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Bicycling has individual and collective health benefits. Safety concerns are a deterrent to bicycling. Incomplete data on bicycling volumes has limited epidemiologic research investigating safety impacts of bicycle infrastructure, such as protected bike lanes. In this case-control study, set in Atlanta, Georgia, USA between 2016-10-01 and 2018-08-31, we estimated the incidence rate of police-reported crashes between bicyclists and motor vehicles (n = 124) on several types of infrastructure (off-street paved trails, protected bike lanes, buffered bike lanes, conventional bike lanes, and sharrows) per distance ridden and per intersection entered. To estimate underlying bicycling (the control series), we used a sample of high-resolution bicycling data from Strava, an app, combined with data from 15 on-the-ground bicycle counters to adjust for possible selection bias in the Strava data. We used model-based standardization to estimate effects of treatment on the treated. After adjustment for selection bias and confounding, estimated ratio effects on segments (excluding intersections) with protected bike lanes (incidence rate ratio [IRR] = 0.5 [95% confidence interval: 0.0, 2.5]) and buffered bike lanes (IRR = 0 [0,0]) were below 1, but were above 1 on conventional bike lanes (IRR = 2.8 [1.2, 6.0]) and near null on sharrows (IRR = 1.1 [0.2, 2.9]). Per intersection entry, estimated ratio effects were above 1 for entries originating from protected bike lanes (incidence proportion ratio [IPR] = 3.0 [0.0, 10.8]), buffered bike lanes (IPR = 16.2 [0.0, 53.1]), and conventional bike lanes (IPR = 3.2 [1.8, 6.0]), and were near 1 and below 1, respectively, for those originating from sharrows (IPR = 0.9 [0.2, 2.1]) and off-street paved trails (IPR = 0.7 [0.0, 2.9]). Protected bike lanes and buffered bike lanes had estimated protective effects on segments between intersections but estimated harmful effects at intersections. Conventional bike lanes had estimated harmful effects along segments and at intersections.

ImportanceChildhood poverty is associated with poor health and behavioral outcomes. The Earned Income Tax Credit (EITC), first implemented in 1975, is the largest cash transfer program for working families with low income in the US.ObjectiveTo assess whether cumulative EITC payments received during childhood are associated with the risk of criminal conviction during adolescence.Design, setting, and participantsIn this cohort study, the analytic sample consisted of US children enrolled in the 1979 National Longitudinal Study of Youth. The children were born between 1979 and 1998 and were interviewed as adolescents (age 15-19 years) between 1994 and 2016. Data analyses were performed from May 2021 to September 2022.ExposureCumulative simulated EITC received by the individual's family from birth through age 14 years.Main outcomes and measuresThe main outcome was dichotomous, self-reported conviction for a crime during adolescence (age 14-18 years). A cumulative, simulated measure of mean EITC benefits received by a child's family from birth through age 14 years was derived from federal, state, and family-size differences in EITC eligibility and payments during the study period to capture EITC benefit variation due to differences in policy parameters but not endogenous factors such as changes in household income. Logistic regression models with fixed effects for state and year and robust SEs clustered by mother estimated relative risk of adolescent conviction. Models were adjusted for state-, mother-, and child-level covariates.ResultsThe analytical sample consisted of 5492 adolescents born between 1979 and 1998; 2762 (50.3%) were male, 1648 (30.0%) were Black, 1125 (20.5%) were Hispanic, and 2719 (49.5%) were not Black or Hispanic. Each additional $1000 of EITC received during childhood was associated with an 11% lower risk of self-reported criminal conviction during adolescence (adjusted odds ratio, 0.89; 95% CI, 0.84-0.95). Adjusted risk differences were larger among boys (-14.2 self-reported convictions per 1000 population [95% CI, -22.0 to -6.3 per 1000 population]) than among girls (-6.2 per 1000 population [95% CI, -10.7 to -1.6 per 1000 population]).Conclusions and relevanceThe findings suggest that income support from the EITC may be associated with reduced youth involvement with the criminal justice system in the US. Cost-benefit analyses of the EITC should consider these longer-term and indirect outcomes.

Family- and neighborhood-level poverty are associated with youth violence. Economic policies may address this risk factor by reducing parental stress and increasing opportunities. The federal Earned Income Tax Credit (EITC) is the largest cash transfer program in the US providing support to low-income working families. Many states have additional EITCs that vary in structure and generosity. To estimate the association between state EITC and youth violence, we conducted a repeated cross-sectional analysis using the variation in state EITC generosity over time by state and self-reported data in the Youth Risk Behavior Surveillance System (YRBSS) from 2005 to 2019. We estimated the association for all youth and then stratified by sex and race and ethnicity. A 10-percentage point greater state EITC was significantly associated with 3.8% lower prevalence of physical fighting among youth, overall (PR: 0.96; 95% CI 0.94-0.99), and for male students, 149 fewer (95% CI: -243, -55) students per 10,000 experiencing physical fighting. A 10-percentage point greater state EITC was significantly associated with 118 fewer (95% CI: -184, -52) White students per 10,000 experiencing physical fighting in the past 12 months while reductions among Black students (75 fewer; 95% CI: -176, 26) and Hispanic/Latino students (14 fewer; 95% CI: -93, 65) were not statistically significant. State EITC generosity was not significantly associated with measures of violence at school. Economic policies that increase financial security and provide financial resources may reduce the burden of youth violence; further attention to their differential benefits among specific population subgroups is warranted.

