Objectives: Patients with mild cognitive impairment (MCI) employ compensatory cognitive processes to maintain independence in day-to-day functioning as compared to patients with Alzheimer’s dementia (AD). The dorsolateral prefrontal cortex (DLFPC) supports cognitive compensation in normal aging and MCI. Using Paired Associative Stimulation combined with Electroencephalography (PAS-EEG) we have previously shown that patients with AD have impaired DLPFC plasticity compared to healthy control (HC) individuals. The aim of this study is to examine whether DLPFC plasticity in individuals with MCI is preserved compared to those with AD and HC, serving as a potential mechanism underlying cognitive compensation in MCI.

Methods: We analyzed a combined cross-sectional data of 47 AD, 16 MCI, and 40 HC participants from three different studies that assessed their DLPFC plasticity using PAS-EEG. PAS-EEG assesses DLPFC plasticity via the induction of Long Term Potentiation (LTP)-like activity, thereby referred to as PAS-LTP. Using multiple regression, we compared PAS-LTP in MCI to PAS-LTP in AD and HCs, after adjusting for age andgender.

Results: Among the 47 participants with AD (mean [SD] age = 75.3 [7] years), 29 were women and 18 were men; among the 16 participants with MCI (mean [SD] age = 74.8 [6] years), 11 were women and 5 were men; and among the 40 HCs (mean [SD] age = 76.4 [5.1] years), 22 were women and 18 were men. After adjusting for age and gender, there was an impact of diagnostic group on PAS-LTP [F (2,95) = 4.19, p = 0.018, between-group comparison η2 = 0.81]. Post-hoc comparisons showed that participants with MCI had a higher PAS-LTP (mean [SD] = 1.31 [0.49]) than those with AD (mean [SD] = 1.09 [0.28]) (Bonferroni corrected p = 0.042) but not different from PAS-LTP in HCs (mean [SD] = 1.25 [0.33]) (Bonferroni corrected p = 1.0).

Conclusions: Our findings indicate that plasticity is preserved in the DLPFC among individuals with MCI, supporting the hypothesis that DLPFC plasticity contributes to cognitive compensation towards delaying progression to AD. Thus, further enhancement of longer preservation of DLPFC plasticity in individuals with MCI could further delay the onset of AD in this population.

Following an outbreak of Salmonella Typhimurium in Wales in July 2021 associated with sheep meat and offal, further genetically related cases were detected across the UK. Cases were UK residents with laboratory-confirmed Salmonella Typhimurium in the same 5-single-nucleotide polymorphism (SNP) single-linkage cluster with specimen date between 01/08/2021–2031/12/2022. We described cases using routine (UK) and enhanced (Wales only) surveillance data. Exposures in cases in Wales were compared with non-Typhimurium Salmonella case–controls. Environmental Health Practitioners and the Food Standards Agency investigated supply chains of food premises reported by ≥2 cases. Animal, carcass, and environmental samples taken for diagnostic or monitoring purposes for gastrointestinal pathogens were included in microbiological investigations. We identified 142 cases: 75% in England, 23% in Wales and 3% in Scotland. Median age was 32 years, and 59% were male. Direct contact with sheep was associated with becoming a case (aOR: 14, 95%CI: 1.4–145) but reported by few (6/32 cases). No single food item, premises, or supplier linked all cases. Multi-agency collaboration enabled the identification of isolates in the same 5-SNP single-linkage cluster from a sheep carcass at an English abattoir and in ruminant, wildlife, poultry, and environmental samples, suggesting multiple vehicles and pathways of infection.

During March 27–July 14, 2020, the Centers for Disease Control and Prevention’s National Healthcare Safety Network extended its surveillance to hospital capacities responding to COVID-19 pandemic. The data showed wide variations across hospitals in case burden, bed occupancies, ventilator usage, and healthcare personnel and supply status. These data were used to inform emergency responses.

Using data from the National Healthcare Safety Network (NHSN), we assessed changes to intensive care unit (ICU) bed capacity during the early months of the COVID-19 pandemic. Changes in capacity varied by hospital type and size. ICU beds increased by 36%, highlighting the pressure placed on hospitals during the pandemic.

Data reported to the Centers for Disease Control and Prevention’s National Healthcare Safety Network (CDC NHSN) were analyzed to understand the potential impact of the COVID-19 pandemic on central-line–associated bloodstream infections (CLABSIs) in acute-care hospitals. Descriptive analysis of the standardized infection ratio (SIR) was conducted by location, location type, geographic area, and bed size.

