This statistic displays the findings of a survey on the share of people participating in lotteries and related products in Great Britain in 2016, by mental ill health. During the survey period, it was found that ** percent of respondents with probable mental ill health as per the GHQ-12 score stated that they participated in the National Lottery draws during the past 12 months.

Abstract (en): In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. The Oregon Health Insurance Experiment follows and compares those selected in the lottery (treatment group) with those not selected (control group). The data collected and provided here include data from in-person interviews, three mail surveys, emergency department records, and administrative records on Medicaid enrollment, the initial lottery sign-up list, welfare benefits, and mortality. This data collection has seven data files: Dataset 1 contains administrative data on the lottery from the state of Oregon. These data include demographic characteristics that were recorded when individuals signed up for the lottery, date of lottery draw, and information on who was selected for the lottery, applied for the lotteried Medicaid plan if selected, and whose application for the lotteried plan was approved. Also included are Oregon mortality data for 2008 and 2009. Dataset 2 contains information from the state of Oregon on the individuals' participation in Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance to Needy Families (TANF). Datasets 3-5 contain the data from the initial, six month, and 12 month mail surveys, respectively. Topics covered by the surveys include demographic characteristics; health insurance, access to health care and health care utilization; health care needs, experiences, and costs; overall health status and changes in health; and depression and medical conditions and use of medications to treat them. Dataset 6 contains an analysis subset of the variables from the in-person interviews. Topics covered by the survey questionnaire include overall health, health insurance coverage, health care access, health care utilization, conditions and treatments, health behaviors, medical and dental costs, and demographic characteristics. The interviewers also obtained blood pressure and anthropometric measurements and collected dried blood spots to measure levels of cholesterol, glycated hemoglobin and C-reactive protein. Dataset 7 contains an analysis subset of the variables the study obtained for all emergency department (ED) visits to twelve hospitals in the Portland area during 2007-2009. These variables capture total hospital costs, ED costs, and the number of ED visits categorized by time of the visit (daytime weekday or nighttime and weekends), necessity of the visit (emergent, ED care needed, non-preventable; emergent, ED care needed, preventable; emergent, primary care treatable), ambulatory case sensitive status, whether or not the patient was hospitalized, and the reason for the visit (e.g., injury, abdominal pain, chest pain, headache, and mental disorders). The collection also includes a ZIP archive (Dataset 8) with Stata programs that replicate analyses reported in three articles by the principal investigators and others: Finkelstein, Amy et al "The Oregon Health Insurance Experiment: Evidence from the First Year". The Quarterly Journal of Economics. August 2012. Vol 127(3). Baicker, Katherine et al "The Oregon Experiment - Effects of Medicaid on Clinical Outcomes". New England Journal of Medicine. 2 May 2013. Vol 368(18). Taubman, Sarah et al "Medicaid Increases Emergency Department Use: Evidence from Oregon's Health Insurance Experiment". Science. 2 Jan 2014. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Presence of Common Scales: Patient Health Questionnaire-9 (PHQ-9) Total Severity Score SF-8 Health Survey Physical Component Score SF-8 Health Survey Mental Component Score Framingham Risk Score Response Rates: For the mail surveys, the response rates were 45 percent for the initial survey, 49 percent for the six month survey, and 41 percent for the 12 month survey. For the in-person survey the response rate was 59 percent. The individu...

Provide information by administrative area, gender, age, and assistance amount owed (amount in thousand yuan).

Existing evidence regarding the effects of Medicaid expansion, largely focused on aggregate effects, suggests health insurance impacts some health, healthcare utilization, and financial hardship outcomes. In this study we apply causal forest and instrumental forest methods to data from the Oregon Health Insurance Experiment (OHIE), to explore heterogeneity in the uptake of health insurance, and in the effects of (a) lottery selection and (b) health insurance on a range of health-related outcomes. The findings of this study suggest that the impact of winning the lottery on the health insurance uptake varies among different subgroups based on age and race. In addition, the results generally coincide with findings in the literature regarding the overall effects: lottery selection (and insurance) reduces out-of-pocket spending, increases physician visits and drug prescriptions, with little (short-term) impact on the number of emergency department visits and hospital admissions. Despite this, we detect quite weak evidence of heterogeneity in the effects of the lottery and of health insurance across the outcomes considered.

Introduction

I've been really interested in plotting and visualizing different NBA trends throughout this Thanksgiving break. Recently, I have been wanting to fact-check a common axiom I hear around the NBA during draft season: the notion that *older* draft prospects tend to have have *lower* upside. This is such a widespread belief that it can be heard on all levels, from NBA fan discussion on r/nba, to media draft analysis, to even GMs speaking about their draft choices.

For this visualization, I calculated the age of every lottery pick in the NBA draft from 1995 - 2015. I started at 1995 since this was the first modern "prep-to-pro" year with Kevin Garnett jumping from high school to the NBA. I ended at 2015 since I don't think we can develop an accurate read on the career trajectory of draft picks chosen after 2015 yet.

