Do High School Students in India Gamble? A Study of Problem Gambling and Its Correlates

Studies from the West suggest that significant numbers of high school students gamble, despite it being illegal in this age group. To date, there have been no studies on the prevalence of gambling among senior high school and higher secondary school students in India. This study reports point prevalence of gambling and its psychosocial correlates among high school students in the State of Kerala, India. 5043 high school students in the age group 15–19 years, from 73 schools, were selected by cluster random sampling from the district of Ernakulam, Kerala, South India. They completed questionnaires that assessed gambling, substance use, psychological distress, suicidality, and symptoms of Attention Deficit Hyperactivity Disorder (ADHD). Of a total of 4989 completed questionnaires, 1400 (27.9 %) high school students reported to have ever gambled and 353 (7.1 %) were problem gamblers. Of those who had ever gambled, 25.2 % were problem gamblers. Sports betting (betting on cricket and football) was the most popular form of gambling followed by the lottery. Problem gamblers when compared with non-problem gamblers and non-gamblers were significantly more likely to be male, have academic failures, have higher rates of lifetime alcohol and tobacco use, psychological distress, suicidality, history of sexual abuse and higher ADHD symptom scores. Gambling among adolescents in India deserves greater attention, as one in four students who ever gambled was a problem gambler and because of its association with a range of psychosocial variables.     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Using best available science to protect critical areas in Washington state: challenges and barriers to planners

Urban development has profound impacts on ecological patterns and processes making the scientific information required for developing environmental ordinances central for mitigating these negative ecological impacts. Washington State requires that planners use the best available science (BAS) to formulate land use ordinances as part of the state’s Growth Management Act (GMA). We present empirical findings describing challenges to planners in defining “best available science” and using BAS to create local ordinances that balance development needs with natural resource protection. We interviewed city and county planners (and their consultants) in western Washington to determine what they find useful about BAS, whether or not BAS is applicable to their jurisdictions, and what constraints they experience in reviewing and using BAS to create or update their land use ordinances. Our results suggest that applying the BAS requirement is particularly difficult in urban areas. Specifically, planners had difficulty applying results from research conducted in systems dissimilar to their urban landscapes. These challenges to planners were exacerbated by (1) a lack of resources and (2) political tensions among stakeholders with competing values in urban settings. We conclude with recommendations for improving the consideration of science in statewide land-use planning.     

Correlates of physical activity in a community sample of older adults in Appalachia

Although much has been learned about the global determinants of physical activity in adults, there has been a lack of specific focus on gender, age, and urban/rural differences. In this church-based community sample of Appalachian adults (N = 1,239), the primary correlates of physical activity included age, gender, obesity, and self-efficacy. Overall, 42% of all participants and 31% of adults age 65 years or older met recommended guidelines for physical activity, which suggests that most participants do not engage in adequate levels of physical activity. Of participants who met physical activity guidelines, the most common modes of moderate and vigorous activity were walking briskly or uphill, heavy housework or gardening, light strength training, and biking. These particular activities that focus on building self-efficacy might be viable targets for intervention among older adults in rural communities.     

Corrected profile likelihood confidence interval for binomial paired incomplete data

Clinical trials often use paired binomial data as their clinical endpoint. The confidence interval is frequently used to estimate the treatment performance. Tang et al. (2009) have proposed exact and approximate unconditional methods for constructing a confidence interval in the presence of incomplete paired binary data. The approach proposed by Tang et al. can be overly conservative with large expected confidence interval width (ECIW) in some situations. We propose a profile likelihood‐based method with a Jeffreys' prior correction to construct the confidence interval. This approach generates confidence interval with a much better coverage probability and shorter ECIWs. The performances of the method along with the corrections are demonstrated through extensive simulation. Finally, three real world data sets are analyzed by all the methods. Statistical Analysis System (SAS) codes to execute the profile likelihood‐based methods are also presented. Copyright © 2013 John Wiley & Sons, Ltd.     

COMPLEMENTARITIES,MULTIPLICITY, AND SUPPLY INFORMATION

If traders can obtain private information about the payoff and the supply of a stock then there can exist (i) complementarity in information acquisition and (ii) multiple equilibria in the financial and information markets. The additional dimension of supply information increases coordination possibilities in the financial market, leading to multiple equilibria. The existence of two information sources can lead to information acquisition being complementary. The multiplicity of equilibria is suggestive of excess volatility and crashes. The different financial market equilibria imply differing patterns of cost of capital and volume of trade. (JEL: D82, D83, G14)     

Accountability Agreements in Ontario Hospitals: Are They Fair?

Governments can be accountable for improving the fairness oftheir priority setting through enhanced transparency and stakeholderengagement. A case analysis is conducted of priority settingin a government health care context in Ontario, Canada, assessinghow implementation of hospital accountability agreements meetsthe conditions of a leading international ethical frameworkfor priority setting, "accountability for reasonableness" (A4R).Hospital accountability agreements provide a mechanism for governmentto ensure that public funding achieves desired performance inhospitals. A key goal of priority setting is fairness. A4R linkspriority setting, legitimacy, and fairness to theories of democraticdeliberation, making a claim for fairness if the four conditionsof relevance, publicity, revision/appeals, and enforcement aresatisfied. Regarding the relevance condition, this analysissuggests that government only partially met the relevance conditionproviding limited stakeholder engagement but with evidence ofpolicy learning and movement toward the establishment of inclusivestakeholder arrangements. Evidence suggests that governmenteventually progressed toward meeting the publicity condition.Government only partially met the revision/appeals conditionand did not meet the enforcement condition, as the other conditionswere only partially met. It is our view that regional governancestructures in Ontario (i.e., Local Health Integration Networksor LHINs) provide an opportunity for the province to improvethe fairness of their accountability agreement processes throughenhancing transparency and stakeholder engagement. More broadly,this case study provides a guide for government to enhance accountabilityby focusing on A4R to improve the fairness of its priority setting.     

Inventory Policy with Parametric Demand: Operational Statistics,Linear Correction,and Regression

In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements.     

Relationships Between Full and Layer Models with Applications to Level Merging

Analysis of a large dimensional contingency table is quite involved. Models corresponding to layers of a contingency table are easier to analyze than the full model. Relationships between the interaction parameters of the full log-linear model and that of its corresponding layer models are obtained. These relationships are not only useful to reduce the analysis but also useful to interpret various hierarchical models. We obtain these relationships for layers of one variable, and extend the results for the case when layers of more than one variable are considered. We also establish, under conditional independence, relationships between the interaction parameters of the full model and that of the corresponding marginal models. We discuss the concept of merging of factor levels based on these interaction parameters. Finally, we use the relationships between layer models and full model to obtain conditions for level merging based on layer interaction parameters. Several examples are discussed to illustrate the results.