Background: The CDC NHSN launched the Antimicrobial Use Option in 2011. The Antimicrobial Use Option allows users to implement risk-adjusted antimicrobial use benchmarking within- and between- facilities using the standardized antimicrobial administration ratio (SAAR) and to evaluate use over time. The SAAR can be used for public health surveillance and to guide an organization’s stewardship or quality improvement efforts. Methods: Antimicrobial Use Option enrollment grew through partner engagement, targeted education, and development of data benchmarking. We analyze enrollment over time and discuss key drivers of participation. Results: Initial 2011 Antimicrobial Use Option enrollment efforts awarded grant Funding: to 4 health departments. These health departments partnered with hospitals, which encouraged vendors to build infrastructure for electronic antimicrobial use reporting. CDC supported vendors through outreach and education. In 2012, with CDC support, Veterans’ Affairs (VA) Informatics, Decision-Enhancement, and Analytic Sciences Center and partners began implementation of Antimicrobial Use Option reporting and validation of submitted data. These early efforts led to enrollment of 64 facilities by 2014 (Fig. 1). As awareness of the antimicrobial use option grew, we focused on facility engagement and development of benchmark metrics. A second round of grant Funding: in 2015 supported submission to the Antimicrobial Use Option from additional facilities by Funding: a vendor, a healthcare system, and an antimicrobial stewardship network. In 2015, CMS recognized the Antimicrobial Use Option as a choice for public health registry reporting under Meaningful Use Stage 3, resulting in an increase in participating hospitals. Antimicrobial Use Option enrollment increased in 2015 (n = 120), coinciding with national prioritization of antimicrobial stewardship. In 2016, the SAAR, was released in NHSN. We leveraged the SAAR to encourage participation from additional facilities and began quarterly calls to encourage continued participation from existing users. In 2016, the Department of Defense began submitting data to the Antimicrobial Use Option, resulting in 207 facilities enrolled in 2016, which grew to 616 in 2017. As of November 2019, 12 vendors self-report submission capabilities and 1,470 facilities, of ~6,800 active NHSN participants, are enrolled in the Antimicrobial Use Option. Two states have passed requirements regulating Antimicrobial Use Option reporting with Tennessee’s requirement going into effect in 2021. Conclusions: The Antimicrobial Use Option offers evidence that collaboration with partners, and leveraging of benchmarking metrics available to a national surveillance system can lead to increased voluntary participation in surveillance of high-priority public health data. Moving forward, we will continue expanding analytic capabilities and partner engagement.

Funding: None

Disclosures: None

Background: Accurate identification of Clostridioides difficile infections (CDIs) from electronic data sources is important for surveillance. We evaluated how frequently laboratory findings were supported by diagnostic coding and treatment data in the electronic health record. Methods: We analyzed a retrospective cohort of patients in the Veterans’ Affairs Health System from 2006 through 2016. A CDI event was defined as a positive laboratory test for C. difficile toxin or toxin genes in the inpatient, outpatient, or long-term care setting with no prior positive test in the preceding 14 days. Events were classified as incident (no CDI in the prior 56 days), or recurrent (CDI in the prior 56 days) and were evaluated for evidence of clinical diagnosis based on International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) and ICD-10-CM codes and at least 1 dose of an anti-CDI agent (intravenous or oral metronidazole, fidaxomicin, or oral vancomycin). We further assessed the possibility of treatment without testing by quantifying positive laboratory tests and diagnostic codes among inpatients receiving an anti-CDI agent. A course of anti-CDI therapy was defined as continuous treatment with the same drug. Results: Among 119,063 incident and recurrent CDI events, 70,114 (58.9%) had a diagnosis code and 15,850 (13.3%) had no accompanying treatment. The proportion of patients with ICD codes was highest among patients treated with fidaxomicin (82.6% of 906) or oral vancomycin (74.3% of 30,777) and was lower among patients receiving metronidazole (63.3% of 103,231) and those without treatment (29.9% of 15,850). The proportion of events with ICD codes and treatment was similar between incident and recurrent episodes. During the study period, there were ~470,000 inpatient courses of metronidazole, fidaxomicin, and oral vancomycin. Table 1 shows the presence of ICD codes and positive laboratory tests by anti-CDI agents. Among 51,100 courses of oral vancomycin, 51% had an ICD code and 44% had a positive test for C. difficile within 7 days of treatment initiation. Among 1,013 courses of fidaxomicin, 79% had an ICD code and 56% had a positive laboratory test. Conclusions: In this large cohort, there was evidence of substantial CDI treatment without confirmatory C. difficile testing and, to a lesser extent, some positive tests without accompanying treatment or coding. A combination of data sources may be needed to more accurately identify CDI from electronic health records for surveillance purposes.

