Search

BackgroundThe Cancer Center Cessation Initiative (C3I) is a National Cancer Institute (NCI) Cancer Moonshot Program that supports NCI-designated cancer centers developing tobacco treatment programs for oncology patients who smoke. C3I-funded centers implement evidence-based programs that offer various smoking cessation treatment components (e.g., counseling, Quitline referrals, access to medications). While evaluation of implementation outcomes in C3I is guided by evaluation of reach and effectiveness (via RE-AIM), little is known about technical efficiency-i.e., how inputs (e.g., program costs, staff time) influence implementation outcomes (e.g., reach, effectiveness). This study demonstrates the application of data envelopment analysis (DEA) as an implementation science tool to evaluate technical efficiency of C3I programs and advance prioritization of implementation resources.MethodsDEA is a linear programming technique widely used in economics and engineering for assessing relative performance of production units. Using data from 16 C3I-funded centers reported in 2020, we applied input-oriented DEA to model technical efficiency (i.e., proportion of observed outcomes to benchmarked outcomes for given input levels). The primary models used the constant returns-to-scale specification and featured cost-per-participant, total full-time equivalent (FTE) effort, and tobacco treatment specialist effort as model inputs and reach and effectiveness (quit rates) as outcomes.ResultsIn the DEA model featuring cost-per-participant (input) and reach/effectiveness (outcomes), average constant returns-to-scale technical efficiency was 25.66 (SD = 24.56). When stratified by program characteristics, technical efficiency was higher among programs in cohort 1 (M = 29.15, SD = 28.65, n = 11) vs. cohort 2 (M = 17.99, SD = 10.16, n = 5), with point-of-care (M = 33.90, SD = 28.63, n = 9) vs. no point-of-care services (M = 15.59, SD = 14.31, n = 7), larger (M = 33.63, SD = 30.38, n = 8) vs. smaller center size (M = 17.70, SD = 15.00, n = 8), and higher (M = 29.65, SD = 30.99, n = 8) vs. lower smoking prevalence (M = 21.67, SD = 17.21, n = 8).ConclusionMost C3I programs assessed were technically inefficient relative to the most efficient center benchmark and may be improved by optimizing the use of inputs (e.g., cost-per-participant) relative to program outcomes (e.g., reach, effectiveness). This study demonstrates the appropriateness and feasibility of using DEA to evaluate the relative performance of evidence-based programs.

This study examined nicotine and cannabis vaping among adolescents in treatment for substance use disorders. Participants were 363 adolescents aged 12-17 (66% male, mean age = 15.5 [SD = 1.3], 46% non-Hispanic white) seen for a specialty addiction intake evaluation between 2017 and 2019 at one of six medical offices of a large, integrated health care system in Northern California. Multivariable logistic regression models tested for associations of sociodemographics, cigarette smoking, and substance use disorders with vaping behaviors. A majority of adolescents reported ever (68%) or current vaping (60%) of nicotine and/or cannabis; current vaping was similar for nicotine (50%) and cannabis (51%); 40% reported current vaping of both. Current smokers (6% of the sample) had higher odds of ever vaping (aOR = 3.95, 95%CI: 1.04-14.95). Black (versus non-Hispanic white) adolescents had lower odds of current nicotine vaping (aOR = 0.08, 95%CI: 0.02-0.37) and current vaping of both nicotine and cannabis (aOR = 0.12, 95%CI: 0.03-0.60). Having an alcohol use disorder was associated with current vaping (aOR = 2.14, 95%CI: 1.06-4.33). Those who endorsed that most friends get drunk/high (aOR = 1.87, 95%CI: 1.02-3.42) or that cannabis was their substance of choice (aOR = 2.36, 95%CI: 1.16-4.81) had higher odds of current cannabis vaping. Higher neighborhood household income ($80,000-$120,000 and >$120,000 vs.

