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The true risk of a COVID-19 resurgence as states reopen businesses is unknown. In this paper, we used anonymized cell-phone data to quantify the potential risk of COVID-19 transmission in business establishments by building a Business Risk Index that measures transmission risk over time. The index was built using two metrics, visits per square foot and the average duration of visits, to account for both density of visits and length of time visitors linger in the business. We analyzed trends in traffic patterns to 1,272,260 businesses across eight states from January 2020 to June 2020. We found that potentially risky traffic behaviors at businesses decreased by 30% by April. Since the end of April, the risk index has been increasing as states reopen. There are some notable differences in trends across states and industries. Finally, we showed that the time series of the average Business Risk Index is useful for forecasting future COVID-19 cases at the county-level ( P  < 0.001). We found that an increase in a county’s average Business Risk Index is associated with an increase in positive COVID-19 cases in 1 week (IRR: 1.16, 95% CI: (1.1–1.26)). Our risk index provides a way for policymakers and hospital decision-makers to monitor the potential risk of COVID-19 transmission from businesses based on the frequency and density of visits to businesses. This can serve as an important metric as states monitor and evaluate their reopening strategies.

A Correction to this paper has been published: https://doi.org/10.1038/s41746-021-00444-1

Background Research has shown that tobacco smoke contains substances of microbiological origin such as ergosterol (a fungal membrane lipid) and lipopolysaccharide (LPS) (in the outer membrane of Gram-negative bacteria). The aim of the present study was to compare the amounts of ergosterol and LPS in the tobacco and mainstream (MS) and sidestream (SS) smoke of some popular US cigarettes. Methods We measured LPS 3-hydroxy fatty acids and fungal biomass biomarker ergosterol in the tobacco and smoke from cigarettes of 11 popular brands purchased in the US. University of Kentucky reference cigarettes were also included for comparison. Results The cigarette tobacco of the different brands contained 6.88-16.17 (mean 10.64) pmol LPS and 8.27-21.00 (mean 14.05) ng ergosterol/mg. There was a direct correlation between the amounts of ergosterol and LPS in cigarette tobacco and in MS smoke collected using continuous suction; the MS smoke contained 3.65-8.23% (ergosterol) and 10.02-20.13% (LPS) of the amounts in the tobacco. Corresponding percentages were 0.30-0.82% (ergosterol) and 0.42-1.10% (LPS) for SS smoke collected without any ongoing suction, and 2.18% and 2.56% for MS smoke collected from eight two-second puffs. Conclusions Tobacco smoke is a bioaerosol likely to contain a wide range of potentially harmful bacterial and fungal components.

BackgroundLimited English proficiency (LEP) patients may be particularly vulnerable in the high acuity and fast-paced setting of the emergency department (ED).ObjectiveTo compare the care processes of LEP patients in the ED.DesignRetrospective cohort study.SettingED in a large tertiary care academic medical center.PatientsAdult LEP and English Proficient (EP) patients during their index presentation to the ED from September 1, 2013, to August 31, 2015. LEP patients were identified as those who selected a preferred language other than English when registering for care.Main MeasuresRates of diagnostic studies, admission, and return visits for those originally discharged from the ED.Key ResultsWe studied 57,435 visits of which 5241 (9.1%) were for patients with LEP. In adjusted analyses, LEP patients were more likely to receive an X-ray/ultrasound (OR 1.11, CI 1.03–1.19) and be admitted to the hospital (OR 1.09, CI 1.01–1.19). There was no difference in 72-h return visits (OR 0.98, CI 0.73–1.33). LEP patients presenting with complaints related to the cardiovascular system were more likely to receive a stress test (OR 1.51, CI 1.22–1.86), and those with gastrointestinal diagnoses were more likely to have an X-ray/ultrasound (OR 1.31, CI 1.02–1.68). In stratified analyses, Spanish speakers were less likely to be admitted (OR 0.8, CI 0.70–0.91), but those preferring “other” languages, which were all languages with < 500 patients, had a statistically significant higher adjusted rate of admission (OR 1.35, CI 1.17–1.57).ConclusionsED patients with LEP experienced both increased rates of diagnostic testing and of hospital admission. Research is needed to examine why these differences occurred and if they represent inefficiencies in care.