Funding: None

Disclosures: None

Background: To provide a standardized, risk-adjusted method for summarizing antimicrobial use (AU), the Centers for Disease Control and Prevention developed the standardized antimicrobial administration ratio, an observed-to-predicted use ratio in which predicted use is estimated from a statistical model accounting for patient locations and hospital characteristics. The infection burden, which could drive AU, was not available for assessment. To inform AU risk adjustment, we evaluated the relationship between the burden of drug-resistant gram-positive infections and the use of anti-MRSA agents. Methods: We analyzed data from acute-care hospitals that reported ≥10 months of hospital-wide AU and microbiologic data to the National Healthcare Safety Network (NHSN) from January 2018 through June 2019. Hospital infection burden was estimated using the prevalence of deduplicated positive cultures per 1,000 admissions. Eligible cultures included blood and lower respiratory specimens that yielded oxacillin/cefoxitin–resistant Staphylococcus aureus (SA) and ampicillin-nonsusceptible enterococci, and cerebrospinal fluid that yielded SA. The anti-MRSA use rate is the total antimicrobial days of ceftaroline, dalbavancin, daptomycin, linezolid, oritavancin, quinupristin/dalfopristin, tedizolid, telavancin, and intravenous vancomycin per 1,000 days patients were present. AU rates were modeled using negative binomial regression assessing its association with infection burden and hospital characteristics. Results: Among 182 hospitals, the median (interquartile range, IQR) of anti-MRSA use rate was 86.3 (59.9–105.0), and the median (IQR) prevalence of drug-resistant gram-positive infections was 3.4 (2.1–4.8). Higher prevalence of drug-resistant gram-positive infections was associated with higher use of anti-MRSA agents after adjusting for facility type and percentage of beds in intensive care units (Table 1). Number of hospital beds, average length of stay, and medical school affiliation were nonsignificant. Conclusions: Prevalence of drug-resistant gram-positive infections was independently associated with the use of anti-MRSA agents. Infection burden should be used for risk adjustment in predicting the use of anti-MRSA agents. To make this possible, we recommend that hospitals reporting to NHSN’s AU Option also report microbiologic culture results.

Funding: None

Disclosures: None

Background: The NHSN has used positive laboratory tests for surveillance of Clostridioides difficile infection (CDI) LabID events since 2009. Typically, CDIs are detected using enzyme immunoassays (EIAs), nucleic acid amplification tests (NAATs), or various test combinations. The NHSN uses a risk-adjusted, standardized infection ratio (SIR) to assess healthcare facility-onset (HO) CDI. Despite including test type in the risk adjustment, some hospital personnel and other stakeholders are concerned that NAAT use is associated with higher SIRs than are EIAs. To investigate this issue, we analyzed NHSN data from acute-care hospitals for July 1, 2017 through June 30, 2018. Methods: Calendar quarters for which CDI test type was reported as NAAT (includes NAAT, glutamate dehydrogenase (GDH)+NAAT and GDH+EIA followed by NAAT if discrepant) or EIA (includes EIA and GDH+EIA) were selected. HO CDI SIRs were calculated for facility-wide inpatient locations. We conducted the following analyses: (1) Among hospitals that did not switch their test type, we compared the distribution of HO incident rates and SIRs by those reporting NAAT vs EIA. (2) Among hospitals that switched their test type, we selected quarters with a stable switch pattern of 2 consecutive quarters of each of EIA and NAAT (categorized as pattern EIA-to-NAAT or NAAT-to-EIA). Pooled semiannual SIRs for EIA and NAAT were calculated, and a paired t test was used to evaluate the difference of SIRs by switch pattern. Results: Most hospitals did not switch test types (3,242, 89%), and 2,872 (89%) reported sufficient data to calculate SIRs, with 2,444 (85%) using NAAT. The crude pooled HO CDI incidence rates for hospitals using EIA clustered at the lower end of the histogram versus rates for NAAT (Fig. 1). The SIR distributions of both NAAT and EIA overlapped substantially and covered a similar range of SIR values (Fig. 1). Among hospitals with a switch pattern, hospitals were equally likely to have an increase or decrease in their SIR (Fig. 2). The mean SIR difference for the 42 hospitals switching from EIA to NAAT was 0.048 (95% CI, −0.189 to 0.284; P = .688). The mean SIR difference for the 26 hospitals switching from NAAT to EIA was 0.162 (95% CI, −0.048 to 0.371; P = .124). Conclusions: The pattern of SIR distributions of both NAAT and EIA substantiate the soundness of NHSN risk adjustment for CDI test types. Switching test type did not produce a consistent directional pattern in SIR that was statistically significant.

