Changing practice patterns caused by the pandemic have created an urgent need for guidance in prescribing stimulants using telepsychiatry for attention-deficit hyperactivity disorder (ADHD). A notable spike in the prescribing of stimulants accompanied the suspension of the Ryan Haight Act, allowing the prescribing of stimulants without a face-to-face meeting. Competing forces both for and against prescribing ADHD stimulants by telepsychiatry have emerged, requiring guidelines to balance these factors. On the one hand, factors weighing in favor of increasing the availability of treatment for ADHD via telepsychiatry include enhanced access to care, reduction in the large number of untreated cases, and prevention of the known adverse outcomes of untreated ADHD. On the other hand, factors in favor of limiting telepsychiatry for ADHD include mitigating the possibility of exploiting telepsychiatry for profit or for misuse, abuse, and diversion of stimulants. This Expert Consensus Group has developed numerous specific guidelines and advocates for some flexibility in allowing telepsychiatry evaluations and treatment without an in-person evaluation to continue. These guidelines also recognize the need to give greater scrutiny to certain subpopulations, such as young adults without a prior diagnosis or treatment of ADHD who request immediate-release stimulants, which should increase the suspicion of possible medication diversion, misuse, or abuse. In such cases, nonstimulants, controlled-release stimulants, or psychosocial interventions should be prioritized. We encourage the use of outside informants to support the history, the use of rating scales, and having access to a hybrid model of both in-person and remote treatment.

Background: Multimodal approaches are often used to prevent transmission of antimicrobial-resistant pathogens among patients in healthcare settings; understanding the effect of individual interventions is challenging. We designed a model to compare the effectiveness of hand hygiene (HH) with or without decolonization in reducing patient colonization with carbapenem-resistant Enterobacterales (CRE). Methods: We developed an agent-based model to represent transmission of CRE in an acute-care hospital comprising 3 general wards and 2 ICUs, each with 20 single-occupancy rooms, located in a community of 85,000 people. The model accounted for the movement of healthcare personnel (HCP), including their visits to patients. CRE dynamics were modeled using a susceptible–infectious–susceptible framework with transmission occurring via HCP–patient contacts. The mean time to clearance of CRE colonization without intervention was 387 days (Zimmerman et al, 2013). Our baseline included a facility-level HH compliance of 30%, with an assumed efficacy of 50%. Contact precautions were employed for patients with CRE-positive cultures with assumed adherence and efficacy of 80% and 50%, respectively. Intervention scenarios included decolonization of culture-positive CRE patients, with a mean time to decolonization of 3 days. We considered 2 hypothetical intervention scenarios: (A) decolonization of patients with the baseline HH compliance and (B) decolonization with a slightly improved HH compliance of 35%. The hospital-level CRE incidence rate was used to compare the results from these intervention scenarios. Results: CRE incidence rates were lower in intervention scenarios than the baseline scenario (Fig. 1). The baseline mean incidence rate was 29.1 per 10,000 patient days. For decolonization with the baseline HH, the mean incidence rate decreased to 14.5 per 10,000 patient days, which is a 50.2% decrease relative to the baseline incidence (Table 1). The decolonization scenario with a slightly improved HH compliance of 35% produced a relative reduction of 71.9% relative to the baseline incidence. Conclusions: Our analysis shows that decolonization, combined with modest improvement in HH compliance, could lead to large decreases in pathogen transmission. In turn, this model implies that efforts to identify and improve decolonization strategies for better patient safety in health care may be needed and are worth exploring.

