A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design

Calf mortality in Turkish dairy farms: Economic impact, regional disparities, and farm-level drivers

This study investigates the economic burden of calf mortality in Turkish dairy farms and its impact on the national economy. We gathered research data by directly surveying dairy farms in seven provinces, each representing a distinct region of Turkiye. By conducting these surveys, we captured data on various aspects of calf mortality, including losses among non-pregnant cows aged two and older, pregnant cows, and those experiencing complications during birth, as well as losses within the 0-6 month age bracket. These figures were then amalgamated to establish the overall calf loss rate. Using a fractional probit model, we examined the empirical relationship between total calf loss rates and the socio-demographic characteristics of farm operators and their establishments. Our findings revealed that approximately 82 % of farms experienced some degree of calf loss, with the calf loss rate among dairy cattle farming accounting for nearly 20 %. Notably, regional disparities emerged as a key observation, alongside the identification of certain socio-demographic farm characteristics that proved statistically significant. Specifically, factors such as the prevalence of local cattle breeds, the proportion of crossbred bulls, as well as the numbers of heifers and calves, stood out as influential. Further scrutiny, fortified by ANOVA tests and relationships between the number of cows and total calf loss rate, underscored pronounced geographical disparities in post-estimation calf loss rates. Meanwhile, correlation heatmaps illuminated noteworthy relationships between specific cattle traits and the extent of calf losses. These findings not only underscore the severity of the issue but also highlight the urgency of preventive measures. In light of these insights, we offer pertinent policy recommendations to stakeholders and policymakers aimed at mitigating this considerable economic burden effectively.

Exploring the influence of drivers' visual surroundings on speeding behavior

Despite awareness campaigns and legal consequences, speeding is a significant cause of road accidents and fatalities globally. To combat this issue, understanding the impact of a driver's visual surroundings is crucial in designing roadways that discourage speeding. This study investigates the influence of visual surroundings on drivers in 15 US cities using 3,407,253 driver view images from Lytx, covering 4,264 miles of roadways. By segmenting and analyzing these images along with vehicle-related variables, the study examines factors affecting speeding behavior. After filtering the images, to ensure an accurate representation of the driver's view, 1,340,035 driver view images were used for analysis. Statistical models, including hurdle beta and bivariate probit models with random driver effects as well as Machine Learning's eXtreme Gradient Boosting (XGBoost), were employed to estimate speeding behavior. The results indicate that factors within the driver's visual environment, weather conditions, and driver heterogeneity significantly impact speeding. Speeding behavior also varies across geographic locations, even within the same city, suggesting a connection between local context and speeding. The study highlights the importance of the driver's environment, showing that more open spaces encourage speeding, while areas with trees and buildings are associated with reduced speeding. Notably, this research differs from previous studies by utilizing real-time data from dash cameras, providing a dynamic and accurate representation of the driver's visual surroundings. This approach enhances the reliability of the findings and empowers transportation engineers and planners to make informed decisions when designing roadways and implementing interventions to address effectively excessive speeding. In addition to examining speeding behavior, the study also analyzes time-headway, a key factor affecting safety and risky driver behavior, to explore its relationship with speeding. The findings offer valuable insights into the factors influencing speeding and the driver's visual environment. These insights can inform efforts to create environments that discourage speeding (and close car following) and ultimately reduce severe accidents caused by excessive speed (and tailgating).

Development and Validation of a Risk Prediction Model to Estimate the Risk of Stroke Among Hypertensive Patients in University of Gondar Comprehensive Specialized Hospital, Gondar, 2012 to 2022

Background

A risk prediction model to predict the risk of stroke has been developed for hypertensive patients. However, the discriminating power is poor, and the predictors are not easily accessible in low-income countries. Therefore, developing a validated risk prediction model to estimate the risk of stroke could help physicians to choose optimal treatment and precisely estimate the risk of stroke.

Objective

This study aims to develop and validate a risk prediction model to estimate the risk of stroke among hypertensive patients at the University of Gondar Comprehensive Specialized Hospital.

Methods

A retrospective follow-up study was conducted among 743 hypertensive patients between September 01/2012 and January 31/2022. The participants were selected using a simple random sampling technique. Model performance was evaluated using discrimination, calibration, and Brier scores. Internal validity and clinical utility were evaluated using bootstrapping and a decision curve analysis.

