Peer mentoring for smoking cessation in public housing: A mixed-methods study

IntroductionTobacco use disproportionately affects low-income African American communities. The recent public housing smoke-free policy has increased the demand for effective smoking cessation services and programs in such settings.MethodsThis mixed-method pilot study explored feasibility and potential impact of a peer-mentoring program for smoking cessation in a public housing unit. The quantitative study used a quasi-experimental design while qualitative data were collected via focus group discussions with peer mentors and participants. Three residents of the public housing complex were trained as peer mentors. Each peer mentor recruited up to 10 smokers in the residence and provided them individual support for 12 weeks. All participants were offered Nicotine Replacement Therapy (NRT). A follow-up investigation was conducted 3 months after completion of the 12-week intervention. At baseline and follow-up, the participants' smoking status was measured using self-report and was verified using exhaled carbon monoxide (eCO) monitoring.ResultsThe intervention group was composed of 30 current smokers who received the peer-mentoring intervention. The control group was composed of 14 individuals. Overall mean eCO levels dropped from 26 ppm (SD 19.0) at baseline to 12 (SD 6.0) at follow-up (P < 0.01). Participants who were enrolled in our program were more likely to have non-smoking eCO levels (<7 ppm) at follow-up (23.3%) compared to those who did not enroll (14.3%).ConclusionOur program is feasible for low-income predominantly African American communities. Using peers as mentors may be helpful in providing services for hard-to-reach populations. Given the non-randomized design of our study, randomized trials are needed to test the efficacy of our program in the future

Novel Machine Learning Algorithms for Prediction and Treatment Decision in Patients with Class III

The decision to treat adult patients with Class III malocclusion is complicated by limited options: orthodontic camouflage or orthognathic surgery. The decision-making process is often guided by the clinician's expertise in managing similar presentations of the malocclusion; and influenced by a myriad of phenotypic and psychosocial factors unique to each patient. Various radiographic and photographic features extracted from the pretreatment records are typically analyzed visually, and perhaps by discriminate analysis formulas, to make the selection approach more objective. Despite the high predictive accuracy percentages reported by those methods, they were limited by computational power and lacked account for the multitude of nonquantifiable variables thought to impact the treatment decision. Aims: 1) Identify morphological characteristics (key demographic, radiographic, and clinical parameters) that affect the treatment decision for non-growing Class III patients. 2) Establish a comprehensive data set for training and testing of the Machine Learning (ML) model/Artificial Neural Networks (ANNs). 3) Conduct internal data mining and use descriptive statistics to investigate the extent of difference in parameter values between the two groups and determine their statistical significance. 4) Build, test, and validate various ML models and compare their predictive accuracy. 5) Calculate the relative contribution of the features in each network model. Methods: Pretreatment records of 182 patients (118 surgical:65 camouflage) who received treatment post their pubertal growth spurts were analyzed and 40 demographic, radiographic, and clinical parameters were collected for each case. Data mining steps were applied to the parameters to identify statistical difference between the two groups. The cases were also divided into a testing and validation set for eight different ML models: Support Vector Machine (SVM), Random Forest, k-Nearest Neighbor (kNN), Logistic Regression, Multi-Layer Preceptron (MLP), Convolutional Neural Network (CNN), Extreme Gradient Boosting (XGBoost), and pruned XGBoost + Selected Features. The performance metrics of the various models were calculated and compared. Results: retreatment parameters vary in their contribution to the therapy approach in patients of Class III malocclusion. Different machine learning models can produce the desired binary output with an accuracy percentage ranging from 78% for the kNN algorithm to 93% for the pruned XGBoost model. The three cephalometric variables of Wit’s appraisal, mx/md ratio, and overjet showed both statistical significance with independent t-test comparison between the camouflage and orthognathic groups, and high weight values across the ML algorithms tested. Conclusion: a highly predictive artificial intelligence model can be developed that is more accurate than all existing RBES and CBES statistical models

Determining the Amount of Carbon Dioxide Emission from Primary Energy Consumption in Different Production Sectors of Iran: A multi-factor Energy Input-output Analysis

This study investigates the impact of final demand change on primary energy consumption, renewable energy consumption, CO2 emissions, and economic growth. For this purpose, the multi-factor energy input-output method proposed by Guevara and Domingos (2017) has been adopted, and an input-Output table for the year 2016 has been used. The results show that among energy products, electricity has the highest primary energy consumption coefficient. Although the rate of renewable energy consumption in this product is higher than other products, due to the small share of renewable energy consumption in primary energy consumption, electricity has the highest rate of CO2 emission. Also, the efficiency of primary energy conversion to secondary energy is 24% with the lowest efficiency among energy products. Among non-energy products, non-metallic mineral products and transportation services have the highest primary energy consumption coefficient and CO2 emission. The results of units’ emission production growth of the sectors related to non-energy products show that leather products had the least CO2 emissions per production growth unit. In contrast, transportation services had the highest emissions per production growth unit