Trends in Public Health

This study aimed to assess the implementation of the MDSR system. The mixed cross-sectional study was conducted in 12 regions. The quantitative analysis used ordinal intervals: Excellent, Good, Satisfactory and Poor. Qualitative analysis developed themes to inform MDR system implementation. The findings showed that leaders were following up at the health facility, the community, and individual clients. The system implementation at the regional level was Satisfactory (75–84%), and at the district and health facility level was Poor (below 75%). Generally, responses to the action plan at all levels studied were rated Poor. The timeliness of notification to MD was found to be Satisfactory (75–84%) in three regions and Poor in Dar es Salaam and Mwanza. The daily notification of MDs was 57.3% of the 349 weekly reports. The completeness variables improved significantly to Excellent scores (above 95%) after a week, but not for notifications. Therefore, the system produces data that goes beyond numbers, thereby making it not inferior. The system generates MD data through timely notifications and weekly reporting, with the latter yielding twice as much.

Background: Race-based data collection and governance remain inconsistent across Canadian health systems, limiting the visibility of African, Caribbean, and Black (ACB) communities. This paper presents a data profile of people working in or alongside ACB-serving organizations in Canada. Methods: A national cross-sectional survey captured sociodemographic characteristics, organizational affiliations, research roles, perceptions of data governance, training needs, barriers to participation, and self-rated research capacities of individuals engaged in ACB research and data governance. Eligible participants were individuals engaged in research, evaluation, data collection, data administration, or related activities in ACB-serving organizations across Canada. We conducted descriptive analysis. Results: Among 384 respondents, 45.8% lived in Ontario and 23.7% in Quebec. Most identified as female (72.1%), and 59.9% held a bachelor’s degree or higher. Many reported involvement in data collection (48.2%), data analysis (41.7%), and data management (41.4%). Training needs were frequently identified in cultural competency (48.7%), research methodology (44.3%), and data analysis (44.3%). Common barriers included lack of representation (46.1%), language challenges (41.4%), historical mistrust (37.5%), and concerns about data sovereignty (37.5%). Confidence was highest for data collection (56.4% rating 7–9) and lowest for securing research funding (34.8% rating 1–3). Conclusions: This data profile provides foundational evidence to support equitable, community-aligned data governance policy and practice within ACB-serving organizational contexts in Canada.

A novel method was applied to account for representation bias in U.S. cannabis prevalence estimates derived from the 2016–2024 Behavioral Risk Factor Surveillance System (BRFSS). Although the BRFSS provides national coverage, the cannabis module was only included in areas comprising about 19% (n = 961,366) of the total respondents. To account for differential module adoption across states, we combined observed cannabis prevalence by legal status from module-adopting areas with the distribution of legal status in non-module areas to generate adjusted national estimates. Areas that adopted the cannabis module were more likely to have legalized both recreational and medical cannabis use and less likely to have prohibited cannabis entirely. Specifically, they were 40% more likely to have recreational and medical legalization and 43% less likely to have total prohibition. Cannabis prevalence was substantially higher in legalized settings, with differences ranging from 91% (2016) to 40% (2024) compared to illegal states. Corresponding differences for medical-only legalization were 6% and 10%, respectively. Based on model-derived estimates, prevalence estimates derived from BRFSS cannabis-module data were approximately 7–8% higher than the corresponding model-based national estimates. This upward bias was more pronounced among older adults, women, Hispanics, and individuals with chronic diseases. However, temporal trends and subgroup patterns remained consistent between adjusted and unadjusted estimates. These findings suggest that while BRFSS cannabis module data may overestimate absolute national prevalence due to differential state adoption, they remain useful for tracking trends over time and across population subgroups. Adjusted estimates provide an alternative model-based framework for interpreting prevalence under different assumptions about jurisdictional composition.