Background: The COVID-19 pandemic disrupted essential health services (EHS) delivery worldwide; however, there are limited data for healthcare facility (HCF)–level EHS disruptions in low- and middle-income countries. We surveyed HCFs in 3 counties in Kenya to understand the extent of and reasons for EHS disruptions occurring during February 2020–May 2021. Methods: We included 3 counties in Kenya with high burden of COVID-19 at the time of study initiation. Stratified sampling of HCFs occurred by HCF level. HCF administrators were interviewed to collect information on types of EHS disruptions that occurred and reasons for disruptions, including those related to infection prevention and control (IPC). Analyses included descriptive statistics with proportions for categorical variables and median with interquartile range (IQR) for continuous variables. Results: In total, 59 HCFs in Kenya provided complete data. All 59 HCFs (100%) reported EHS disruptions due to COVID-19. Among all HCFs, limiting patient volumes was the most common disruption reported (97%), while 56% of HCFs reduced staffing of EHS and 52% suspended EHS. Median duration of disruptions ranged from 7 weeks (IQR, 0–15) for inpatient ward closures to 25 weeks (IQR, 14–37) for limiting patient volumes accessing EHS. Among HCFs that reported disruptions, the most cited reason (ie, 95% of HCFs) was fewer patients receiving services. The most common IPC-related reason for disruption was diversion of resources to accommodate physical distancing measures (76%) followed by COVID-19 outbreaks among patients or staff (34%); staff shortages due to COVID-19 illness (25%) or perceived infection risk (19%); and lack of adequate personal protective equipment (20%). Conclusions: Most HCFs reported disruptions to EHS during the pandemic, including many that were related to IPC. Some disruptions may be mitigated by strengthening IPC infrastructure and practices, including protecting healthcare personnel to prevent staffing shortages.

Funding: None

Disclosures: None

Background: Antibiotics are frequently prescribed in nursing homes, often inappropriately. Data sources are needed to facilitate measurement and reporting of antibiotic use to inform antibiotic stewardship efforts. Previous analyses have shown that the type of nursing-home stay, that is, short stay (<100 days), is a strong predictor of high antibiotic use compared to longer nursing-home stays. The study objective was to compare 2 different data sources, electronic health record (EHR) and long-term care (LTC) pharmacy data, for surveillance of antibiotic use and type of nursing-home stay. Methods: EHR and pharmacy data during 2017 were included from 1,933 and 1,348 US-based nursing homes, respectively. We compared data elements available in each data source for antibiotic use reporting. In each data set, we attempted to describe antibiotic use as the proportion of residents on an antibiotic, days-of-therapy (DOT) per 1,000 resident days (RD), and distribution of antibiotic course duration, overall and at the facility level. Facility proportion of short-stay and long-stay (>100 days) nursing-home residents were calculated using admission dates and census data in the EHR data set and a payor variable in the pharmacy data set (Figure 1). The 2 data sources also provided antibiotic characteristics, including antibiotic class, agent, and route of administration. The deidentified nature of facility data prevented direct comparison of antibiotic use measures between facilities. Results: The EHR and pharmacy data sets contained 381,382 and 326,713 residents, respectively (Table 1). Within the EHR, 51% of residents were prescribed an antibiotic in 2017, at a median rate of 77 DOT per 1,000 RD. In the LTC pharmacy, 46% of residents were prescribed an antibiotic at a median rate of 79 DOT per 1,000 RD (Table 1). Short-stay residents contributed a smaller proportion of total RDs in the EHR relative to the pharmacy cohort (21% vs 50%, respectively). Conclusions: Nursing-home antibiotic use data obtained from EHR and pharmacy vendors can be used for calculating antibiotic use measures, which is important for antibiotic use reporting and facility-level tracking to identify opportunities for improving prescribing practices and provide facility-level benchmarks. Further validation of both data sources in the same facilities is needed to compare antibiotic use rates and to determine the most appropriate proxy for type of nursing-home stay for facility-level risk adjustment of antibiotic use rates.

Funding: No

Disclosures: None

Figure 1.

Table 1.

Colleges and universities around the world engaged diverse strategies during the COVID-19 pandemic. Baylor University, a community of ˜22,700 individuals, was 1 of the institutions which resumed and sustained operations. The key strategy was establishment of multidisciplinary teams to develop mitigation strategies and priority areas for action. This population-based team approach along with implementation of a “Swiss Cheese” risk mitigation model allowed small clusters to be rapidly addressed through testing, surveillance, tracing, isolation, and quarantine. These efforts were supported by health protocols including face coverings, social distancing, and compliance monitoring. As a result, activities were sustained from August 1 to December 8, 2020. There were 62,970 COVID-19 tests conducted with 1435 people testing positive for a positivity rate of 2.28%. A total of 1670 COVID-19 cases were identified with 235 self-reports. The mean number of tests per week was 3500 with approximately 80 of these positive (11/d). More than 60 student tracers were trained with over 120 personnel available to contact trace, at a ratio of 1 per 400 university members. The successes and lessons learned provide a framework and pathway for similar institutions to mitigate the ongoing impacts of COVID-19 and sustain operations during a global pandemic.

OBJECTIVES/GOALS:

• Conduct an environmental scan of Marion County (Indianapolis) neighborhoods using electronic medical record data, state health data, and social and economic data

• Develop strong network of community collaborators

• Conduct a thorough assessment for each targeted neighborhood by listening and understanding the pressing health issues in the community and working together to design and deliver solutions

METHODS/STUDY POPULATION:

• Identify measures in the 3 domains of vulnerability, health and assets for the targeted neighborhoods and conduct bivariate descriptive statistics and multivariable regression analyses to investigate association between measures of vulnerability and health outcomes.

• Initiate relationships with leaders and residents in targeted neighborhoods

• Locate organizations working in targeted neighborhoods through online mapping software and word-of-mouth at neighborhood events, and created a spreadsheet with contact information.

• Conduct multidisciplinary assessment (i.e. key informant interviews, focus groups, town hall meetings) of the targeted neighborhood.

• Iteratively synthesize assessments to develop areas of interest and relevance to the community.

• Develop a road map for solutions identified by the community.

RESULTS/ANTICIPATED RESULTS: The results from the environmental scan conducted will be displayed in a report and visual “map” of health outcomes and health determinants, including assets and barriers for the targeted neighborhoods. The research team will use results from the environmental scan coupled with listening activities including attendance at community events, key informant interviews and focus groups to develop relationships and strong collaborations with the targeted neighborhood stakeholders. The relationship building between the research team and community will provide increased trust and engagement that will further enhance the effectiveness of the assessments completed with the targeted neighborhood. The assessments will help to empower communities to develop sustainable solutions and drive future work that will lead to future grant applications and larger-scale implementation in other community impact hub neighborhoods. DISCUSSION/SIGNIFICANCE OF IMPACT: Through the community impact hub work, we will develop collaborative efforts with targeted neighborhoods with the greatest health inequities in the Marion County area. In partnership with these neighborhoods, we will build a foundation – a network of community collaborators and a focused plan – upon which we will improve the health outcomes of residents while learning best practices on how to do so.