Results

Incidence of stroke was 31.4 per 1000 person-years (95% CI: 26.0, 37.7). Combinations of six predictors were selected for model development (sex, residence, baseline diastolic blood pressure, comorbidity, diabetes, and uncontrolled hypertension). In multivariable logistic regression, the discriminatory power of the model was 0.973 (95% CI: 0.959, 0.987). Calibration plot illustrated an overlap between the probabilities of the predicted and actual observed risks after 10,000 times bootstrap re-sampling, with a sensitivity of 92.79%, specificity 93.51%, and accuracy of 93.41%. The decision curve analysis demonstrated that the net benefit of the model was better than other intervention strategies, starting from the initial point.

Conclusion

An internally validated, accurate prediction model was developed and visualized in a nomogram. The model is then changed to an offline mobile web-based application to facilitate clinical applicability. The authors recommend that other researchers eternally validate the model.

Abstinence-contingent wage supplements for adults experiencing homelessness and alcohol use disorder: A randomized clinical trial

This study evaluated the effectiveness of abstinence-contingent wage supplements in promoting alcohol abstinence and employment in adults experiencing homelessness and alcohol use disorder. A randomized clinical trial was conducted from 2019 to 2022. After a 1-month Induction period, 119 participants were randomly assigned to a Usual Care Control group (n = 57) or an Abstinence-Contingent Wage Supplement group (n = 62). Usual Care participants were offered counseling and referrals to employment and treatment programs. Abstinence-Contingent Wage Supplement participants could earn stipends for working with an employment specialist and wage supplements for working in a community job but had to maintain abstinence from alcohol as determined by transdermal alcohol concentration monitoring devices to maximize pay. Abstinence-Contingent Wage Supplement participants reported significantly higher rates of alcohol abstinence than Usual Care participants during the 6-month intervention (82.8% vs. 60.2% of months, OR = 3.4, 95% CI 1.8 to 6.3, p < .001). Abstinence-Contingent Wage Supplement participants were also significantly more likely to obtain employment (51.3% vs. 31.6% of months, OR = 2.6, 95% CI 1.5 to 4.4, p < .001) and live out of poverty (38.2% vs. 16.7% of months, OR = 3.7, 95% CI 2.0 to 7.1, p < .001) than Usual Care participants. These findings suggest that Abstinence-Contingent Wage Supplements can promote alcohol abstinence and employment in adults experiencing homelessness and alcohol use disorder. ClinicalTrials.gov Identifier: NCT03519009.

Risk factors associated with calf mortality in Western Canadian cow-calf operations

This study examined the influence of management practices and herd demographics on calf mortality proportions in Western Canadian cow-calf operations, utilizing data from the second Western Canadian Cow-calf Survey. The survey was conducted between October 23, 2017, and February 28, 2018. The survey, which was open to all cow-calf producers across Western Canada, provided producer-reported data regarding calf death loss and corresponding herd-level factors. A fractional logit model was employed to identify significant factors associated with calf mortality proportions. The findings revealed that shorter breeding seasons (<63 days), calves born within the same season, and regular pregnancy checks for breeding females were negatively associated with calf mortality proportions. Conversely, regular breeding soundness evaluations for breeding bulls, traditional weaning methods, and vaccinating heifers for scours showed positive associations with increased calf mortality proportions. Herd operations where dams were vaccinated against clostridial and bovine respiratory diseases had lower calf mortality proportions. Notably, operations with experienced primary decision-makers holding off-farm jobs had lower predicted calf mortality proportions compared to those managed by full-time cattle producers. Higher predicted calf mortality proportions were observed in operations employing a backgrounding system. The study's limitations included potential biases due to its cross-sectional nature and reliance on producer-reported data, which might lead to an underestimation of calf mortality proportions. Also, the limited sample size and missing data might have affected the statistical significance of some variables. This study contributed to the research on cow-calf operation by using a fractional logit model to analyze the correlation between risk factors and calf mortality proportions, and by identifying novel herd-level risk factors. It provided a basis for future research aimed at developing empirically-based management strategies to optimize calf health outcomes.

