Surveillance-based informative testing for detection and containment of SARS-CoV-2 outbreaks on a public university campus: an observational and modelling study.

BACKGROUND: Despite severe outbreaks of COVID-19 among colleges and universities across the USA during the Fall 2020 semester, the majority of institutions did not routinely test students. While high-frequency repeated testing is considered the most effective strategy for disease mitigation, most institutions do not have the necessary infrastructure or funding for implementation. Therefore, alternative strategies for testing the student population are needed. Our study detailed the implementation and results of testing strategies to mitigate SARS-CoV-2 spread on a university campus, and we aimed to assess the relative effectiveness of the different testing strategies. METHODS: For this retrospective cohort study, we included 6273 on-campus students arriving to a large public university in the rural USA (Clemson, SC, USA) for in-person instruction in the Fall 2020 semester (Sept 21 to Nov 25). Individuals arriving after Sept 23, those who tested positive for SARS-CoV-2 before Aug 19, and student athletes and band members were not included in this study. We implemented two testing strategies to mitigate SARS-CoV-2 spread during this period: a novel surveillance-based informative testing (SBIT) strategy, consisting of random surveillance testing to identify outbreaks in residence hall buildings or floors and target them for follow-up testing (Sept 23 to Oct 5); followed by a repeated weekly surveillance testing (Oct 6 to Nov 22). Relative changes in estimated weekly prevalence were examined. We developed SARS-CoV-2 transmission models to compare the relative effectiveness of weekly testing (900 daily surveillance tests), SBIT (450 daily surveillance tests), random surveillance testing (450 daily surveillance tests), and voluntary testing (0 daily surveillance tests) on disease mitigation. Model parameters were based on our empirical surveillance data in conjunction with published sources. FINDINGS: SBIT was implemented from Sept 23 to Oct 5, and identified outbreaks in eight residence hall buildings and 45 residence hall floors. Targeted testing of residence halls was 2·03 times more likely to detect a positive case than random testing (95% CI 1·67-2·46). Weekly prevalence was reduced from a peak of 8·7% to 5·6% during this 2-week period, a relative reduction of 36% (95% CI 27-44). Prevalence continued to decrease after implementation of weekly testing, reaching 0·8% at the end of in-person instruction (week 9). SARS-CoV-2 transmission models concluded that, in the absence of SBIT (ie, voluntary testing only), the total number of COVID-19 cases would have increased by 154% throughout the semester. Compared with SBIT, random surveillance testing alone would have resulted in a 24% increase in COVID-19 cases. Implementation of weekly testing at the start of the semester would have resulted in 36% fewer COVID-19 cases throughout the semester compared with SBIT, but it would require twice the number of daily tests. INTERPRETATION: It is imperative that institutions rigorously test students during the 2021 academic year. When high-frequency testing (eg, weekly) is not possible, SBIT is an effective strategy to mitigate disease spread among the student population that can be feasibly implemented across colleges and universities. FUNDING: Clemson University, USA.

Rates of perfect self-reported adherence to direct-acting antiviral therapy and its correlates among people who inject drugs on medications for opioid use disorder: The PREVAIL study.

Adequate adherence to direct-acting antivirals (DAAs) for hepatitis C virus (HCV) is critical to attaining sustained virologic response (SVR). In this PREVAIL study's secondary analyses, we explored the association between self-reported and objective DAAs adherence among a sample of people who inject drugs (PWID) receiving medications for opioid use disorder (MOUD) (N = 147). Self-reported adherence was recoded 3 times during treatment (weeks 4, 8 and 12) using a visual analog scale (VAS), whereas objective adherence was collected continuously during treatment using electronic blister packs. Participants who reported being perfectly adherent had significantly higher blister pack adherence in each period (weeks 4, 8 and 12; ps < .05) and over the 12-week study (p < .001) compared to those who reported being non-perfectly adherent. Whites were more likely to report perfect adherence (91.7%) than Blacks (48.7%), Latinos (52.2%) and other (75.0%) race groups. Participants who reported recent use of cocaine (63.9%) or polysubstance use (60.0%) and those who had a positive result for cocaine (62.8%) were more likely to be non-perfectly adherent, although none of these factors were associated with blister pack adherence. This study showed that the VAS could serve as a reliable option for assessing DAAs adherence among PWID on MOUD. The implementation of VAS may be an ideal option for monitoring adherence among PWID on MOUD, especially in clinical settings with limited resources. PWID on MOUD who are Black or other races than White, as well as those who report recent cocaine or polysubstance use may require additional support to maintain optimal DAA adherence.

