Faculty Bibliography

PURPOSE/OBJECTIVE:This study estimated the prevalence of and factors associated with secondhand smoke (SHS) exposure, and assessed attitudes and knowledge about SHS among pregnant women in Cairo, Egypt. METHODS:Pregnant women in the third trimester were recruited to participate in a survey assessing tobacco smoking and SHS exposure during their current pregnancy. Participants were recruited from three antenatal clinics in Cairo, Egypt, from June 2015 to May 2016. We examined differences in sociodemographic characteristics and SHS exposure, attitudes, and knowledge by smoking/SHS status. We used multivariable ordinary least squares regression to examine the association between husbands' smoking and pregnant women's mean daily hours of SHS exposure, adjusting for women's smoking status, age group, education, and urban (vs. suburban/rural) residence. RESULTS:Of two hundred pregnant women aged 16-37 years, about two-thirds (69%) had a husband who smoked tobacco. During their current pregnancy, most women reported being non-smokers (71%), and 38% of non-smokers reported being SHS-exposed. Non-smokers exposed to SHS tended to live in more rural areas and have husbands who smoked in the home. In adjusted analyses, having a husband who smoked was significantly associated with a greater mean number of hours of SHS exposure per day exposed, and this difference was driven by husbands who smoked in the home (p < 0.001). Women in the SHS-exposed group were less likely than other groups to agree that SHS exposure was harmful to their own or their future child's health; however, all groups agreed that SHS was harmful to newborn health. CONCLUSION/CONCLUSIONS:Among our sample of pregnant women in Cairo, Egypt, there was a high rate of SHS exposure as well as misconceptions about the safety of SHS exposure to a developing fetus. Our findings suggest a need for targeted education and gender-sensitive messaging about SHS exposure, along with improved enforcement of existing tobacco control policies.

OBJECTIVE:Both diabetes and smoking significantly increase the risk of cardiovascular disease (CVD). Understanding whether a diagnosis of diabetes can be leveraged to promote smoking cessation is a gap in the literature. METHODS:We used data from the US National Health Interview Survey, 2006 to 2018, to investigate the relationship between self-report of diagnosis of diabetes and subsequent smoking abstinence among 142,884 respondents who reported regular smoking at baseline. Effect sizes were presented as hazard ratios (HRs) derived from multivariable Cox regression models adjusted for potential confounders using diabetes as a time-dependent covariate. Subgroup-specific estimates were obtained using interaction terms between diabetes and variables of interest. RESULTS:A self-reported diagnosis of diabetes was associated with smoking abstinence (HR: 1.21; 95% CI: 1.16 to 1.27). The strength of the association varied based on race (P for interaction: 0.004), where it was strongest in African Americans (HR: 1.44; 95% CI: 1.29 to 1.60); income (P for interaction <0.001), where it was strongest in those with a yearly income less than $35,000 (HR: 1.45; 95% CI: 1.36 to 1.53); and educational attainment (P for interaction <0.001), where it was strongest in those who did not attend college (HR: 1.48; 95% CI: 1.40 to 1.57). CONCLUSION/CONCLUSIONS:Among adults who smoke, a diagnosis of diabetes is significantly associated with subsequent smoking abstinence. The association is strongest in socially disadvantaged demographics, including African Americans, low-income individuals, and those who did not attend college.

BACKGROUND:Engaging researchers as research subjects is key to informing the development of effective and relevant research practices. It is important to understand how best to engage researchers as research subjects. METHODS:factorial experiment, as part of a Multiphase Optimization Strategy, was performed to evaluate effects of four recruitment strategy components on participant opening of an emailed survey link and survey completion. Participants were members of three US-based national health research consortia. A stratified simple random sample was used to assign potential survey participants to one of 16 recruitment scenarios. Recruitment strategy components were intended to address both intrinsic and extrinsic sources of motivation, including: $50 gift, $1,000 raffle, altruistic messaging, and egoistic messaging. Multivariable generalized linear regression analyses adjusting for consortium estimated component effects on outcomes. Potential interactions among components were tested. Results are reported as adjusted odds ratios (aOR) with 95% confidence intervals (95% CI). RESULTS:Surveys were collected from June to December 2023. A total of 418 participants were included from the consortia, with final analytical sample of 400 eligible participants. Out of the final sample, 82% (341) opened the survey link and 35% (147) completed the survey. Altruistic messaging increased the odds of opening the survey (aOR 2.02, 95% CI: 1.35-2.69, p = 0.033), while egoistic messaging significantly reduced the odds of opening the survey (aOR 0.56, 95%CI 0.38-0.75, p = 0.08). The receipt of egoistic messaging increased the odds of completing the survey once opened (aOR 1.81, 95%CI: 1.39-2.23, p < 0.05). There was a significant negative interaction effect between the altruistic appeal and egoistic messaging strategies for survey completion outcome. Monetary incentives did not a have a significant impact on survey completion. CONCLUSION/CONCLUSIONS:Intrinsic motivation is likely to be a greater driver of health researcher participation in survey research than extrinsic motivation. Altruistic and egoistic messaging may differentially impact initial interest and survey completion and when combined may lead to improved rates of recruitment, but not survey completion. Further research is needed to determine how to best optimize message content and whether the effects observed are modified by survey burden.