Disclosures: None

Background: Pathogen transmission among staff and residents in nursing homes can vary depending on their interactions and by the amount of time a resident receives care in the facility. Understanding the relative differences in transmission rates between and among staff and residents can identify the pathways that contributed most to the spread of SARS-CoV-2 in US nursing homes. Further exploring relative differences by categorizing facilities by residents’ lengths of stay can identify priority categories for intervention. Methods: Using US National Healthcare Safety Network (NHSN) surveillance data on resident and staff cases, vaccination, and resident deaths during June 2020–June 2022, we estimated SARS-CoV-2 transmission among and between residents and staff. We used a Bayesian inversion of a susceptible–exposed–infected–removed–virus–death (SEIRVD) compartmental model to produce the estimates. The facilities were divided into those with median length of stay (LOS) among the residents of 10 weeks. Additional inputs included the incidence and vaccination levels of the county where each facility was located. For the compartmental model, all data were averaged to form a representative facility for each category. Transmission was estimated separately for 3 periods: (1) June 2020–March 2021 as before the SARS-CoV-2 delta variant, (2) April 2021—October 2021 during SARS-CoV-2 delta variant dominance, and (3) November 2021—June 2022 during the prevalence of the SARS-CoV-2 omicron variant. Results: Regardless of facility category, transmission was highest from staff to residents or resident to resident (Fig.). These estimates of transmission were highest during the pre–SARS-CoV-2 delta variant phase. Transmission in that phase was highest in the facilities with LOS >10 weeks from staff to residents at 0.88 per week (95% credible interval [CrI], 0.06–1.85), in the facilities with LOS 6–10 weeks from staff to residents at 0.68 per week (95% CrI, 0.03–1.78), and in the facilities with LOS <6 weeks between residents at 0.47 per week (95% CrI, 0.02–0.95). Conclusions: Staff-to-resident or resident-to-resident transmission were the dominant pathways of spread of SARS-CoV-2 across the periods or the facility categories. Facilities with LOS 6 weeks or longer had higher median transmission estimates across the periods and transmission routes compared to facilities with LOS less than 6 weeks, implying that when prioritization of intervention resources is needed, facilities caring for populations with longer stays could be prioritized.

Disclosures: None

Background: Healthcare-associated infections (HAIs) are a major global threat to patient safety. Systematic surveillance is crucial for understanding HAI rates and antimicrobial resistance trends and to guide infection prevention and control (IPC) activities based on local epidemiology. In India, no standardized national HAI surveillance system was in place before 2017. Methods: Public and private hospitals from across 21 states in India were recruited to participate in an HAI surveillance network. Baseline assessments followed by trainings ensured that basic microbiology and IPC implementation capacity existed at all sites. Standardized surveillance protocols for central-line–associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs) were modified from the NHSN for the Indian context. IPC nurses were trained to implement surveillance protocols. Data were reported through a locally developed web portal. Standardized external data quality checks were performed to assure data quality. Results: Between May 2017 and April 2019, 109 ICUs from 37 hospitals (29 public and 8 private) enrolled in the network, of which 33 were teaching hospitals with >500 beds. The network recorded 679,109 patient days, 212,081 central-line days, and 387,092 urinary catheter days. Overall, 4,301 bloodstream infection (BSI) events and 1,402 urinary tract infection (UTI) events were reported. The network CLABSI rate was 9.4 per 1,000 central-line days and the CAUTI rate was 3.4 per 1,000 catheter days. The central-line utilization ratio was 0.31 and the urinary catheter utilization ratio was 0.57. Moreover, 3,542 (73%) of 4,742 pathogens reported from BSIs and 868 (53%) of 1,644 pathogens reported from UTIs were gram negative. Also, 1,680 (26.3%) of all 6,386 pathogens reported were Enterobacteriaceae. Of 1,486 Enterobacteriaceae with complete antibiotic susceptibility testing data reported, 832 (57%) were carbapenem resistant. Of 951 Enterobacteriaceae subjected to colistin broth microdilution testing, 62 (7%) were colistin resistant. The surveillance platform identified 2 separate hospital-level HAI outbreaks; one caused by colistin-resistant K. pneumoniae and another due to Burkholderia cepacia. Phased expansion of surveillance to additional hospitals continues. Conclusions: HAI surveillance was successfully implemented across a national network of diverse hospitals using modified NHSN protocols. Surveillance data are being used to understand HAI burden and trends at the facility and national levels, to inform public policy, and to direct efforts to implement effective hospital IPC activities. This network approach to HAI surveillance may provide lessons to other countries or contexts with limited surveillance capacity.

Funding: None

Disclosures: None