BackgroundThe Cancer Center Cessation Initiative (C3I) was launched in 2017 as a part of the NCI Cancer Moonshot program to assist NCI-designated cancer centers in developing tobacco treatment programs for oncology patients. Participating centers have implemented varied evidence-based programs that fit their institutional resources and needs, offering a wide range of services including in-person and telephone-based counseling, point of care, interactive voice response systems, referral to the quitline, text- and web-based services, and medications.MethodsWe used a mixed methods comparative case study design to evaluate system-level implementation costs across 15 C3I-funded cancer centers that reported for at least one 6-month period between July 2018 and June 2020. We analyzed operating costs by resource category (e.g., personnel, medications) concurrently with transcripts from semi-structured key-informant interviews conducted during site visits. Personnel salary costs were estimated using Bureau of Labor Statistics wage data adjusted for area and occupation, and non-wage benefits. Qualitative findings provided additional information on intangible resources and contextual factors related to implementation costs.ResultsMedian total monthly operating costs across funded centers were $11,045 (range: $5129-$20,751). The largest median operating cost category was personnel ($10,307; range: $4122-$19,794), with the highest personnel costs attributable to the provision of in-person program services. Monthly (non-zero) cost ranges for other categories were medications ($17-$573), materials ($6-$435), training ($96-$516), technology ($171-$2759), and equipment ($10-$620). Median cost-per-participant was $466 (range: $70-$2093) and cost-per-quit was $2688 (range: $330-$9628), with sites offering different combinations of program components, ranging from individually-delivered in-person counseling only to one program that offered all components. Site interviews provided context for understanding variations in program components and their cost implications.ConclusionsAmong most centers that have progressed in tobacco treatment program implementation, cost-per-quit was modest relative to other prevention interventions. Although select centers have achieved similar average costs by offering program components of various levels of intensity, they have varied widely in program reach and effectiveness. Evaluating implementation costs of such programs alongside reach and effectiveness is necessary to provide decision makers in oncology settings with the important additional information needed to optimize resource allocation when establishing tobacco treatment programs.

The Affordable Care Act (ACA) promised to narrow smoking disparities by expanding access to healthcare and mandating comprehensive coverage for tobacco treatment starting in 2014. We examined whether two years after ACA implementation disparities in receiving clinician advice to quit and smokers' knowledge and use of treatment resources remained. We conducted telephone interviews in 2016 with a stratified random sample of self-reported smokers newly enrolled in the Kaiser Permanente Northern California's (KPNC) integrated healthcare delivery system in 2014 (N = 491; 50% female; 53% non-white; 6% Spanish language). We used Poisson regression with robust standard errors to test whether sociodemographics, insurance type, comorbidities, smoking status in 2016 (former, light/nondaily [

Background/Objectives: Tobacco use remains a significant public health issue, particularly among individuals with low incomes, including Medicaid recipients who often face multiple barriers to quitting. This study aimed to identify barriers, from the perspective of Medicaid managed care organizations (MCOs), influencing Medicaid recipient participation in tobacco cessation programs. Methods: Focus group interviews were conducted with Florida Medicaid MCOs to elicit processes for case identification, outreach, referral, program participation, and incentives. Answers were synthesized into themes. Results: Medicaid recipients were primarily identified through nicotine dependency claim codes or Health Risk Assessments (HRAs). Individuals were referred to state and local community tobacco cessation programs through text messaging and outreach by MCO case managers. The MCOs identified the following as barriers: primary care physicians (PCPs) with limited knowledge about cessation programs and pharmacologic treatments for nicotine dependence, low availability of health coaches, long wait times for entry into cessation programs, weak coordination between MCOs and cessation programs, and insufficient incentives for individuals for program participation. Suggested strategies to overcome barriers were continuing medical education (CME) for PCPs about tobacco cessation programs and prescription therapies, increasing the training of health coaches, more investment in quitlines, increasing data sharing between MCOs and cessation programs, and increasing incentives for individuals. Conclusions: These findings highlight the importance of engaging MCOs in discussions about policy and program improvements, as their insights can drive meaningful changes in how tobacco cessation and other preventive health programs are structured and implemented. Targeted interventions are needed to enhance tobacco cessation program participation among Medicaid recipients. [ABSTRACT FROM AUTHOR]