IMPORTANCE/UNASSIGNED:Virtual patient-physician communications have increased since 2020 and negatively impacted primary care physician (PCP) well-being. Generative artificial intelligence (GenAI) drafts of patient messages could potentially reduce health care professional (HCP) workload and improve communication quality, but only if the drafts are considered useful. OBJECTIVES/UNASSIGNED:To assess PCPs' perceptions of GenAI drafts and to examine linguistic characteristics associated with equity and perceived empathy. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This cross-sectional quality improvement study tested the hypothesis that PCPs' ratings of GenAI drafts (created using the electronic health record [EHR] standard prompts) would be equivalent to HCP-generated responses on 3 dimensions. The study was conducted at NYU Langone Health using private patient-HCP communications at 3 internal medicine practices piloting GenAI. EXPOSURES/UNASSIGNED:Randomly assigned patient messages coupled with either an HCP message or the draft GenAI response. MAIN OUTCOMES AND MEASURES/UNASSIGNED:PCPs rated responses' information content quality (eg, relevance), using a Likert scale, communication quality (eg, verbosity), using a Likert scale, and whether they would use the draft or start anew (usable vs unusable). Branching logic further probed for empathy, personalization, and professionalism of responses. Computational linguistics methods assessed content differences in HCP vs GenAI responses, focusing on equity and empathy. RESULTS/UNASSIGNED:A total of 16 PCPs (8 [50.0%] female) reviewed 344 messages (175 GenAI drafted; 169 HCP drafted). Both GenAI and HCP responses were rated favorably. GenAI responses were rated higher for communication style than HCP responses (mean [SD], 3.70 [1.15] vs 3.38 [1.20]; P = .01, U = 12 568.5) but were similar to HCPs on information content (mean [SD], 3.53 [1.26] vs 3.41 [1.27]; P = .37; U = 13 981.0) and usable draft proportion (mean [SD], 0.69 [0.48] vs 0.65 [0.47], P = .49, t = -0.6842). Usable GenAI responses were considered more empathetic than usable HCP responses (32 of 86 [37.2%] vs 13 of 79 [16.5%]; difference, 125.5%), possibly attributable to more subjective (mean [SD], 0.54 [0.16] vs 0.31 [0.23]; P < .001; difference, 74.2%) and positive (mean [SD] polarity, 0.21 [0.14] vs 0.13 [0.25]; P = .02; difference, 61.5%) language; they were also numerically longer (mean [SD] word count, 90.5 [32.0] vs 65.4 [62.6]; difference, 38.4%), but the difference was not statistically significant (P = .07) and more linguistically complex (mean [SD] score, 125.2 [47.8] vs 95.4 [58.8]; P = .002; difference, 31.2%). CONCLUSIONS/UNASSIGNED:In this cross-sectional study of PCP perceptions of an EHR-integrated GenAI chatbot, GenAI was found to communicate information better and with more empathy than HCPs, highlighting its potential to enhance patient-HCP communication. However, GenAI drafts were less readable than HCPs', a significant concern for patients with low health or English literacy.

