A 500,000-Person Experiment Evaluating the Impact of Geographically-Targeted, High-Payoff Vaccine Lotteries

Lotteries have been shown to motivate behavior change in many settings. However, the value of large-scale, geographically-targeted lotteries as a policy tool for changing the behaviors of entire populations is a matter of heated debate. In mid-2021, we implemented a pre-registered, city-wide experiment in Philadelphia to test the effects of three, high-payoff (up to $50,000) geographically-targeted lotteries designed to motivate adult residents of Philadelphia to get vaccinated against COVID-19. All Philadelphia residents ages 18 and older were eligible for inclusion in each drawing but, if selected, could not accept a prize unless they had received at least one dose of a COVID-19 vaccine. In each drawing, residents of a randomly selected “treatment” zip code received half of the 12 lottery prizes (boosting their chances of a win to 50-100x those of other Philadelphians). This experimental design makes possible a causal estimate of the impact of vastly increasing people’s odds of winning a vaccine lottery. We estimate that the first treated zip code, which drew considerable media attention, may have experienced a small bump in vaccinations compared to control zip codes: vaccinations rose by an estimated 61 per 100,000 people (an 11% increase). Pooling results from all three zip codes treated over the course of our six-week experiment, however, we do not detect any overall benefits. This unsustained effect may be because media attention waned, salience of the lottery declined, or attitudes about vaccination became increasingly entrenched over time. Further, our 95% confidence interval provides an upper bound on the overall benefits of treatment in our study of 9%. Given that lotteries of this scale cost hundreds of thousands of dollars to implement, the lack of a substantial benefit from this experiment strengthens the policy case for other, more impactful ways to encourage health behavior change.

Geographically Targeted Spending in Mixed-Member Majoritarian Electoral Systems

ABSTRACT Can governments elected under mixed-member majoritarian (mmm) electoral systems use geographically targeted spending to increase their chances of staying in office, and if so, how? Although twenty-eight countries use mmm electoral systems, scant research has addressed this question. The authors explain how mmm’s combination of electoral systems in two unlinked tiers creates a distinct strategic environment in which a large party and a small party can trade votes in one tier for votes in the other tier in a way that increases the number of seats won by both. They then explain how governing parties dependent on vote trading can use geographically targeted spending to cement it. These propositions are tested using original data from Japan (2003–2013) and Mexico (2012–2016). In both cases, municipalities in which the supporters of governing parties split their ballots as instructed were found to have received more money after elections. The findings have broad implications for research on mmm electoral systems, distributive politics, and the politics of Japan and Mexico.

The global, regional, and national burden of acute pancreatitis in 204 countries and territories, 1990–2019

Background Acute pancreatitis is a common and potentially lethal gastrointestinal disease, but literatures for the disease burden are scarce for many countries. Understanding the current burden of acute pancreatitis and the different trends across various countries is essential for formulating effective preventive intervenes. We aimed to report the incidence, mortality, and disability-adjusted life-years (DALYs) caused by acute pancreatitis in 204 countries and territories between 1990 and 2019. Methods Estimates from the Global Burden of Disease Study 2019 (GBD 2019) were used to analyze the epidemiology of acute pancreatitis at the global, regional, and national levels. We also reported the correlation between development status and acute pancreatitis’ age-standardized DALY rates, and calculated DALYs attributable to alcohol etiology that had evidence of causation with acute pancreatitis. All of the estimates were shown as counts and age-standardized rates per 100,000 person-years. Results There were 2,814,972.3 (95% UI 2,414,361.3–3,293,591.8) incident cases of acute pancreatitis occurred in 2019 globally; 1,273,955.2 (1,098,304.6–1,478,594.1) in women and 1,541,017.1 (1,307,264.4–1,814,454.3) in men. The global age-standardized incidence rate declined from 37.9/100,000 to 34.8/100,000 during 1990–2019, an annual decrease of 8.4% (5.9–10.4%). In 2019, there were 115,053.2 (104,304.4–128,173.4) deaths and 3,641,105.7 (3,282,952.5–4,026,948.1) DALYs due to acute pancreatitis. The global age-standardized mortality rate decreased by 17.2% (6.6–27.1%) annually from 1.7/100,000 in 1990 to 1.4/100,000 in 2019; over the same period, the age-standardized DALY rate declined by 17.6% (7.8–27.0%) annually. There were substantial differences in the incidence, mortality and DALYs across regions. Alcohol etiology attributed to a sizable fraction of acute pancreatitis-related deaths, especially in the high and high-middle SDI regions. Conclusion Substantial variation existed in the burden of acute pancreatitis worldwide, and the overall burden remains high with aging population. Geographically targeted considerations are needed to tailor future intervenes to relieve the burden of acute pancreatitis in specific countries, especially for Eastern Europe.

Wildfire-mitigating power shut-offs promote household-level adaptation but not climate policy support

Unmitigated climate change threatens to disrupt energy systems, for example through weather- and wildfire-induced electricity shortages. Public responses to these energy crises have the potential to shape decarbonization trajectories. Here, we estimate the attitudinal and behavioral effects of Californian power shut-offs in 2019, intended to reduce wildfire ignition risks. We use a geographically targeted survey to compare residents living within outage zones to matched residents in similar neighborhoods who retained their electricity. Outage experience increased respondent intentions to purchase gas or diesel generators and home battery systems, but reduced intentions to purchase electric vehicles. Respondents blamed outages on their utility, not local, state, or federal governments. However, outages did not change climate policy preferences, including willingness-to-pay for either wildfire or climate-mitigating reforms. Our findings show that, in reaction to some climate-linked disruptions, individuals may undertake adaptive responses that, collectively, could exacerbate future climate risks.

Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties

As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 impact’s indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, age structure, natural supply, economic condition, air quality, and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).

Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality Impacts on Texas Counties

As COVID-19 run rampant in high-density housing sites, it is important to use real-time data tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research is appropriately analyzed the disparities between spatial-temporal clusters, expectation Maximization clustering (EM) and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to South-east Texas analysis in GWR modeling. The sensitive period took place in the last two quarters in 2020. We explored Postgre application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 impact’s indices to perform Principal Component Analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, age structure, natural supply, economic condition, air quality and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility and satisfy for the need of Emergency Operations Plan (EOP).