Objectives Examine electronic health record (EHR) use and factors contributing to documentation burden in acute and critical care nurses. Materials and Methods A mixed-methods design was used guided by Unified Theory of Acceptance and Use of Technology. Key EHR components included, Flowsheets , Medication Administration Records (MAR), Care Plan , Notes , and Navigators. We first identified 5 units with the highest documentation burden in 1 university hospital through EHR log file analyses. Four nurses per unit were recruited and engaged in interviews and surveys designed to examine their perceptions of ease of use and usefulness of the 5 EHR components. A combination of inductive/deductive coding was used for qualitative data analysis. Results Nurses acknowledged the importance of documentation for patient care, yet perceived the required documentation as burdensome with levels varying across the 5 components. Factors contributing to burden included non-EHR issues (patient-to-nurse staffing ratios; patient acuity; suboptimal time management) and EHR usability issues related to design/features. Flowsheets , Care Plan , and Navigators were found to be below acceptable usability and contributed to more burden compared to MAR and Notes. The most troublesome EHR usability issues were data redundancy, poor workflow navigation, and cumbersome data entry based on unit type. Discussion Overall, we used quantitative and qualitative data to highlight challenges with current nursing documentation features in the EHR that contribute to documentation burden. Differences in perceived usability across the EHR documentation components were driven by multiple factors, such as non-alignment with workflows and amount of duplication of prior data entries. Nurses offered several recommendations for improving the EHR, including minimizing redundant or excessive data entry requirements, providing visual cues (eg, clear error messages, highlighting areas where missing or incorrect information are), and integrating decision support. Conclusion Our study generated evidence for nurse EHR use and specific documentation usability issues contributing to burden. Findings can inform the development of solutions for enhancing multi-component EHR usability that accommodates the unique workflow of nurses. Documentation strategies designed to improve nurse working conditions should include non-EHR factors as they also contribute to documentation burden. [ABSTRACT FROM AUTHOR]

Use of electronic nicotine delivery systems (ENDS) has increased substantially over the past decade. However, unlike smoking, which is systematically captured by clinicians through routine screening and discrete documentation fields in the electronic health record (EHR), unknown is the extent to which clinicians are documenting patients' use of ENDS. Data were gathered from medical visits with patients aged 12 and older (N=9,119; 55% male) treated in a large, integrated healthcare system. We used natural language processing to assess the incidence rates of clinician documentation of patients' ENDS use in unstructured tobacco comments in the EHR, and the words most frequently documented in relation to ENDS, from 2006-2015. ENDS documentation in the EHR increased dramatically over time (from 0.01 to 9.5 per 10,000 patients, p

INTRODUCTION: Sodium‐glucose cotransporter 2 (SGLT2) inhibitors exhibit potential benefits in reducing dementia risk, yet the optimal beneficiary subgroups remain uncertain. METHODS: Individuals with type 2 diabetes (T2D) initiating either SGLT2 inhibitor or sulfonylurea were identified from OneFlorida+ Clinical Research Network (2016–2022). A doubly robust learning was deployed to estimate risk difference (RD) and 95% confidence interval (CI) of all‐cause dementia. RESULTS: Among 35,458 individuals with T2D, 1.8% in the SGLT2 inhibitor group and 4.7% in the sulfonylurea group developed all‐cause dementia over a 3.2‐year follow‐up, yielding a lower risk for SGLT2 inhibitors (RD, –2.5%; 95% CI, –3.0% to –2.1%). Hispanic ethnicity and chronic kidney disease were identified as the two important variables to define four subgroups in which RD ranged from –4.3% (–5.5 to –3.2) to –0.9% (–1.9 to 0.2). DISCUSSION: Compared to sulfonylureas, SGLT2 inhibitors were associated with a reduced risk of all‐cause dementia, but the association varied among different subgroups. Highlights: New users of sodium‐glucose cotransporter 2 (SGLT2) inhibitors were significantly associated with a lower risk of all‐cause dementia as compared to those of sulfonylureas.The association varied among different subgroups defined by Hispanic ethnicity and chronic kidney disease.A significantly lower risk of Alzheimer's disease and vascular dementia was observed among new users of SGLT2 inhibitors compared to those of sulfonylureas. [ABSTRACT FROM AUTHOR]