BACKGROUND:During the COVID-19 pandemic, acute respiratory infection (ARI) antibiotic prescribing in ambulatory care markedly decreased. It is unclear if antibiotic prescription rates will remain lowered. METHODS:We used trend analyses of antibiotics prescribed during and after the first wave of COVID-19 to determine whether ARI antibiotic prescribing rates in ambulatory care have remained suppressed compared to pre-COVID-19 levels. Retrospective data was used from patients with ARI or UTI diagnosis code(s) for their encounter from 298 primary care and 66 urgent care practices within four academic health systems in New York, Wisconsin, and Utah between January 2017 and June 2022. The primary measures included antibiotic prescriptions per 100 non-COVID ARI encounters, encounter volume, prescribing trends, and change from expected trend. RESULTS:At baseline, during and after the first wave, the overall ARI antibiotic prescribing rates were 54.7, 38.5, and 54.7 prescriptions per 100 encounters, respectively. ARI antibiotic prescription rates saw a statistically significant decline after COVID-19 onset (step change -15.2, 95% CI: -19.6 to -4.8). During the first wave, encounter volume decreased 29.4% and, after the first wave, remained decreased by 188%. After the first wave, ARI antibiotic prescription rates were no longer significantly suppressed from baseline (step change 0.01, 95% CI: -6.3 to 6.2). There was no significant difference between UTI antibiotic prescription rates at baseline versus the end of the observation period. CONCLUSIONS:The decline in ARI antibiotic prescribing observed after the onset of COVID-19 was temporary, not mirrored in UTI antibiotic prescribing, and does not represent a long-term change in clinician prescribing behaviors. During a period of heightened awareness of a viral cause of ARI, a substantial and clinically meaningful decrease in clinician antibiotic prescribing was observed. Future efforts in antibiotic stewardship may benefit from continued study of factors leading to this reduction and rebound in prescribing rates.

Prostate-specific antigen (PSA)-based prostate cancer screening is a preference-sensitive decision for which experts recommend a shared decision making (SDM) approach. This study aimed to examine PSA screening SDM in primary care. Methods included qualitative analysis of audio-recorded patient-provider interactions supplemented by quantitative description. Participants included 5 clinic providers and 13 patients who were: (1) 40-69 years old, (2) Black, (3) male, and (4) attending clinic for routine primary care. Main measures were SDM element themes and "observing patient involvement in decision making" (OPTION) scoring. Some discussions addressed advantages, disadvantages, and/or scientific uncertainty of screening, however, few patients received all SDM elements. Nearly all providers recommended screening, however, only 3 patients were directly asked about screening preferences. Few patients were asked about prostate cancer knowledge (2), urological symptoms (3), or family history (6). Most providers discussed disadvantages (80%) and advantages (80%) of PSA screening. Average OPTION score was 25/100 (range 0-67) per provider. Our study found limited SDM during PSA screening consultations. The counseling that did take place utilized components of SDM but inconsistently and incompletely. We must improve SDM for PSA screening for diverse patient populations to promote health equity. This study highlights the need to improve SDM for PSA screening.

BACKGROUND:Meta-research in which researchers are the study subjects can illuminate how to better support researchers and enhance the development of research capacity. Comprehensively compiling the literature in this area can help define best practices for research capacity development and reveal gaps in the literature. However, there are challenges to assessing and synthesizing the breadth of the meta-research literature produced. METHODS:In this article, we discuss the current barriers to conducting literature reviews on meta-research and strategies to address these barriers. We then outline proposed methods for conducting a scoping review on meta-research with researchers as study subjects. DISCUSSION/CONCLUSIONS:Due to its interdisciplinary nature, broad scope, and difficult to pinpoint terminology, little is known about the state of meta-research with researchers as the study subjects. For this reason, there is a need for a scoping review that will identify research performed in which researchers were the study subjects.

BACKGROUND:Up to 50% of antibiotic prescriptions for upper respiratory infections (URIs) are inappropriate. Clinical decision support (CDS) systems to mitigate unnecessary antibiotic prescriptions have been implemented into electronic health records, but their use by providers has been limited. OBJECTIVE:As a delegation protocol, we adapted a validated electronic health record-integrated clinical prediction rule (iCPR) CDS-based intervention for registered nurses (RNs), consisting of triage to identify patients with low-acuity URI followed by CDS-guided RN visits. It was implemented in February 2022 as a randomized controlled stepped-wedge trial in 43 primary and urgent care practices within 4 academic health systems in New York, Wisconsin, and Utah. While issues were pragmatically addressed as they arose, a systematic assessment of the barriers to implementation is needed to better understand and address these barriers. METHODS:We performed a retrospective case study, collecting quantitative and qualitative data regarding clinical workflows and triage-template use from expert interviews, study surveys, routine check-ins with practice personnel, and chart reviews over the first year of implementation of the iCPR intervention. Guided by the updated CFIR (Consolidated Framework for Implementation Research), we characterized the initial barriers to implementing a URI iCPR intervention for RNs in ambulatory care. CFIR constructs were coded as missing, neutral, weak, or strong implementation factors. RESULTS:Barriers were identified within all implementation domains. The strongest barriers were found in the outer setting, with those factors trickling down to impact the inner setting. Local conditions driven by COVID-19 served as one of the strongest barriers, impacting attitudes among practice staff and ultimately contributing to a work infrastructure characterized by staff changes, RN shortages and turnover, and competing responsibilities. Policies and laws regarding scope of practice of RNs varied by state and institutional application of those laws, with some allowing more clinical autonomy for RNs. This necessitated different study procedures at each study site to meet practice requirements, increasing innovation complexity. Similarly, institutional policies led to varying levels of compatibility with existing triage, rooming, and documentation workflows. These workflow conflicts were compounded by limited available resources, as well as an implementation climate of optional participation, few participation incentives, and thus low relative priority compared to other clinical duties. CONCLUSIONS:Both between and within health care systems, significant variability existed in workflows for patient intake and triage. Even in a relatively straightforward clinical workflow, workflow and cultural differences appreciably impacted intervention adoption. Takeaways from this study can be applied to other RN delegation protocol implementations of new and innovative CDS tools within existing workflows to support integration and improve uptake. When implementing a system-wide clinical care intervention, considerations must be made for variability in culture and workflows at the state, health system, practice, and individual levels. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT04255303; https://clinicaltrials.gov/ct2/show/NCT04255303.