Active travel and paratransit use in African cities: Mixed-method systematic review and meta-ethnography

Active travel, as a key form of physical activity, can help offset noncommunicable diseases as rapidly urbanising countries undergo epidemiological transition. In Africa a human mobility transition is underway as cities sprawl and motorization rises and preserving active travel modes (walking, cycling and public transport) is important for public health. Across the continent, public transport is dominated by paratransit, privately owned informal modes serving the general public. We reviewed the literature on active travel and paratransit in African cities, published from January 2008 to January 2019. We included 19 quantitative, 14 mixed-method and 8 qualitative studies (n = 41), narratively synthesizing the quantitative data and meta-ethnographically analysing the qualitative data. Integrated findings showed that walking was high, cycling was low and paratransit was a critical mobility option for poor peripheral residents facing long livelihood-generation journeys. As an indigenous solution to dysfunctional mobility systems shaped by colonial and apartheid legacies it was an effective connector, penetrating areas unserved by formal public transport and helping break cycles of poverty. From a public health perspective, it preserved active travel by reducing mode-shifting to private vehicles. Yet many city authorities viewed it as rogue, out of keeping with the 'ideal modern city', adopting official anti-paratransit stances without necessarily considering the contribution of active travel to public health. The studies varied in quality and showed uneven geographic representation, with data from Central and Northern Africa especially sparse; notably, there was a high prevalence of non-local authors and out-of-country funding. Nevertheless, drawing together a rich cross-disciplinary set of studies spanning over a decade, the review expands the literature at the intersection of transport and health with its novel focus on paratransit as a key active travel mode in African cities. Further innovative research could improve paratransit's legibility for policymakers and practitioners, fostering its inclusion in integrated transport plans.

Disentangling Drivers of Food Waste in Households: Evidence from Nigeria

Food waste is a burden on society in terms of the money wasted. There is limited information on the determinants of food waste and the amount lost to food waste by households as most previous studies were on post-harvest losses. Hence, determinants of food waste among households in Kogi West Senatorial District, Kogi State Nigeria were investigated. A three-stage sampling technique was used to select the respondents, while a structured questionnaire was used for data collection. Data were analyzed using Tobit regression and an equality test. The study revealed that food waste was higher in male headed households. The average monthly food waste proportion among urban households was significantly higher than that of rural households. The estimated amounts lost to food waste per month were ₦2103 and ₦5530 for the rural and urban households, respectively. These represented 7.2% and 13.1% of the total expenditure on food per month for rural and urban households, respectively. Among rural households, leftovers of food and lack of proper storage were the main reasons for food waste, while leftovers of food and preparation of food more than needed were the reasons for food waste among urban households. The sex of respondents, work experience, and monthly income influenced the proportion of food waste among rural households, while the dependency ratio, monthly income, and monthly food expenditure were the determinants of proportion of food waste among the urban households. Non-Governmental Organization efforts through sensitization campaigns focused on the need to reduce food waste, especially among urban households, would help to reduce the financial burden of food waste on households.

Developing a grouped random parameter beta model to analyze drivers' speeding behavior on urban and suburban arterials with probe speed data

Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials.

Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level

Background

Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community‐based interventions have been shown to be both effective and cost‐effective in preventing cardiovascular disease. There is a dearth of robust studies identifying the key determinants of cardiovascular disease and the underlying effect mechanisms at the neighborhood level. We aim to contribute to the evidence base for neighborhood cardiovascular health research.

Methods and Results

We created a new neighborhood health data set at the census tract level by integrating 4 types of potential predictors, including unhealthy behaviors, prevention measures, sociodemographic factors, and environmental measures from multiple data sources. We used 4 tree‐based machine learning techniques to identify the most critical neighborhood‐level factors in predicting the neighborhood‐level prevalence of stroke, and compared their predictive performance for variable selection. We further quantified the effects of the identified determinants on stroke prevalence using a Bayesian linear regression model. Of the 5 most important predictors identified by our method, higher prevalence of low physical activity, larger share of older adults, higher percentage of non‐Hispanic Black people, and higher ozone levels were associated with higher prevalence of stroke at the neighborhood level. Higher median household income was linked to lower prevalence. The most important interaction term showed an exacerbated adverse effect of aging and low physical activity on the neighborhood‐level prevalence of stroke.

Conclusions

Tree‐based machine learning provides insights into underlying drivers of neighborhood cardiovascular health by discovering the most important determinants from a wide range of factors in an agnostic, data‐driven, and reproducible way. The identified major determinants and the interactive mechanism can be used to prioritize and allocate resources to optimize community‐level interventions for stroke prevention.