Background Timely primary care follow-up after acute care discharge may improve outcomes. Objective To evaluate whether post-discharge follow-up rates differ among patients discharged from hospitals directly affiliated with their primary care clinic (same-site), other hospitals within their health system (same-system), and hospitals outside their health system (outside-system). Design Retrospective cohort study. Patients Adult patients of five primary care clinics within a 14-hospital health system who were discharged home after a hospitalization or emergency department (ED) stay. Main Measures Primary care visit within 14 days of discharge. A multivariable Poisson regression model was used to estimate adjusted rate ratios (aRRs) and risk differences (aRDs), controlling for sociodemographics, acute visit characteristics, and clinic characteristics. Key Results The study included 14,310 discharges (mean age 58.4 [SD 19.0], 59.5% female, 59.5% White, 30.3% Black), of which 57.7% were from the same-site, 14.3% same-system, and 27.9% outside-system. By 14 days, 34.5% of patients discharged from the same-site hospital received primary care follow-up compared to 27.7% of same-system discharges (aRR 0.88, 95% CI 0.79 to 0.98; aRD − 6.5 percentage points (pp), 95% CI − 11.6 to − 1.5) and 20.9% of outside-system discharges (aRR 0.77, 95% CI [0.70 to 0.85]; aRD − 11.9 pp, 95% CI − 16.2 to − 7.7). Differences were greater for hospital discharges than ED discharges (e.g., aRD between same-site and outside-system − 13.5 pp [95% CI, − 20.8 to − 8.3] for hospital discharges and − 10.1 pp [95% CI, − 15.2 to − 5.0] for ED discharges). Conclusions Patients discharged from a hospital closely affiliated with their primary care clinic were more likely to receive timely follow-up than those discharged from other hospitals within and outside their health system. Improving care transitions requires coordination across both care settings and health systems.

Baseline Characteristics for Study Participants and Screened But Not Enrolled Patients Patient characteristic Study participants (n = 60) Screened but not enrolled (n = 34) P-value Female sex, n (%) 30 (50.0) 13 (38.2) 0.27 Age, mean (std) 61.8 (11.9) 62.7 (9.1) 0.55 Race, n (%) 0.27 Black 20 (33.3) 14 (41.2) White 24 (40.0) 14 (41.2) Other 16 (26.7) 4 (11.8) Hispanic, n (%) 8 (13.3) 2 (5.9) 0.26 English-only speakers, n (%) 52 (86.7) 29 (85.3) 0.83 Comorbidities, n (%) Hypertension 48 (80.0) 29 (85.3) 0.52 Hyperlipidemia 47 (78.3) 30 (88.2) 0.23 Cardiovascular disease* 12 (20.0) 8 (23.5) 0.68 Baseline quality measures, mean (std) 3.37 (1.21) 3.09 (1.56) 0.62 Baseline HbA1c, mean (std) 8.73 (1.16) 9.33 (0.86) 0.001 Social needs, n (%) Challenges maintaining a healthy diet 46 (76.7) 19 (55.8) 0.04 Challenges understanding diet’s impact on diabetes 34 (56.7) 9 (26.4) 0.005 Challenges exercising 47 (78.3) 22 (64.7) 0.15 Challenges affording medication 11 (18.3) 10 (29.4) 0.22 Challenges affording healthy food 18 (30) 7 (20.6) 0.32 Challenges affording housing and utilities 13 (21.6) 4 (11.8) 0.23 Telehealth access, n (%) 35 (58.3) 17 (50.0) 0.43 Patient portal access, n (%) 35 (58.3) 27 (79.4) 0.03 Insurance status, n (%)† Commercial 25 (41.7) 17 (50.0) 0.43 Medicare 29 (48.3) 17 (50.0) 0.88 Medicaid 2 (3.3) 0 (0.0) 0.28 MassHealth 17 (28.3) 5 (14.7) 0.13 *Cardiovascular disease includes the following conditions: myocardial infarction (STEMI or NSTEMI), coronary artery disease, stroke, cerebrovascular accident, angina (stable or unstable), and transient ischemic attack, or history of percutaneous coronary intervention or coronary artery bypass graft †Some individuals had more than one source of insurance Post-program completion, quality measure scores improved (Table 2). Impact of Intervention On Quality Measure Scores and Clinical Outcomes Outcome Before intervention After intervention P-value Quality measure score* 3.37 (1.21) 3.99 (0.89) 0.008 Microalbumin measurement or ACEI/ARB prescription 0.80 (0.40) 0.90 (0.30) 0.35 LDL measurement or statin prescription 0.63 (0.48) 0.92 (0.28) 0.007 Aspirin prescription 0.53 (0.50) 0.53 (0.50) 0.94 HbA1c measurements 1.40 (0.78) 1.65 (0.57) 0.15 Clinical outcomes HbA1c (%) 8.73 (1.16) 8.45 (1.42) 0.26 LDL (mg/dL) 90.6 (41.8) 92.9 (46.0) 0.94 Microalbumin (mg) 184 (476) 180 (470) 0.97 All data are shown as mean with standard deviation Abbreviations: ACEI angiotensin-converting enzyme inhibitor, ARB angiotensin receptor blocker, LDL low-density lipoprotein, HbA1c hemoglobin A1c *Quality measure scores range from 0 to 5 and were created based on 2021 Healthcare Effectiveness Data and Information Set (HEDIS) measures.5One point was added to the score for each of the following in the past year: urine microalbumin measurement or ACEI/ARB prescription; LDL measurement or statin prescription; aspirin prescription, and each HbA1c measurement (maximum = 2 based on HEDIS measures), where 5 was a perfect score and 0 meant that no quality measures were conducted Patient satisfaction (55% response, N = 33/60) was unchanged (P > 0.05) from national benchmarks (“Access” 75% versus 65%, “Communication” 83% versus 85%, “Care Coordination” 80% versus 74%, “Office Staff” 91% versus 79%) except in the “Showing Respect” subdomain, where our intervention improved (100% versus 83%, P < 0.001). [...]a DM telemedicine program addressing social needs shows promise and is a feasible, effective intervention. National Association of Community Health Centers website. 2020. http://www.nachc.org/research-and-data/prapare/.

