Typically, long-term acute care hospitals (LTACHs) have less experience in and incentives to implementing aggressive infection control for drug-resistant organisms such as carbapenem-resistant Enterobacteriaceae (CRE) than acute care hospitals. Decision makers need to understand how implementing control measures in LTACHs can impact CRE spread regionwide. Using our Chicago metropolitan region agent-based model to simulate CRE spread and control, we estimated that a prevention bundle in only LTACHs decreased prevalence by a relative 4.6%-17.1%, averted 1,090-2,795 new carriers, 273-722 infections and 37-87 deaths over 3 years and saved $30.5-$69.1 million, compared with no CRE control measures. When LTACHs and intensive care units intervened, prevalence decreased by a relative 21.2%. Adding LTACHs averted an additional 1,995 carriers, 513 infections, and 62 deaths, and saved $47.6 million beyond implementation in intensive care units alone. Thus, LTACHs may be more important than other acute care settings for controlling CRE, and regional efforts to control drug-resistant organisms should start with LTACHs as a centerpiece.

Importance Multidrug-resistant organisms (MDROs) can spread across health care facilities in a region. Because of limited resources, certain interventions can be implemented in only some facilities; thus, decision-makers need to evaluate which interventions may be best to implement. Objective To identify a group of target facilities and assess which MDRO intervention would be best to implement in the Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County, a large regional public health collaborative in Orange County, California. Design, Setting, and Participants An agent-based model of health care facilities was developed in 2016 to simulate the spread of methicillin-resistantStaphylococcus aureus(MRSA) and carbapenem-resistant Enterobacteriaceae (CRE) for 10 years starting in 2010 and to simulate the use of various MDRO interventions for 3 years starting in 2017. All health care facilities (23 hospitals, 5 long-term acute care hospitals, and 74 nursing homes) serving adult inpatients in Orange County, California, were included, and 42 target facilities were identified via network analyses. Exposures Increasing contact precaution effectiveness, increasing interfacility communication about patients' MDRO status, and performing decolonization using antiseptic bathing soap and a nasal product in a specific group of target facilities. Main Outcomes and Measures MRSA and CRE prevalence and number of new carriers (ie, transmission events). Results Compared with continuing infection control measures used in Orange County as of 2017, increasing contact precaution effectiveness from 40% to 64% in 42 target facilities yielded relative reductions of 0.8% (range, 0.5%-1.1%) in MRSA prevalence and 2.4% (range, 0.8%-4.6%) in CRE prevalence in health care facilities countywide after 3 years, averting 761 new MRSA transmission events (95% CI, 756-765 events) and 166 new CRE transmission events (95% CI, 158-174 events). Increasing interfacility communication of patients' MDRO status to 80% in these target facilities produced no changes in the prevalence or transmission of MRDOs. Implementing decolonization procedures (clearance probability: 39% in hospitals, 27% in long-term acute care facilities, and 3% in nursing homes) yielded a relative reduction of 23.7% (range, 23.5%-23.9%) in MRSA prevalence, averting 3515 new transmission events (95% CI, 3509-3521 events). Increasing the effectiveness of antiseptic bathing soap to 48% yielded a relative reduction of 39.9% (range, 38.5%-41.5%) in CRE prevalence, averting 1435 new transmission events (95% CI, 1427-1442 events). Conclusions and Relevance The findings of this study highlight the ways in which modeling can inform design of regional interventions and suggested that decolonization would be the best strategy for the Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County.

