Greg Ridgeway

Bayesian inference for the distribution of grams of marijuana in a joint.

BACKGROUND The average amount of marijuana in a joint is unknown, yet this figure is a critical quantity for creating credible measures of marijuana consumption. It is essential for projecting tax revenues post-legalization, estimating the size of illicit marijuana markets, and learning about how much marijuana users are consuming in order to understand health and behavioral consequences. METHODS Arrestee Drug Abuse Monitoring data collected between 2000 and 2010 contain relevant information on 10,628 marijuana transactions, joints and loose marijuana purchases, including the city in which the purchase occurred and the price paid for the marijuana. Using the Brown-Silverman drug pricing model to link marijuana price and weight, we are able to infer the distribution of grams of marijuana in a joint and provide a Bayesian posterior distribution for the mean weight of marijuana in a joint. RESULTS We estimate that the mean weight of marijuana in a joint is 0.32g (95% Bayesian posterior interval: 0.30-0.35). CONCLUSIONS Our estimate of the mean weight of marijuana in a joint is lower than figures commonly used to make estimates of marijuana consumption. These estimates can be incorporated into drug policy discussions to produce better understanding about illicit marijuana markets, the size of potential legalized marijuana markets, and health and behavior outcomes.

Officer Risk Factors Associated with Police Shootings: A Matched Case–Control Study

ABSTRACT Particularly with the resurgence of concern over police use of deadly force, there is a pressing need to understand the risk factors that lead to police shootings. This study uses a matched-case–control design to remove confounders of shooting incidents and identify features of officers that increased their risk of shooting. By matching shooting officers to nonshooting officers at the same scene, the analysis isolates the role of the officers’ features from the features of the incident’s environment. The study uses data from the New York City Police Department on 291 officers involved in 106 officer-involved shootings adjudicated between 2004 and 2006. Black officers were 3.3 times and officers rapidly accumulating negative marks in their files were 3.1 times more likely to shoot than other officers. Officers who started their police career later in life were less likely to shoot. The results indicate that officer features related to discharging a firearm are identifiable.

Availability of Health-Related Programs in Private and Public Prisons

Little is known about the resources available to protect inmates’ health in private prisons compared to their public counterparts. This is the first national-level study that exclusively examined the availability of health-related programs in private and public prisons in the United States. We applied propensity score weighting and doubly robust estimation to compare private prisons to comparable public prisons. Data were self-reported by prison administrators as part of the 2005 Census of State and Federal Adult Correctional Facilities. We found that private prisons offered fewer substance dependency, psychological/psychiatric, and HIV/AIDS-related programs. But the differences were progressively reduced when the comparison was limited to public prisons most similar on a variety of facility-level characteristics. The extent to which the two types of prisons differ is closely tied to the characteristics of the facilities that are compared.

Development and Validation of an Algorithm for Identifying Patients with Hemophilia A in an Administrative Claims Database

BACKGROUND The accuracy with which hemophilia A can be identified in claims databases is unknown. OBJECTIVE Develop and validate an algorithm using predictive modeling supported by machine learning to identify patients with hemophilia A in an administrative claims database. METHODS We first created a screening algorithm using medical and pharmacy claims to identify potential hemophilia A patients in the US HealthCore Integrated Research Database between January 1, 2006 and April 30, 2015. Medical records for a random sample of patients were reviewed to confirm case status. In this validation sample, we used lasso logistic regression with cross-validation to select covariates in claims data and develop a predictive model to estimate the probability of being a confirmed hemophilia A case. RESULTS The screening algorithm identified 2,252 patients and we reviewed medical records for 400 of these patients. The screening algorithm had a positive predictive value (PPV) of 65%. The predictive model identified 18 predictors of being a hemophilia A case or noncase. The strongest predictors of case status included male sex, factor VIII therapy, office visits for hemophilia A, and hospitalizations for hemophilia A. The strongest predictors of noncase status included hospitalizations for reasons other than hemophilia A and factor VIIa therapy. A probability threshold of ≥0.6 resulted in a PPV of 94.7% (95% CI: 92.0-97.5) and sensitivity of 94.4% (95% CI: 91.5-97.2). CONCLUSIONS We developed and validated an algorithm to identify hemophilia A cases in an administrative claims database with high sensitivity and high PPV.

Assessing the Fairness and Effectiveness of Bicycle Stops in Tampa

This research investigates the fairness and effectiveness of making a large number of bicycle stops as a proactive policing strategy designed to reduce unsafe riding and crime in Tampa, Florida. Public concern about the fairness and effectiveness of this tactic was magnified by a 2015 newspaper article that noted racial disparities in bicycle stops by the Tampa Police Department (TPD). Our analyses found that there are large racial disparities in bicycle stops, which cannot be explained by differences in ridership as measured by our benchmark, bicycle crashes with injury. The observed racial disparities in bicycle stops appear to be attributable to TPD’s crime control efforts, though we cannot rule out some racial bias. Given that crime control was a motivating factor for TPD’s use of bicycle stops, we assessed the effect of bicycle stops on crime using a natural experiment. We found that bicycle stops did not have a meaningful effect on crime.

Health system and law enforcement synergies for injury surveillance, control and prevention: a scoping review

Background Healthcare providers and law enforcement (LE) officers are among the most common first responders to injuring events. Despite frequent interface between the health system (HS) and LE sectors, the published evidence that supports their collaboration in injury surveillance, control and prevention has not been comprehensively reviewed. Methods We conducted a scoping review of literature published from 1990 to 2016 that focused on local and regional HS and LE collaborations in injury surveillance, control and prevention. Our aim was to describe what is known and what remains unexplored about these cross-sector efforts. Results 128 articles were included in the final review. These were categorised by their focus on either surveillance activities or partnerships in injury control and prevention programmes. The majority of surveillance articles focused on road traffic injuries. Conversely, articles describing partnerships and programme evaluations primarily targeted the prevention of interpersonal violence. Discussion This review yielded two major findings: overall, the combination of HS and LE injury data added value to surveillance systems, especially as HS data augmented LE data; and HS and LE partnerships have been developed to improve injury control and prevention. However, there are few studies that have evaluated the impact and sustainability of these partnerships. Conclusions The current evidence to support HS and LE collaboration in injury surveillance and control and prevention programmes is heterogeneous. Notable gaps suggest ample opportunity for further research and programme evaluation across all types of injury.