Disclosures: None

Funding: None

Background: The CDC NHSN surveillance coverage includes central-line–associated bloodstream infections (CLABSIs) in acute-care hospital intensive care units (ICUs) and select patient-care wards across all 50 states. This surveillance enables the use of CLABSI data to measure time between events (TBE) as a potential metric to complement traditional incidence measures such as the standardized infection ratio and prevention progress. Methods: The TBEs were calculated using 37,705 CLABSI events reported to the NHSN during 2015–2018 from medical, medical-surgical, and surgical ICUs as well as patient-care wards. The CLABSI TBE data were combined into 2 separate pairs of consecutive years of data for comparison, namely, 2015–2016 (period 1) and 2017–2018 (period 2). To reduce the length bias, CLABSI TBEs were truncated for period 2 at the maximum for period 1; thereby, 1,292 CLABSI events were excluded. The medians of the CLABSI TBE distributions were compared over the 2 periods for each patient care location. Quantile regression models stratified by location were used to account for factors independently associated with CLABSI TBE, such as hospital bed size and average length of stay, and were used to measure the adjusted shift in median CLABSI TBE. Results: The unadjusted median CLABSI TBE shifted significantly from period 1 to period 2 for the patient care locations studied. The shift ranged from 20 to 75.5 days, all with 95% CIs ranging from 10.2 to 32.8, respectively, and P < .0001 (Fig. 1). Accounting for independent associations of CLABSI TBE with hospital bed size and average length of stay, the adjusted shift in median CLABSI TBE remained significant for each patient care location that was reduced by ∼15% (Table 1). Conclusions: Differences in the unadjusted median CLABSI TBE between period 1 and period 2 for all patient care locations demonstrate the feasibility of using TBE for setting benchmarks and tracking prevention progress. Furthermore, after adjusting for hospital bed size and average length of stay, a significant shift in the median CLABSI TBE persisted among all patient care locations, indicating that differences in patient populations alone likely do not account for differences in TBE. These findings regarding CLABSI TBEs warrant further exploration of potential shifts at additional quantiles, which would provide additional evidence that TBE is a metric that can be used for setting benchmarks and can serve as a signal of CLABSI prevention progress.

Funding: None

Disclosures: None

Background: The National Healthcare Safety Network (NHSN) has used positive laboratory tests for surveillance of Clostridioides difficile infection (CDI) LabID events since 2009. Typically, CDIs are detected using enzyme immunoassays (EIAs), nucleic acid amplification tests (NAATs), or various test combinations. The NHSN uses a risk-adjusted, standardized infection ratio (SIR) to assess healthcare facility-onset (HO) CDI. Despite including test type in the risk adjustment, some hospital personnel and other stakeholders are concerned that NAAT use is associated with higher SIRs than EIA use. To investigate this issue, we analyzed NHSN data from acute-care hospitals for July 1, 2017, through June 30, 2018. Methods: Calendar quarters where CDI test type was reported as NAAT (includes NAAT, glutamate dehydrogenase (GDH)+NAAT and GDH+EIA followed by NAAT if discrepant) or EIA (includes EIA and GDH+EIA) were selected. HO-CDI SIRs were calculated for facility-wide inpatient locations. We conducted the following 2 analyses: (1) Among hospitals that did not switch their test type, we compared the distribution of HO incident rates and SIRs by those reporting NAAT versus EIA. (2) Among hospitals that switched their test type, we selected quarters with a stable switch pattern of 2 consecutive quarters of each of EIA and NAAT (categorized as EIA-to-NAAT or NAAT-to-EIA). Pooled semiannual SIRs for EIA and NAAT were calculated, and a paired t test was used to evaluate the difference in SIRs by switch pattern. Results: Most hospitals did not switch test types (3,242, 89%), and 2,872 (89%) reported sufficient data to calculate an SIR, with 2,444 (85%) using NAAT. The crude pooled HO CDI incidence rates for hospitals using EIAs clustered at the lower end of the histogram versus rates for NAATs (Fig. 1). The SIR distributions, both NAATs and EIAs, overlapped substantially and covered a similar range of SIR values (Fig. 1). Among hospitals with a switch pattern, hospitals were equally likely to have an increase or decrease in their SIRs (Fig. 2). The mean SIR difference for the 42 hospitals switching from EIA to NAAT was 0.048 (95% CI, −0.189 to 0.284; P = .688). The mean SIR difference for the 26 hospitals switching from NAAT to EIA was 0.162 (95% CI, −0.048 to 0.371; P = .124). Conclusions: The pattern of SIR distribution for both NAAT and EIA substantiate the soundness of the NHSN’s risk adjustment for CDI test types. Switching test type did not produce a consistent directional pattern in SIR that was statistically significant.