Mapping WOMAC Onto the EQ-5D-5L Utility Index in Patients With Hip or Knee Osteoarthritis

OBJECTIVES

To map the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) onto the EQ-5D-5L in patients with hip or knee osteoarthritis (OA).

METHODS

A prospective observational study was conducted on 758 patients with hip or knee OA who completed the EQ-5D-5L and WOMAC questionnaires, of whom 644 completed them both again 6 months later. Baseline data were used to derive mapping functions. Generalized additive models were used to identify to which powers the WOMAC subscales should be raised to achieve a linear relationship with the response. For the modeling, general linear models (GLM), Tobit models, and beta regression models were used. Age, sex, and affected joints were also considered. Preferred models were selected based on Akaike and Bayesian information criteria, adjusted R², mean absolute error (MAE), and root mean squared error (RMSE). The functions were validated with the follow-up data using MAE, RMSE, and the intraclass correlation coefficient.

RESULTS

The preferred models were a GLM with Pain²+Pain³+Function+Pain·Function as covariates and a beta model with Pain³+Function+Function²+Function³ as covariates. The adjusted R² were similar (0.6190 and 0.6136, respectively). The predictive performance of these models in the validation sample was similar and both models showed an overprediction for health states worse than death.

CONCLUSION

To our knowledge, these are the first functions mapping the WOMAC onto the EQ-5D-5L in patients with hip or knee OA. They showed an acceptable fit and precision and could be very useful for clinicians and researchers when cost-effectiveness studies are needed and generic preference-based health-related quality of life instruments to derive utilities are not available.

Inclusion of special populations in clinical research: important considerations and guidelines

BACKGROUND

Trials that involve human participants call for experiments or observations that are performed in a clinical research setting. Currently, there are over 16,000 clinical trials open in the United States. Despite continuing efforts to include "special populations" in clinical trials, there are gaps in participation for people who are either minors or elderly adults, are from historically under-represented minorities, or live in rural communities. The inclusion of these special populations in clinical trials research is essential for conclusions that benefit all populations. Data suggest that study partic-ipation rates for special populations have fallen to levels that could endanger the successful performance of some types of research. This is particularly concerning in the 21st century, where demographic trends in the United States continue to shift towards an older and Hispanic population with fewer rural dwellers. Trends in New Mexico and other minority-majority states mirror many of these shifts.

RELEVANCE FOR PATIENTS

In this review, we highlight improvement strategies for enhanced clinical trial participation by members of special populations. Key drivers for disparate clinical trials participation and outcomes often include differences in genetics, physiology, and perceptions of mistrust towards researchers. To overcome these barriers, we focus on best practices in recruitment strategies from the perspectives of the participants, the researchers and the institutions that support clinical trials.

Beta Regression Model for Predicting the Development of Pink Rot in Potato Tubers During Storage

Pink rot is an important disease of potato with worldwide distribution. Severe yield and quality losses have been reported at harvest and in postharvest storage. Under conditions favoring disease development, pink rot severity can continue to increase from the field to storage and from storage to transit, causing further losses. Prediction of pink rot disease development in storage has great potential for growers to intervene at an earlier stage of disease development to minimize economic losses. Pink rot disease is estimated as percent rot confined on the interval (0 or 1, corresponding to 0% as no disease and 100% as maximum disease). In this study, beta regression is considered over the traditional ordinary least squares regression (linear regression) for fitting continuous response variables bounded on the unit interval (0,1). This method is considered a good alternative to data transformation and analysis by linear regression. The percentages of incidence of pink rot in tubers at harvest, yield, and days after harvest were used as study covariates to predict pink rot development from 32 to 78 days postharvest. Results demonstrate that the interaction between percentage of pink rot at harvest and yield is a significant predictor (P < 0.0001) of the beta regression model. A linear regression model was also designed to compare the results with the proposed beta regression model. Linear predictors observed in diagnostic plots with linear regression model was found to not be constant and an adjusted R² (0.49) was obtained. The pseudo R² (0.56) and constant variance for this study suggests that the beta regression function is adequate for predicting the development of pink rot during storage. The use of the beta prediction model could help growers decide whether to apply a fungicide to tubers going into storage or to market their crop before significant storage losses are incurred.