OBJECTIVES/OBJECTIVE:To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD. MATERIALS AND METHODS/METHODS:Combination symptom and demographic vignettes were developed for 27 most common symptom complaints. Standardized prompts, written from a patient perspective, with varying demographic permutations of age, sex, and race/ethnicity were entered into ChatGPT (GPT-4) between July and August 2023. In total, 3 runs of 540 ChatGPT prompts were compared to the corresponding WebMD Symptom Checker output using a mixed-methods approach. In addition to diagnostic correctness, the associated text generated by ChatGPT was analyzed for readability (using Flesch-Kincaid Grade Level) and qualitative aspects like disclaimers and demographic tailoring. RESULTS:ChatGPT matched WebMD in 91% of diagnoses, with a 24% top diagnosis match rate. Diagnostic accuracy was not significantly different across demographic groups, including age, race/ethnicity, and sex. ChatGPT's urgent care recommendations and demographic tailoring were presented significantly more to 75-year-olds versus 25-year-olds (P < .01) but were not statistically different among race/ethnicity and sex groups. The GPT text was suitable for college students, with no significant demographic variability. DISCUSSION/CONCLUSIONS:The use of non-health-tailored generative AI, like ChatGPT, for simple symptom-checking functions provides comparable diagnostic accuracy to commercially available symptom checkers and does not demonstrate significant demographic bias in this setting. The text accompanying differential diagnoses, however, suggests demographic tailoring that could potentially introduce bias. CONCLUSION/CONCLUSIONS:These results highlight the need for continued rigorous evaluation of AI-driven medical platforms, focusing on demographic biases to ensure equitable care.

BACKGROUND:We assessed longitudinal effects of e-cigarette use on respiratory symptoms in a nationally representative sample of US adults by combustible tobacco smoking status. METHODS:We analyzed Waves 4-5 public-use data from the Population Assessment of Tobacco and Health Study. Study sample included adult respondents who reported no diagnosis of respiratory diseases at Wave 4, and completed Waves 4-5 surveys with no missing data on analytic variables (N = 15,291). Outcome was a validated index of functionally important respiratory symptoms based on 7 wheezing/cough questions (range 0-9). An index score of ≥2 was defined as having important respiratory symptoms. Weighted lagged logistic regression models were performed to examine the association between e-cigarette use status at Wave 4 (former/current vs. never use) and important respiratory symptoms at Wave 5 by combustible tobacco smoking status (i.e., never/former/current smokers), adjusting for Wave 4 respiratory symptom index, sociodemographic characteristics, secondhand smoke exposure, body mass index, and chronic disease. RESULTS:Among current combustible tobacco smokers, e-cigarette use was associated with increased odds of reporting important respiratory symptoms (former e-cigarette use: adjusted odds ratio [AOR] = 1.39, 95% confidence interval [CI]: 1.07-1.81; current e-cigarette use: AOR = 1.55, 95% CI: 1.17-2.06). Among former combustible tobacco smokers, former e-cigarette use (AOR = 1.51, 95% CI: 1.06-2.15)-but not current e-cigarette use (AOR = 1.59, 95% CI: 0.91-2.78)-was associated with increased odds of important respiratory symptoms. Among never combustible tobacco smokers, no significant association was detected between e-cigarette use and important respiratory symptoms (former e-cigarette use: AOR = 1.62, 95% CI: 0.76-3.46; current e-cigarette use: AOR = 0.82, 95% CI: 0.27-2.56). CONCLUSIONS:The association between e-cigarette use and respiratory symptoms varied by combustible tobacco smoking status. Current combustible tobacco smokers who use e-cigarettes have an elevated risk of respiratory impairments.