Funding: None

Disclosures: None

Background: The CDC National Healthcare Safety Network (NHSN) is the nation’s most widely used healthcare-associated infection (HAI) and antibiotic use and resistance (AUR) surveillance system. More than 22,000 healthcare facilities report data to the NHSN. The NHSN data are used by facilities, the CDC, health departments, the CMS, among other organizations and agencies. In 2017, the CDC updated the NHSN Agreement to Participate and Consent (Agreement), completed by facilities, broadening health department access to NHSN data and extending eligibility for data use agreements (DUAs) to local and territorial health departments. DUAs enable access to NHSN data reported by facilities in the health department’s jurisdiction and have been available to state health departments since 2011. The updated agreement also enables the CDC to provide NHSN data to health departments for targeted prevention projects outbreak investigations and responses. Methods: We reviewed the current NHSN DUA inventory to assess the extent to which health departments use the NHSN’s new data access provisions and used semistructured interviews with health department staff, conducted via emails, phone, and in person conversations, to identify and describe their NHSN data uses. Results: As of late 2019, the NHSN has DUAs with health departments in 17 states, 7 local health departments (including municipalities and counties), and 1 US territory. The NHSN also has received requests from 2 state health departments for data supporting HAI prevention projects. Health departments with DUAs described improved relationships with facilities in their jurisdictions because of new opportunities to offer NHSN data analysis assistance to facilities. One local health department analyzed their NHSN carbapenem-resistant Enterobacteriaceae (CRE) data to identify (1) facilities in its jurisdiction with comparatively high CRE infection burden and (2) geographic areas to target for a CRE isolate submission program. Outreach to facilities with high CRE burden led to enrollment of 15 clinical laboratories into a voluntary isolate submission program to analyze CRE isolates for additional characterization. Examples of health departments’ use of data for action include: notifying facilities with high standardized infection ratios (SIRs) and sharing Targeted Assessment for Prevention (TAP) reports. Conclusions: The NHSN’s role as a shared surveillance resource has expanded in multiple public health jurisdictions as a result of new data access provisions. Health departments are using NHSN data in their programmatic responses to HAI and AR challenges. New access to NHSN data is enabling public health jurisdictions to assess problems and opportunities, provide guidance for prevention projects, and support program evaluations.

Funding: None

Disclosures: None

Background: Water management programs (WMPs) are needed to minimize the growth and transmission of opportunistic pathogens in healthcare facility water systems. In 2017, the Centers for Medicare & Medicaid Service (CMS) began requiring that certified hospitals in the United States have water management policies and procedures; in response, the National Healthcare Safety Network (NHSN) Annual Hospital Survey included new, voluntary questions on practices regarding water management and monitoring. Of 4,929 hospitals surveyed in 2017, 3,821 (77.5%) reported having a WMP. Of these 3,821 facilities, 86.9% reported regular monitoring of water temperature; 66.2% monitored disinfectant (eg, residual chlorine); 63.1% used specific tests for Legionella; and 35.6% performed heterotrophic plate counts (HPCs). We analyzed new, 2018 hospital survey data to assess further progress toward meeting CMS requirements for WMPs. Methods: We analyzed 2018 NHSN Annual Hospital Survey responses for facilities that reported on WMPs in 2017. Responses included information regarding risk assessments for Legionella and other waterborne pathogens as well as details regarding WMP teams and water-monitoring practices. WMP team members were categorized as administrative (hospital administrator, compliance officer, risk or quality management), epidemiology or infection control (epidemiologist or infection preventionist, other clinical), or environmental or facilities (consultant, facility manager or engineer, equipment or chemical supplier, maintenance). Statistical significance was assessed using the McNemar test, where appropriate. Results: Of hospitals reporting on WMPs in 2017, 4,087 of 4,929 (83%) responded again in 2018. The proportion of facilities that reported having a WMP increased from 3,258 of 4,087 (79.7%) in 2017 to 3,647 of 4,087 (89.2%) in 2018 (P < .0001). Of the 3,647 hospitals that reported having a WMP in 2018, 95.9% had conducted a risk assessment for waterborne pathogens; 67.3% of these facilities had most recently done so within 1 year of the survey. WMP teams had representation from environmental or facilities staff at 98.8% of hospitals, epidemiology or infection control staff at 89.8% of hospitals, and administrative staff at 71.7% of hospitals. Of facilities with WMPs in 2018, 90.5% reported regular monitoring of water temperature, 72.2% disinfectant, 67.4% tests for Legionella, and 48.8% HPCs. Conclusions: More hospitals reported having a WMP in 2018 than 2017. However, ~1 in 10 respondents lacked a WMP. Differences in water monitoring practices across facilities potentially reflect a lack of standardization in how WMPs are implemented. Some hospital WMPs do not incorporate routine monitoring of water temperature and disinfectant, which is a basic practice. CDC continues to develop tools, resources, and training to support facility WMP teams in meeting CMS requirements and protecting patients from water-associated pathogens.

