Centering Health Equity Within COVID-19 Contact Tracing: Connecticut's Community Outreach Specialist Program.

CONTEXT: The COVID-19 pandemic has disproportionately impacted vulnerable populations, including those who are non-English-speaking and those with lower socioeconomic status; yet, participation of these groups in contact tracing was initially low. Distrust of government agencies, anticipated COVID-19-related stigma, and language and cultural barriers between contact tracers and communities are common challenges. PROGRAM: The Community Outreach Specialist (COS) program was established within the Connecticut Department of Public Health (DPH) COVID-19 contact tracing program to encourage participation in contact tracing and address a need for culturally competent care and social and material support among socially vulnerable and non-English-speaking populations in 11 high-burden jurisdictions in Connecticut. IMPLEMENTATION: In partnership with state and local health departments, we recruited 25 COS workers with relevant language skills from target communities and trained them to deliver contact tracing services to vulnerable and non-English speaking populations. EVALUATION: We conducted a cross-sectional analysis using data from ContaCT, DPH's enterprise contact tracing system. Overall, the COS program enrolled 1938 cases and 492 contacts. The proportion of residents reached (ie, called and interviewed) in the COS program was higher than that in the regular contact tracing program for both cases (70% vs 57%, P < .001) and contacts (84% vs 64%, P < .001). After adjusting for client age, sex, race and ethnicity, language, and jurisdiction, we found that the COS program was associated with increased reach for contacts (odds ratio [OR] = 1.52; 95% confidence interval [95% CI], 1.17-1.99) but not for cases (OR = 0.78; 95% CI, 0.70-0.88). Rapid qualitative analysis of programmatic field notes and meeting reports provided evidence that the COS program was feasible and acceptable to clients and contributed to COVID-19 education and communication efforts. CONCLUSION: A COS program employing a client-centered, community-engaged strategy for reaching vulnerable and non-English-speaking populations was feasible and more effective at reaching contacts than standard COVID-19 contact tracing.

Association Between Utilization of Quitline Services and Probability of Tobacco Abstinence in Low-Income Smokers.

INTRODUCTION: Quitlines (QL) are an effective means for smoking cessation, but a paucity of data exist examining the dose-response relationship between use of QL services and quit rates, especially among low-income smokers. The purpose of this study was to study the relationship between tobacco abstinence and use of QL services among low-income smokers. METHODS: Secondary analysis of a randomized trial of every- or some-day smokers aged 18 years or older visiting an urban emergency department. Inclusion criteria included self-pay or Medicaid insurance, as a proxy for low-income and low socioeconomic status. Intervention participants received a motivational interview, 6 weeks of nicotine patches and gum, a referral faxed to the state-sponsored QL, a booster call, and a quitline brochure. Control participants received the brochure. Smoking status was assessed by phone at 1 and 3 months, with confirmation via exhaled carbon monoxide testing at 3 months for those reporting abstinence. QL usage was obtained by utilization data from the QL database. RESULTS: Of 778 subjects, 197 (25.3%) reported any use of QL services at 3 months. Participants were trichotomized: no QL usage, 1 call only, and >1 call (583, 99, and 98 participants, respectively). Quit rates at 3 months in these no, low-, and high-use groups were, respectively, 7.2%, 9.1%, and 15.3% (P=0.03). Participants who used the QL had a median of 28 total minutes of telephone contact. CONCLUSION: Among low-income smokers, greater use of QL services is associated with higher abstinence. Whether this resulted from a direct effect of the QL, or greater motivation among smokers using QL services cannot be determined from these data.