For each age range, I plotted a boxplot to visualize the distribution of the players' career PER, WS/48, BPM, and VORP. Let me know if you prefer to see another stat included here - I just went with the ones that Basketball Reference had publicly available.

Data

Here is the link to my plot

Key Results and Conclusions

Minimal differences among 18-21 year old prospects

It seems that differences in "upside" among 18-21 year old prospects are largely contrived by our brain's intuition, since there do not appear to be any significant difference in performance or success in the NBA for 18-19 year olds when compared to 19-20 and 20-21 year olds. Although VORP shows that the best of the best players since 1995 have been those drafted at age 18-19, the variation in distribution of BPM, WS/48, and career PER data is much lower.

Thus, we should be a lot more careful when assigning more favorable grades to extremely young prospects because they don't seem to have markedly better careers when compared to their slightly older counterparts. (Example: The data shows that 20.8 year old Donovan Mitchell would not have any different upside than 18.9 year old Kevin Knox)

Lower Extreme values for 22+ year old prospects

Interestingly, it looks like the median production is not really affected by the age of the prospect selected at all. However, there are some clear differences in the extremes.

The collective distribution of 22 and 23 year old lottery prospects shows that they tend to have much lower upper quartiles and extreme values, thus the best-case scenarios for these types of players is not as exciting. Although this difference is not as pronounced for 18-21 year olds, there is a huge drop off in the upper extreme values when moving from the 21-22 year old range to the 22-23 range.

Contrary to many other contexts, the NBA draft is a lot more about the outliers than it is about the median selection - each team is gambling on their pick becoming a future Tim Duncan or Dirk Nowitzki, and a successful draft would mean finding a franchise player-level talent. Therefore, our final conclusion is that although there are minimal differences in upside when comparing prospects in the 18-21 age range, 22+ year old prospects tend to have markedly lower ceilings than their younger peers.

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Acknowledgements/Notes

• Data was scraped from basketball reference (player pages, draft pages, advanced stats pages) as well as wikipedia (specific dates of each draft for age calculation). Scraping was done using beautiful soup.
• Figures were processed using numpy/pandas and visualized in matplotlib.
• Sample sizes for each age range:

The Community Activities Team is funded by the People's Health Trust using money raised by HealthTotal through The Health Lottery. People's Health Trust is an independent charity addressing health inequalities across Great Britain. It works closely with each of the 51 society lotteries and makes grants using money raised by the society lotteries through The Health Lottery. Active Communities supports people to create and shape local projects that will help their community or neighbourhood to become even better. Active Communities projects aim to develop social links and ties and support residents to discuss and act on things that are important to them.

Mitigating the spread of disease is crucial for the well-being of agricultural production systems. Implementing biosecurity disease prevention measures can be expensive, so producers must balance the costs of biosecurity investments with the expected benefits of reducing the risk of infections. To investigate the risk associated with this decision making process, we developed an online experimental game that simulates biosecurity investment allocation of a pork production facility during an outbreak. Participants are presented with several scenarios that vary the visibility of the disease status and biosecurity protection implemented at neighboring facilities. Certain rounds allowed participants to spend resources to reduce uncertainty and reveal neighboring biosecurity and/or disease status. We then test how this uncertainty affects the decisions to spend simulation dollars to increase biosecurity and reduce risk. We recruited 50 attendees from the 2018 World Pork Expo to participate in our simulation. We compared their performance to an opportunity sample of 50 online participants from the survey crowdsourcing tool, Amazon Mechanical Turk (MTurk). With respect to biosecurity investment, we did not find a significant difference between the risk behaviors of industry professionals and those of MTurk participants for each set of experimental scenarios. Notably, we found that our sample of industry professionals opted to pay to reveal disease and biosecurity information more often than MTurk participants. However, the biosecurity investment decisions were not significantly different during rounds in which additional information could be purchased. To further validate these findings, we compared the risk associated with each group's responses using a well-established risk assessment survey implementing paired lottery choices. Interestingly, we did not find a correlation in risk quantified with simulated biosecurity investment in comparison to the paired lottery choice survey. This may be evidence that general economic risk preferences may not always translate into simulated behavioral risk, perhaps due to the contextual immersion provided by experimental gaming simulations. Online recruitment tools can provide cost effective research quality data that can be rapidly assembled in comparison to industry professionals, who may be more challenging to sample at scale. Using a convenience sample of industry professionals for validation can also provide additional insights into the decision making process. These findings lend support to using online experimental simulations for interpreting risk associated with a complex decision mechanism.

OLG's annual payments to the province support: * hospitals * research, prevention and treatment of problem gambling * amateur sport * local and provincial charities (through the Ontario Trillium Foundation) * other government priorities including general healthcare and education *[OLG]: Ontario Lottery and Gaming Corporation