Funding: None

Disclosures: None

Background:Staphylococcus aureus is frequently implicated in healthcare-associated infections in the United States, and a substantial proportion of these infections are attributed to methicillin-resistant Staphylococcus aureus (MRSA). Although MRSA infections have decreased in health care settings, accurate estimates of the rate of decline call for risk-adjusted methods for calculating the resistant proportion (%R), that is, the proportion of S. aureus resistant to cefoxitin or oxacillin. Risk-adjusted %R also enables more accurate interhospital comparisons and can serve as a quantitative guide and evaluation metric for prevention efforts. Methods: To develop a risk-adjusted %R for S. aureus, we analyzed the antimicrobial susceptibility test (AST) results for S. aureus isolates reported to the CDC NHSN Antimicrobial Resistance Option during 2017–2018. Isolates were reported for cerebrospinal fluid (CSF), blood, lower respiratory tract (LRT), and urine. Isolates without cefoxitin and oxacillin test results, or from the facilities that had >10% missing test results were excluded. Test results were differentiated between those associated with community-onset and hospital-onset (HO) infections by defining the latter group as test results for isolates obtained 3 days or more after hospital admission. Logistic regression was used to evaluate the factors associated with oxacillin/cefoxitin resistance. Hospital, patient and isolate-level variables from NHSN annual survey and AR option were assessed as covariates. Variable entry into the models is based on significance level P < .05. Results: Among 9,992 hospital-onset SA isolates from 9,019 patients in 315 facilities, 5,488 (54.9%) were MRSA. Logistic regression showed that a higher proportion of HO-MRSA was significantly associated with older age, female, particular sources of specimen (urine and LRT), and selected hospital characteristics: hospitals not serving as major teaching hospitals, hospitals with a higher proportion of MRSA among community-onset SA isolates, hospitals with lower percentage of beds in intensive care units, and hospitals outsourcing AST service (Table 1). Conclusions: HO-MRSA is independently associated with community burden of MRSA, older and female patient populations, and hospital teaching status and AST practices, which highlights the importance of public health engagement and regional collaborations to prevent MRSA. To provide a standardized MRSA proportion for public health surveillance, taking some of these factors into account in MRSA proportion standardization should be considered.

Funding: None

Disclosures: None

Life course research embraces the complexity of health and disease development, tackling the extensive interactions between genetics and environment. This interdisciplinary blueprint, or theoretical framework, offers a structure for research ideas and specifies relationships between related factors. Traditionally, methodological approaches attempt to reduce the complexity of these dynamic interactions and decompose health into component parts, ignoring the complex reciprocal interaction of factors that shape health over time. New methods that match the epistemological foundation of the life course framework are needed to fully explore adaptive, multilevel, and reciprocal interactions between individuals and their environment. The focus of this article is to (1) delineate the differences between lifespan and life course research, (2) articulate the importance of complex systems science as a methodological framework in the life course research toolbox to guide our research questions, (3) raise key questions that can be asked within the clinical and translational science domain utilizing this framework, and (4) provide recommendations for life course research implementation, charting the way forward. Recent advances in computational analytics, computer science, and data collection could be used to approximate, measure, and analyze the intertwining and dynamic nature of genetic and environmental factors involved in health development.