Alternative methods for hospital occupancy forecasting, essential information in hospital crisis planning, are necessary in a novel pandemic when traditional data sources such as disease testing are limited. To determine whether mandatory daily employee symptom attestation data can be used as syndromic surveillance to estimate COVID-19 hospitalizations in the communities where employees live. This cohort study was conducted from April 2, 2020, to November 4, 2020, at a large academic hospital network of 10 hospitals accounting for a total of 2384 beds and 136 000 discharges in New England. The participants included 6841 employees who worked on-site at hospital 1 and lived in the 10 hospitals' service areas. Daily employee self-reported symptoms were collected using an automated text messaging system from a single hospital. Mean absolute error (MAE) and weighted mean absolute percentage error (MAPE) of 7-day forecasts of daily COVID-19 hospital census at each hospital. Among 6841 employees living within the 10 hospitals' service areas, 5120 (74.8%) were female individuals and 3884 (56.8%) were White individuals; the mean (SD) age was 40.8 (13.6) years, and the mean (SD) time of service was 8.8 (10.4) years. The study model had a MAE of 6.9 patients with COVID-19 and a weighted MAPE of 1.5% for hospitalizations for the entire hospital network. The individual hospitals had an MAE that ranged from 0.9 to 4.5 patients (weighted MAPE ranged from 2.1% to 16.1%). For context, the mean network all-cause occupancy was 1286 during this period, so an error of 6.9 is only 0.5% of the network mean occupancy. Operationally, this level of error was negligible to the incident command center. At hospital 1, a doubling of the number of employees reporting symptoms (which corresponded to 4 additional employees reporting symptoms at the mean for hospital 1) was associated with a 5% increase in COVID-19 hospitalizations at hospital 1 in 7 days (regression coefficient, 0.05; 95% CI, 0.02-0.07; P < .001). This cohort study found that a real-time employee health attestation tool used at a single hospital could be used to estimate subsequent hospitalizations in 7 days at hospitals throughout a larger hospital network in New England.

Timely primary care follow-up after acute care discharge may improve outcomes. To evaluate whether post-discharge follow-up rates differ among patients discharged from hospitals directly affiliated with their primary care clinic (same-site), other hospitals within their health system (same-system), and hospitals outside their health system (outside-system). Retrospective cohort study. Adult patients of five primary care clinics within a 14-hospital health system who were discharged home after a hospitalization or emergency department (ED) stay. Primary care visit within 14 days of discharge. A multivariable Poisson regression model was used to estimate adjusted rate ratios (aRRs) and risk differences (aRDs), controlling for sociodemographics, acute visit characteristics, and clinic characteristics. The study included 14,310 discharges (mean age 58.4 [SD 19.0], 59.5% female, 59.5% White, 30.3% Black), of which 57.7% were from the same-site, 14.3% same-system, and 27.9% outside-system. By 14 days, 34.5% of patients discharged from the same-site hospital received primary care follow-up compared to 27.7% of same-system discharges (aRR 0.88, 95% CI 0.79 to 0.98; aRD - 6.5 percentage points (pp), 95% CI - 11.6 to - 1.5) and 20.9% of outside-system discharges (aRR 0.77, 95% CI [0.70 to 0.85]; aRD - 11.9 pp, 95% CI - 16.2 to - 7.7). Differences were greater for hospital discharges than ED discharges (e.g., aRD between same-site and outside-system - 13.5 pp [95% CI, - 20.8 to - 8.3] for hospital discharges and - 10.1 pp [95% CI, - 15.2 to - 5.0] for ED discharges). Patients discharged from a hospital closely affiliated with their primary care clinic were more likely to receive timely follow-up than those discharged from other hospitals within and outside their health system. Improving care transitions requires coordination across both care settings and health systems.