Transient exposures are difficult to measure in epidemiologic studies, especially when both the status of being at risk for an outcome and the exposure change over time and space, as when measuring built-environment risk on transportation injury. Contemporary "big data" generated by mobile sensors can improve measurement of transient exposures. Exposure information generated by these devices typically only samples the experience of the target cohort, so a case-control framework may be useful. However, for anonymity, the data may not be available by individual, precluding a case-crossover approach. We present a method called at-risk-measure sampling. Its goal is to estimate the denominator of an incidence rate ratio (exposed to unexposed measure of the at-risk experience) given an aggregated summary of the at-risk measure from a cohort. Rather than sampling individuals or locations, the method samples the measure of the at-risk experience. Specifically, the method as presented samples person-distance and person-events summarized by location. It is illustrated with data from a mobile app used to record bicycling. The method extends an established case-control sampling principle: sample the at-risk experience of a cohort study such that the sampled exposure distribution approximates that of the cohort. It is distinct from density sampling in that the sample remains in the form of the at-risk measure, which may be continuous, such as person-time or person-distance. This aspect may be both logistically and statistically efficient if such a sample is already available, for example from big-data sources like aggregated mobile-sensor data.

Background and aimsPrescription drug monitoring programs (PDMP), defined as state-level databases used in the United States that collect prescribing information when controlled substances are dispensed, have varied substantially between states and over time. Little is known about the combinations of PDMP features that, collectively, may produce the greatest impact on prescribing and overdose. We aimed to (1) identify the types of PDMP models that have developed from 1999 to 2016, (2) estimate whether states have transitioned across PDMP models over time and (3) examine whether states have adopted different types of PDMP models in response to the burden of opioid overdose.MethodsA latent transition analysis of PDMP models based on an adaptation of nine PDMP characteristics classified by prescription opioid policy experts as potentially important determinants of prescribing practices and prescription opioid overdose events.ResultsWe divided the time-period into three intervals (1999-2004, 2005-09, 2010-16), and found three distinct PDMP classes in each interval. The classes in the first and second interval can be characterized as 'no/weak', 'proactive' and 'reactive' types of PDMPs, and in the third interval as 'weak', 'cooperative' and 'proactive'. The meaning of these classes changed over time: until 2009, states in the 'no/weak' class had no active PDMP, whereas states in the 'proactive' class were more likely to proactively provide unsolicited information to PDMP users, provide open access to law enforcement, and require more frequent data reporting than states in the 'reactive' class. In 2010-16, the 'weak' class resembled the 'reactive' class in previous intervals. States in the 'cooperative' class in 2010-16 were less likely than states in the 'proactive' class to provide unsolicited reports proactively or to provide open access to law enforcement; however, they were more likely than those in the 'proactive' class to share PDMP data with other states and to report more federal drug schedules.ConclusionsSince 1999, US states have tended to transition to more robust classes of prescription drug monitoring programs. Opioid overdose deaths in prior years predicted the state's prescription drug monitoring program class but did not predict transitions between prescription drug monitoring program classes over time.

Ordinary kriging, a spatial interpolation technique, is commonly used in social sciences to estimate neighborhood attributes such as physical disorder. Universal kriging, developed and used in physical sciences, extends ordinary kriging by supplementing the spatial model with additional covariates. We measured physical disorder on 1,826 sampled block faces across four U.S. cities (New York, Philadelphia, Detroit, and San Jose) using Google Street View imagery. We then compared leave-one-out cross-validation accuracy between universal and ordinary kriging and used random subsamples of our observed data to explore whether universal kriging could provide equal measurement accuracy with less spatially dense samples. Universal kriging did not always improve accuracy. However, a measure of housing vacancy did improve estimation accuracy in Philadelphia and Detroit (7.9 percent and 6.8 percent lower root mean square error, respectively) and allowed for equivalent estimation accuracy with half the sampled points in Philadelphia. Universal kriging may improve neighborhood measurement.

BackgroundSudden cardiac arrest has been linked independently both to stressful neighborhood conditions and to polymorphisms in the ADRB2 gene. The ADRB2 gene mediates sympathetic activation in response to stress. Therefore, if neighborhood conditions cause cardiac arrest through the stress pathway, the ADRB2 variant may modify the association between neighborhood conditions, such as socioeconomic deprivation and incidence of cardiac arrest.MethodsThe Cardiac Arrest Blood Study Repository is a population-based repository of specimens and other data from adult cardiac arrest patients residing in King County, Washington. Cases (n = 1,539) were 25- to 100-year-old individuals of European descent who experienced out-of-hospital cardiac arrest from 1988 to 2004. Interactions between neighborhood conditions and the ADRB2 genotype on cardiac arrest risk were assessed in a case-only study design. Gene-environment independence was assessed in blood samples obtained from King County residents initially contacted by random-digit dialing.ResultsFewer than 4% of study subjects resided in socioeconomically deprived neighborhoods. Nonetheless, the case-only analysis indicated the presence of supramultiplicative interaction of socioeconomic deprivation and the homozygous Gln27Glu variant (case-only odds ratio: 1.8 [95% confidence interval: 1.0, 2.9]). Interactions between population density and the homozygous Gln27Glu variant were weaker (case-only odds ratio: 1.2 [95% confidence interval: 0.97, 1.5]).ConclusionsFindings support a supramultiplicative interaction between the Gln27Glu ADRB2 variant and socioeconomic deprivation among individuals of European descent. This result is consistent with the hypothesis that the elevation in cardiac arrest risk associated with socioeconomic deprivation operates through the stress pathway.