Caroline Rudisill

We study the formation of risk perceptions— subjective probability beliefs— of three adverse events—COVID-19 contagion, influenza contagion, and food poisoning—at the onset (2020) and outset (2023) of the COVID-19 pandemic in the United States. We show that perceived risk levels for COVID-19 are similar to those for influenza, are not significantly influenced by proximity to infection, and are shaped instead by an individual’s gender, education, and employment status. Using an instrumental variable strategy, we assess whether these perceptions influence a number of protective behaviors. We find that although risk perceptions are associated with various protective behaviors, they only causally increase the likelihood of phone or online medical consultations by about a percentage point.

African Americans (AAs) tend to have unhealthy eating behaviors, less physical activity (PA) participation, and higher stress levels compared to people who are White. These variables also have bidirectional relationships. Ecological Momentary Assessment (EMA) methodology can be used to examine these relationships through repeated data collection. This narrative review aims to assess EMA studies that examine the bidirectional relationships between healthy eating, PA, and stress in AA adults. Four databases were searched for studies that met inclusion criteria. Two separate searches were completed: one for healthy eating and stress, and one for PA and stress. Two reviewers independently assessed articles to minimize bias, while resolving discrepancies through discussion. Of the 97 articles identified, four studies met inclusion criteria (one on diet, two on PA, and one on both). These studies used five daily prompts sent for seven days and found that stress was associated with daily unhealthy eating and less daily PA, and increased PA participation was associated with less daily stress among AA women. No studies examined how healthy eating impacts stress or how both healthy eating and PA can influence stress, and three of the studies only included AA women. EMA methodology is useful for exploring how stress influences healthy eating and PA, and how PA impacts stress. Given the disproportionate stress burden among AA adults, future research should utilize EMA to explore how healthy eating impacts stress and the combined impact of healthy eating and PA on stress among this population.

Objective To assess safety and healthcare utilization (HU) after metabolic/bariatric surgery (MBS) and compare it to commonly performed general surgery procedures for benign indications. Research Design and Methods Data obtained from a nationwide database (NHS Digital) on 800 consecutive patients who underwent bariatric surgery (n = 100) and seven other types of surgery (n = 100 for each) for non-acute, non-cancer indications at a tertiary hospital in England. Non-MBS procedures included adrenalectomy, gastro-esophageal junction surgery (GEJ), in-patient cholecystectomy (IC), outpatient cholecystectomy (OC), colorectal surgery (CR), hernia surgery (HS) and neck endocrine surgery (NES). Results Despite a higher number of preoperative co-morbidities and ASA score, MBS had significantly lower odds of 30-day major complications (Clavien–Dindo grade≥IIIa) compared to CR (6%;p = 0.013) and GEJ (8%;p = 0.004). No differences in complication rates with all other groups. The total number of procedure-related readmissions over 5-year after MBS were similar to HS, NES or IC and lower than CR, AS, and GEJ. The average per-patient 5-year cost of procedure-related readmissions after MBS was £790 + 337, lower than for CR (£8,270 + 3218, p < 0.001), AS (£3242 + 772, p < 0.001) and GEJ (£1705 + 976, p = 0.013) and similar to other procedures. There was a higher number of outpatient visits after MBS in the first two years after surgery, consistent with national protocols and no differences in 5-year A&E attendance among groups. Conclusions MBS is associated with a relatively low risk of post-operative complications and 5-year healthcare use compared to other commonly used types of surgical treatments. These findings could help dispel misperceptions that undermine access to effective treatment of obesity.

Program administration and implementation costs are critical for scale-up and dissemination of effective public health interventions. Costing methods withi

Importance: Health systems in the US are increasingly screening for social determinants of health (SDOH). However, guidance incorporating stakeholder feedback is limited.Objective: To examine patient and care team experiences in early implementation of SDOH screening in primary care.Design, Setting, and Participants: This qualitative study included cross-sectional analysis of SDOH screenings during primary care visits from February 22 to May 10, 2022, primary care team member interviews from July 6, 2022, to March 8, 2023, and patient stakeholder engagement on June 30, 2022. The setting was a large southeastern US health care system. Eligible patients were aged 18 years or older with completed visits in primary care.Exposure: Screening for SDOH in primary care.Main outcomes and Measures: Multivariable logistic regression evaluated patient (eg, age, race and ethnicity) and care team characteristics (eg, practice type), and screening completeness. Interviews contextualized the quantitative analysis.Results: There were 78 928 visits in practices conducting any SDOH screening. The population with visits had a mean (SD) age of 57.6 (18.1) years; 48 086 (60.9%) were female, 12 569 (15.9%) Black, 60 578 (76.8%) White, and 3088 (3.9%) Hispanic. A total of 54 611 visits (69.2%) were with a doctor of medicine and 13 035 (16.5%) with a nurse practitioner. Most had no SDOH questions answered (75 298 [95.4%]) followed by all questions (2976 [3.77%]). Logistic regression analysis found that clinician type, patient race, and primary payer were associated with screening likelihood: for clinician type, nurse practitioner (odds ratio [OR], 0.13; 95% CI, 0.03-0.62; P = .01) and physician assistant (OR, 3.11; 95% CI, 1.19-8.10; P = .02); for patient race, Asian (OR, 1.69; 95% CI, 1.25-2.28; P = .001); Black (OR, 1.49; 95% CI, 1.10-2.01; P = .009); or 2 or more races (OR, 1.48; 95% CI, 1.12-1.94; P = .006); and for primary payer, Medicaid (OR, 0.62; 95% CI, 0.48-0.80; P < .001); managed care (OR, 1.17; 95% CI, 1.07-1.29; P = .001); uninsured or with Access Health (OR, 0.26; 95% CI, 0.10-0.67; P = .005), and Tricare (OR, 0.71; 95% CI, 0.55-0.92; P = .01). Interview themes included barriers (patient hesitancy, time and resources for screening and referrals, and number of questions/content overlap) and facilitators (communication, practice champions, and support for patient needs).Conclusions and Relevance: This qualitative study presents potential guidance regarding factors that could improve SDOH screening within busy clinical workflows.

Background: Health systems are increasingly addressing patients’ social determinants of health (SDoH)-related needs and investigating their effects on health resource use. SDoH needs vary geographically; however, little is known about how this geographic variation in SDoH needs impacts the relationship between SDoH needs and health resource use.Methods: This study uses data from a SDoH survey administered to a pilot patient population in a single health system and the electronic medical records of the surveyed patients to determine if the impact of SDoH needs on emergency department use varies geospatially at the US Census block group level. A Bayesian zero-inflated negative binomial model was used to determine if emergency department visits after SDoH screening varied across block groups. Additionally, the relationships between the number of emergency department visits and the response to each SDoH screening question was assessed using Bayesian negative binomial hurdle models with spatially varying coefficients following a conditional autoregressive (CAR) model at the census block group level.Results: Statistically important differences in emergency department visits after screening were found between block groups. Statistically important spatial variation was found in the association between patient responses to the questions concerning unhealthy home environments (e.g. mold, bugs/rodents, not enough air conditioning/heat) or domestic violence/abuse and the mean number of emergency department visits after the screen.Conclusions: Notable spatial variation was found in the relationships between screening positive for unhealthy home environments or domestic violence/abuse and emergency department use. Despite the limitation of a relatively small sample size, sensitivity analyses suggest spatially varying relationships between other SDoH-related needs and emergency department use.