Deconstructing the 'cheque effect': short-term changes in injection drug use after receiving income assistance and associated factors

Effect of modified income assistance payment schedules on substance use service access: Evidence from an experimental study

BACKGROUND

Despite being critical to reducing the impacts of poverty internationally, synchronized monthly government income assistance payments are linked to intensified drug use and associated harms, including disrupted access to substance use-related services. This study evaluates whether alternative income assistance distribution schedules improve harm reduction (HR), pharmacotherapy and substance use service utilization.

METHODS

This exploratory, parallel group, unblinded, randomized controlled trial analyzed data from adults (n = 192) in Vancouver, Canada receiving income assistance, and reporting active, regular illicit drug use. Participants were randomly assigned on a 1:2:2 basis for six income assistance payment cycles to: (1) existing government schedules (control); (2) a "staggered" single monthly payment; or (3) "split & staggered" twice-monthly payments. Generalized linear mixed models analyzed secondary outcomes of HR, pharmacotherapy and substance use service utilization as well as barriers accessing these services.

RESULTS

Forty-five control, 71 staggered, and 76 split & staggered volunteers participated between 2015 and 2019. Multivariable modified per-protocol analyses demonstrate increased access to substance use services (Adjusted Odds Ratio [AOR] 1.64, 95% Confidence Interval [CI] 1.02-2.64) for split & staggered arm participants, and, conversely, increased barriers to HR for participants in the staggered (AOR 2.34, 95% CI 1.24-4.41) and split & staggered (AOR 2.16, 95% CI 1.08-4.35) arms. Results also showed decreased barriers to pharmacotherapy around government payments (AOR 0.23, 95% CI 0.06-0.90), pharmacotherapy around individual payments (AOR 0.12, 95% CI 0.02-0.58), and HR around individual payments (AOR 0.11, 95% CI 0.02-0.63) for staggered arm participants.

CONCLUSION

Modifying payments schedules demonstrate improved access to overall substance use services, and reduced barriers to HR and pharmacotherapy around income assistance payments. However, increased overall barriers to HR access were also shown. These complex, predominantly beneficial findings support the exploration of offering alternative payment schedules to support service access.

The leading neighborhood-level predictors of drug overdose: A mixed machine learning and spatial approach

BACKGROUND

Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy during recent years. To combat this health issue, this study aims to identify the leading neighborhood-level predictors of drug overdose and develop a model to predict areas at the highest risk of drug overdose using geographic information systems and machine learning (ML) techniques.

METHOD

Neighborhood-level (block group) predictors were grouped into three domains: socio-demographic factors, drug use variables, and protective resources. We explored different ML algorithms, accounting for spatial dependency, to identify leading predictors in each domain. Using geographically weighted regression and the best-performing ML algorithm, we combined the output prediction of three domains to produce a final ensemble model. The model performance was validated using classification evaluation metrics, spatial cross-validation, and spatial autocorrelation testing.

RESULTS

The variables contributing most to the predictive model included the proportion of households with food stamps, households with an annual income below $35,000, opioid prescription rate, smoking accessories expenditures, and accessibility to opioid treatment programs and hospitals. Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross-validation. The ensemble model using ML outperformed the GWR method.

CONCLUSION

This study identified strong neighborhood-level predictors that place a community at risk of experiencing drug overdoses, as well as protective factors. Our findings may shed light on several specific avenues for targeted intervention in neighborhoods at risk for high drug overdose burdens.