Do no harm: the impact of implementing cancer prevention strategies on other preventive health measures.

BACKGROUND: Translational efforts to increase uptake of evidence-based practices typically look at those outcomes in isolation of their impact on other aspects of care delivery. If we are in fact to "do no harm", we must consider the possible negative impact of improving use of one practice on other quality measures. Alternatively, a focus on one practice could lead to spread of effective strategies to other practices, which would be highly beneficial. We studied the impact of a colorectal cancer (CRC) screening initiative on delivery of other preventive care measures. METHODS: We used an interrupted time series design with implementation year as the interruption point. The initiative was conducted between 2015 and 2020, with three staggered cohorts. Main outcomes were quality measures for colorectal cancer screening, cervical cancer screening, hypertension management, diabetes management, weight screening and follow-up, tobacco use screening and cessation treatment, and depression screening and follow-up. RESULTS: The initiative was associated with an increase in CRC screening (OR = 1.67, p ≤ 0.01; average marginal effect = 12.2% points), and did not reduce performance on other quality measures in the year of CRC program implementation or a change in their respective secular trends. CONCLUSIONS: The initiative led to a clinically meaningful increase in CRC screening and was not associated with reductions in delivery of six other preventive services. Quality improvement (QI) initiatives typically approach implementation with an eye towards reducing unintended impact and leveraging existing staff and resources. Implementation research studies may benefit from considering how QI initiatives factor in the local context in implementation efforts.

Bundling Colorectal Cancer Screening Outreach with Screening for Social Risk in Federally Qualified Health Centers: A Stepped-Wedge Implementation-Effectiveness Study.

BACKGROUND: Bundling is combining individual interventions to meet quality metrics. Bundling offers of cancer screening with screening for social determinants of health (SDOH) may enable health centers to assist patients with social risks and yield efficiencies. OBJECTIVE: To measure effects of bundling fecal immunochemical testing (FIT) and SDOH screening in federally qualified health centers (FQHCs). DESIGN: Clustered stepped-wedge trial. PARTICIPANTS: Four Massachusetts FQHCs randomized to implement bundled FIT-SDOH over 8-week "steps." INTERVENTION: Outreach to 50-75-year-olds overdue for CRC screening to offer FIT with SDOH screening. The implementation strategy used facilitation and training for data monitoring and reporting. MAIN MEASURES: Implementation process descriptions, data from facilitation meetings, and CRC and SDOH screening rates. Rates were compared between implementation and control FQHCs in each "step" by fitting generalized linear mixed-effects models with random intercepts for FQHCs, patients, and "step" by FQHC. KEY RESULTS: FQHCs tailored implementation processes to their infrastructure, workflows, and staffing and prioritized different groups for outreach. Two FQHCs used population health outreach, and two integrated FIT-SDOH within established programs, such as pre-visit planning. Of 34,588 patients overdue for CRC screening, 54% were female; 20% Black, 11% Latino, 10% Asian, and 47% white; 32% had Medicaid, 16% Medicare, 32% private insurance, and 11% uninsured. Odds of CRC screening completion in implementation "steps" compared to controls were higher overall and among groups prioritized for outreach (overall: adjusted odds ratio (aOR) 2.41, p = 0.005; prioritized: aOR 2.88, p = 0.002). Odds of SDOH screening did not differ across "steps." CONCLUSIONS: As healthcare systems are required to conduct more screenings, it is notable that outreach for a long-standing cancer screening requirement increased screening, even when bundled with a newer screening requirement. This outreach was feasible in a real-world safety-net clinical population and may conserve resources, especially compared to more complex or intensive outreach strategies. CLINICAL TRIALS REGISTRATION: NCT04585919.

Data sharing in the context of community-engaged research partnerships.

Over the past 20 years, the National Institutes for Health (NIH) has implemented several policies designed to improve sharing of research data, such as the NIH public access policy for publications, NIH genomic data sharing policy, and National Cancer Institute (NCI) Cancer Moonshot public access and data sharing policy. In January 2023, a new NIH data sharing policy has gone into effect, requiring researchers to submit a Data Management and Sharing Plan in proposals for NIH funding (NIH. Supplemental information to the, 2020b; NIH. Final policy for data, 2020a). These policies are based on the idea that sharing data is a key component of the scientific method, as it enables the creation of larger data repositories that can lead to research questions that may not be possible in individual studies (Alter and Gonzalez, 2018; Jwa and Poldrack, 2022), allows enhanced collaboration, and maximizes the federal investment in research. Important questions that we must consider as data sharing is expanded are to whom do benefits of data sharing accrue and to whom do benefits not accrue? In an era of growing efforts to engage diverse communities in research, we must consider the impact of data sharing for all research participants and the communities that they represent. We examine the issue of data sharing through a community-engaged research lens, informed by a long-standing partnership between community-engaged researchers and a key community health organization (Kruse et al., 2022). We contend that without effective community engagement and rich contextual knowledge, biases resulting from data sharing can remain unchecked. We provide several recommendations that would allow better community engagement related to data sharing to ensure both community and researcher understanding of the issues involved and move toward shared benefits. By identifying good models for evaluating the impact of data sharing on communities that contribute data, and then using those models systematically, we will advance the consideration of the community perspective and increase the likelihood of benefits for all.