Effect of alternative income assistance schedules on drug use and drug-related harm: a randomised controlled trial

Abstract

Background The synchronised monthly disbursement of income assistance, whereby all recipients are paid on the same day, has been associated with increases in illicit drug use and serious associated harms. This phenomenon is often referred to as the cheque effect. Because payment variability can affect consumption patterns, this study aimed to assess whether these harms could be mitigated through a structural intervention that varied income assistance payment timing and frequency.Methods This randomised, parallel group trial was done in Vancouver, Canada, and enrolled recipients of income assistance whose drug use increased around payment days. The recipients were randomly assigned 1:2:2 to a control group that received monthly synchronised income assistance payments on government payment days, a staggered group in which participants received single desynchronised monthly income assistance payments, or a split and staggered group in which participants received desynchronised income assistance payments split into two instalments per month, 2 weeks apart, for six monthly payment cycles. Desynchronised payments in the intervention groups were made on individual payment days outside the week of the standard government schedules. Randomisation was through a pre-established stratified block procedure. Investigators and statisticians were masked to group allocation, but participants and front-line staff were not. Complete final results are reported after scheduled interim analyses and the resulting early stoppage of recruitment. Under intention-to-treat specifications, generalised linear mixed models were used to analyse the primary outcome, which was escalations in drug use, predefined as a 40% increase in at least one of: use frequency; use quantity; or number of substances used during the 3 days after government payments. Secondary analyses examined analogous drug use outcomes coinciding with individual payments as well as exposure to violence. This trial is registered with ClinicalTrials.gov, NCT02457949.

Effect of COVID-19 on Pet Food Bank Servicing: Quantifying Numbers of Clients Serviced in the Vancouver Downtown Eastside, British Columbia, Canada

“…It is possible that clients are prioritizing other needed purchases during that week and are unable to come for pet food assistance. Previous research identified that income assistance payments in the DTES population that struggles with drug addiction coincides with drug-related harms, albeit the phenomenon is highly complex and nuanced ( 26 , 26 , 48 ). With that in mind, one possible way to mobilize this data to action could be to consider increasing the services offered during the third week of each month, for instance by distributing extra food to clients, to overcome this potential barrier to care.…”

“…The area within downtown Vancouver that houses and serves ~20,000 people with many experiencing financial hardships is generally regarded as the Downtown Eastside (DTES; Figure 1 ), which is ~4 km 2 and comprises the neighborhoods of Gastown, Chinatown, and Strathcona. A study in the area which focused on drug usage found that 26% of the population examined had an overdose or “life threatening event,” and that 3% passed away between October 2015 and January 2019 ( 26 ). Anecdotally, the prevalence of companion animals in the DTES of Vancouver is high, however metrics of that population are lacking.…”

Cohort Profile: The Assessing Economic Transitions (ASSET) Study—A Community-Based Mixed-Methods Study of Economic Engagement among Inner-City Residents

“…The participants are followed using outreach and retention methods that promote interview completion and reduce loss to follow-up in the study population [ 90 ]. The outreach methods used by study staff include providing interview schedule cards and contacting participants prior to their due date to book an appointment via telephone, email, and mailed letters.…”

“…The instrument was further validated through stakeholder consultation with community members and service providers, fieldtested among prospective study participants, and refined prior to the initiation of forma recruitment. The participants are followed using outreach and retention methods that promote interview completion and reduce loss to follow-up in the study population [90]. The outreach methods used by study staff include providing interview schedule cards and contacting participants prior to their due date to book an appointment via telephone, email and mailed letters.…”

A manifesto for computational sociology: The Canadian perspective

“…Computational methods include a gamut of different techniques including machine learning (e.g., deep learning, statistical learning, reinforcement learning), social network analysis, text and data mining (e.g., sentiment analysis, topic modelling, named‐entity recognition), agent‐based modelling, more flexible regression/estimation models (e.g., regression shrinkage and selection, Bayesian statistics, spatial regression models), advances in survey methods (e.g., survey experiments, optimum design, respondent‐driven sampling), and so on. Some sociologists in Canada have contributed directly to the development of particular methods (Alexander & Alkema, 2021; Andersen, 2008; Bignami‐Van Assche et al., forthcoming; Fosse & Winship, 2019; Fox, 2015; Fox & Andersen, 2006; Fu et al., 2020, 2021; Hayduk, 1996; Li et al., forthcoming; Miles, 2016; Nelson, 2020; Stecklov et al., 2018; Wellman et al., 2003, 2020), but more often sociologists have embraced and adapted methods developed by computer scientists, statisticians, and econometricians (Abul‐Fottouh et al., 2020; Boase, 2016; Das, 2022; Gallupe et al., 2019; Gruzd & Mai, 2020; Gu et al., 2021; Hogan & Berry, 2011; Howe et al., forthcoming; Kudla & Parnaby, 2018; Letarte et al., 2021; Li & Luo, 2020; McLevey, 2022; McMahan & McFarland, 2021; Quan‐Haase et al., 2021; Richardson et al., 2021; Roth et al., forthcoming; Shor & Miltsov, 2020; Shor et al., 2013; Silver & Silva, 2021; Smith, 2020; Sytsma et al., 2021; Veenstra & Vanzella‐Yang, 2022; Yuan et al., 2022).…”