Factors influencing lung cancer screening completion following participation in shared decision-making: A retrospective study in a U.S. academic health system.

PURPOSE: Shared decision making (SDM) between patients and designated health professionals is recommended by several professional organizations prior to lung cancer screening by low dose CT (LDCT). This study seeks to identify factors, including characteristics of patients and referring clinicians, that influence LDCT screening completion following participation in SDM. MATERIALS AND METHODS: This retrospective study consisted of n = 171 patients eligible for LDCT screening and who participated in SDM between 2016 and 2017 in one of two sites in Prisma Health, an academic health care delivery system in South Carolina. Patient characteristics included age, sex, race, body mass index, marital status, insurance, smoking status and history, family history of lung cancer, SDM site, and distance to screening site. Characteristics of referred clinicians included age, sex, race, specialty, years of practice, education, and residency. Descriptive statistics and multivariable generalized linear mixed models were used to compare effects of patient and referring clinician characteristics on LDCT completion. RESULTS: A total of 152 patients (89%) completed LDCT screening after participation in SDM. SDM site (p = 0.02), longer distances to the screening site (p = 0.03), referrals from internal medicine clinicians (p = 0.03), and referrals from younger clinicians (p = 0.01) and from those with less years of experience (p = 0.02) were significantly associated with a lower likelihood of screening completion. CONCLUSIONS: Several factors significantly associated with screening completion were identified. This information can assist with development of interventions to improve communication and decision-making between patients, clinicians, and SDM health professionals, and inform design of targeted decision aids embedded into SDM procedures.

Enhancing Patient Activation and Self-Management Activities in Patients With Type 2 Diabetes Using the US Department of Defense Mobile Health Care Environment: Feasibility Study.

BACKGROUND: Past mobile health (mHealth) efforts to empower type 2 diabetes (T2D) self-management include portals, text messaging, collection of biometric data, electronic coaching, email, and collection of lifestyle information. OBJECTIVE: The primary objective was to enhance patient activation and self-management of T2D using the US Department of Defense's Mobile Health Care Environment (MHCE) in a patient-centered medical home setting. METHODS: A multisite study, including a user-centered design and a controlled trial, was conducted within the US Military Health System. Phase I assessed preferences regarding the enhancement of the enabling technology. Phase II was a single-blinded 12-month feasibility study that randomly assigned 240 patients to either the intervention (n=123, received mHealth technology and behavioral messages tailored to Patient Activation Measure [PAM] level at baseline) or the control group (n=117, received equipment but not messaging. The primary outcome measure was PAM scores. Secondary outcome measures included Summary of Diabetes Self-Care Activities (SDSCA) scores and cardiometabolic outcomes. We used generalized estimating equations to estimate changes in outcomes. RESULTS: The final sample consisted of 229 patients. Participants were 61.6% (141/229) male, had a mean age of 62.9 years, mean glycated hemoglobin (HbA1c) of 7.5%, mean BMI of 32.7, and a mean duration of T2D diagnosis of 9.8 years. At month 12, the control group showed significantly greater improvements compared with the intervention group in PAM scores (control mean 7.49, intervention mean 1.77; P=.007), HbA1c (control mean -0.53, intervention mean -0.11; P=.006), and low-density lipoprotein cholesterol (control mean -7.14, intervention mean 4.38; P=.01). Both groups showed significant improvement in SDSCA, BMI, waist size, and diastolic blood pressure; between-group differences were not statistically significant. Except for patients with the highest level of activation (PAM level 4), intervention group patients exhibited significant improvements in PAM scores. For patients with the lowest level of activation (PAM level 1), the intervention group showed significantly greater improvement compared with the control group in HbA1c (control mean -0.09, intervention mean -0.52; P=.04), BMI (control mean 0.58, intervention mean -1.22; P=.01), and high-density lipoprotein cholesterol levels (control mean -4.86, intervention mean 3.56; P<.001). Significant improvements were seen in AM scores, SDSCA, and waist size for both groups and in diastolic and systolic blood pressure for the control group; the between-group differences were not statistically significant. The percentage of participants who were engaged with MHCE for ≥50% of days period was 60.7% (68/112; months 0-3), 57.4% (62/108; months 3-6), 49.5% (51/103; months 6-9), and 43% (42/98; months 9-12). CONCLUSIONS: Our study produced mixed results with improvement in PAM scores and outcomes in both the intervention and control groups. Structural design issues may have hampered the influence of tailored behavioral messaging within the intervention group. TRIAL REGISTRATION: ClinicalTrials.gov NCT02949037; https://clinicaltrials.gov/ct2/show/NCT02949037. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.6993.