% (sensitivity: 70.52%; specificity: 70.30%). The research suggested that the empirical model developed based on disease surveillance and climatic conditions may have applications in malaria control and prevention activities.

Quantitative exploration of factors influencing psychotic disorder ailments in Nigeria

In this data article, records on demographic data, family problem issues, as well as results of medical tests from five major classes of psychotic disorder namely: bipolar; vascular dementia, minimal brain dysfunction; insomnia; and schizophrenia, were collected on 500 psychotic patients carefully selected from the pool of medical records of Yaba Psychiatric Hospital, Lagos, Nigeria, for the period of 5 years, between January 2010 and December 2014, were examined. X-squared Statistic was used to examine each of psychotic disorders to identify demographic (age, gender, religion, marital status, and occupation) and family issues (loss of parent, history of such ailment in the family (family status), divorce, head injury, and heredity of such ailment (genetic) factors that influence them. A clear description on each of these psychotic disorders (bipolar; vascular dementia, minimal brain dysfunction (MBD), insomnia and Schizophrenia) was considered separately using tables and bar diagrams. Data analysis results are as follows: firstly, 40.2%, of the 500 psychotic patients tested positive to bipolar, 40.6% to insomnia, 75.0% to schizophrenia, 43.6% to MBD and 69.2% to vascular dementia. Secondly, female patients were more prone to all the psychotic indicators than their male counterpart except in MBD. Thirdly, the oldest age group (> 60 years) is more prone to bipolar and insomnia ailments, while the mid age group (30 - 60 years) is prone to schizophrenia and vascular dementia, and the youngest group (< 30 years) is prone to MBD. Lastly, the factors that influence the ailments are listed: bipolar (age, occupation, marital status, divorce, and spiritual consultation); insomnia (age, occupation, marital status, divorce, and spiritual consultation); schizophrenia (age, occupation, religion, marital status, hereditary, and divorce); MBD (gender, age, occupation, and marital status); and vascular dementia (history of the ailment and spiritual consultation). Bipolar and insomnia are influenced by the same set of factors, which implies that any patient having one is most likely to be at risk of having the other.

Assessing the habitat suitability of agricultural landscapes for characteristic breeding bird guilds using landscape metrics

Many of the processes behind the decline of farmland birds can be related to modifications in landscape structure (composition and configuration), which can partly be expressed quantitatively with measurable or computable indices, i.e. landscape metrics. This paper aims to identify statistical relationships between the occurrence of birds and the landscape structure. We present a method that combines two comprehensive procedures: the "landscape-centred approach" and "guild classification". Our study is based on more than 20,000 individual bird observations based on a 4-year bird monitoring approach in a typical agricultural area in the north-eastern German lowlands. Five characteristic bird guilds, each with three characteristic species, are defined for the typical habitat types of that area: farmland, grassland, hedgerow, forest and settlement. The suitability of each sample plot for each guild is indicated by the level of persistence (LOP) of occurrence of three respective species. Thus, the sample plots can be classified as "preferred" or "less preferred" depending on the lower and upper quartiles of the LOP values. The landscape structure is characterized by 16 different landscape metrics expressing various aspects of landscape composition and configuration. For each guild, the three landscape metrics with the strongest rank correlation with the LOP values and that are not mutually dependent were identified. For four of the bird guilds, the classification success was better than 80%, compared with only 66% for the grassland bird guild. A subset of six landscape metrics proved to be the most meaningful and sufficiently classified the sample areas with respect to bird guild suitability. In addition, derived logistic functions allowed the production of guild-specific habitat suitability maps for the whole landscape. The analytical results show that the proposed approach is appropriate to assess the habitat suitability of agricultural landscapes for characteristic bird guilds.