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AbstractThe adequacy of the Supplemental Nutrition Assistance Program (SNAP) benefits is always an important concern. This article extends the most common measure for evaluating the adequacy of SNAP, food expenditures, and uses more comprehensive metrics to evaluate the impact of the American Recovery and Reinvestment Act (ARRA). These more comprehensive metrics are easy to implement with existing data, more closely tied to the purpose of the SNAP, and indicate a slightly larger impact of the ARRA.

In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM(2.5) is one of the main components of air pollution. Therefore, it is necessary to know the PM(2.5) air quality in advance for health. Many studies on air quality are based on the government's official air quality monitoring stations, which cannot be widely deployed due to high cost constraints. Furthermore, the update frequency of government monitoring stations is once an hour, and it is hard to capture short-term PM(2.5) concentration peaks with little warning. Nevertheless, dealing with short-term data with many stations, the volume of data is huge and is calculated, analyzed and predicted in a complex way. This alleviates the high computational requirements of the original predictor, thus making Spark suitable for the considered problem. This study proposes a PM(2.5) instant prediction architecture based on the Spark big data framework to handle the huge data from the LASS community. The Spark big data framework proposed in this study is divided into three modules. It collects real time PM(2.5) data and performs ensemble learning through three machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting Decision Tree) to predict the PM(2.5) concentration value in the next 30 to 180 min with accompanying visualization graph. The experimental results show that our proposed Spark big data ensemble prediction model in next 30-min prediction has the best performance (R(2) up to 0.96), and the ensemble model has better performance than any single machine learning model. Taiwan has been suffering from a situation of relatively poor air pollution quality for a long time. Air pollutant monitoring data from LASS community can provide a wide broader monitoring, however the data is large and difficult to integrate or analyze. The proposed Spark big data framework system can provide short-term PM(2.5) forecasts and help the ...

Purpose: To examine if employee health literacy (HL) status moderated reach, retention, and weight outcomes in a worksite weight loss program. Design: The study was a two-group cluster randomized controlled weight loss trial. Setting: The study was conducted in 28 worksites. Subjects: Subjects comprised 1460 employees with a body mass index >25 kg/m2. Interventions: Two 12-month weight loss interventions targeted diet and physical activity behaviors: incentaHEALTH (INCENT; incentivized individually targeted Internet-based intervention) and Livin' My Weigh (LMW; less-intense quarterly newsletters). Measures: A validated three-item HL screening measure was self-completed at baseline. Weight was objectively assessed with the Health Spot scale at baseline and 12-month follow-up. Analysis: The impact of HL on program effectiveness was assessed through fixed-effect parametric models that controlled for individual (i.e., age, gender, race, ethnicity, income, education) and worksite random effects. Results: Enrolled employees had significantly higher HL status [13.54 (1.68)] as compared to unenrolled [13.04 (2.17)] ( p < .001). This finding was consistent in both interventions. Also, HL moderated weight loss effects ( beta = .66; SE = 027; p = .014) and losing >5% weight ( beta = −1.53; SE = .77; p < .047). For those with lower baseline HL, the INCENT intervention produced greater weight loss outcomes compared to LMW. The HL level of employees retained was not significantly different from those lost to follow-up. Conclusion: HL influences reach and moderates weight effects. These findings underscore the need to integrate recruitment strategies and further evaluate programmatic approaches that attend to the needs of low-HL audiences.