Background. When trying to control regional spread of antibiotic-resistant pathogens such as carbapenem-resistant Enterobacteriaceae (CRE), decision makers must choose the highest-yield facilities to target for interventions. The question is, with limited resources, how best to choose these facilities. Methods. Using our Regional Healthcare Ecosystem Analyst-generated agent-based model of all Chicago metropolitan area inpatient facilities, we simulated the spread of CRE and different ways of choosing facilities to apply a prevention bundle (screening, chlorhexidine gluconate bathing, hand hygiene, geographic separation, and patient registry) to a resource-limited 1686 inpatient beds. Results. Randomly selecting facilities did not impact prevalence, but averted 620 new carriers and 175 infections, saving $6.3 million in total costs compared to no intervention. Selecting facilities by type (eg, long-term acute care hospitals) yielded a 16.1% relative prevalence decrease, preventing 1960 cases and 558 infections, saving $62.4 million more than random selection. Choosing the largest facilities was better than random selection, but not better than by type. Selecting by considering connections to other facilities (ie, highest volume of discharge patients) yielded a 9.5% relative prevalence decrease, preventing 1580 cases and 470 infections, and saving $51.6 million more than random selection. Selecting facilities using a combination of these metrics yielded the greatest reduction (19.0% relative prevalence decrease, preventing 1840 cases and 554 infections, saving $59.6 million compared with random selection). Conclusions. While choosing target facilities based on single metrics (eg, most inpatient beds, most connections to other facilities) achieved better control than randomly choosing facilities, more effective targeting occurred when considering how these and other factors (eg, patient length of stay, care for higher-risk patients) interacted as a system.

Background. Clinical testing detects a fraction of carbapenem-resistant Enterobacteriaceae (CRE) carriers. Detecting a greater proportion could lead to increased use of infection prevention and control measures but requires resources. Therefore, it is important to understand the impact of detecting increasing proportions of CRE carriers. Methods. We used our Regional Healthcare Ecosystem Analyst-generated agent-based model of adult inpatient healthcare facilities in Orange County, California, to explore the impact that detecting greater proportions of carriers has on the spread of CRE. Results. Detecting and placing 1 in 9 carriers on contact precautions increased the prevalence of CRE from 0% to 8.0% countywide over 10 years. Increasing the proportion of detected carriers from 1 in 9 up to 1 in 5 yielded linear reductions in transmission; at proportions >1 in 5, reductions were greater than linear. Transmission reductions did not occur for 1, 4, or 5 years, varying by facility type. With a contact precautions effectiveness of <= 70%, the detection level yielding nonlinear reductions remained unchanged; with an effectiveness of >80%, detecting only 1 in 5 carriers garnered large reductions in the number of new CRE carriers. Trends held when CRE was already present in the region. Conclusion. Although detection of all carriers provided the most benefits for preventing new CRE carriers, if this is not feasible, it may be worthwhile to aim for detecting >1 in 5 carriers.

BACKGROUND:Although current human papillomavirus (HPV) genotype screening tests identify genotypes 16 and 18 and do not specifically identify other high-risk types, a new extended genotyping test identifies additional individual (31, 45, 51, and 52) and groups (33/58, 35/39/68, and 56/59/66) of high-risk genotypes.METHODS:We developed a Markov model of the HPV disease course and evaluated the clinical and economic value of HPV primary screening with Onclarity (BD Diagnostics, Franklin Lakes, NJ) capable of extended genotyping in a cohort of women 30 years or older. Women with certain genotypes were later rescreened instead of undergoing immediate colposcopy and varied which genotypes were rescreened, disease progression rate, and test cost.RESULTS:Assuming 100% compliance with screening, HPV primary screening using current tests resulted in 25,194 invasive procedures and 48 invasive cervical cancer (ICC) cases per 100,000 women. Screening with extended genotyping (100% compliance) and later rescreening women with certain genotypes averted 903 to 3163 invasive procedures and resulted in 0 to 3 more ICC cases compared with current HPV primary screening tests. Extended genotyping was cost-effective ($2298-$7236/quality-adjusted life year) when costing $75 and cost saving (median, $0.3-$1.0 million) when costing $43. When the probabilities of disease progression increased (2-4 times), extended genotyping was not cost-effective because it resulted in more ICC cases and accrued fewer quality-adjusted life years.CONCLUSIONS:Our study identified the conditions under which extended genotyping was cost-effective and even cost saving compared with current tests. A key driver of cost-effectiveness is the risk of disease progression, which emphasizes the need to better understand such risks in different populations.

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Background Multidrug-resistant organisms (MDROs) spread between hospitals, nursing homes (NH), and long-term acute care facilities (LTACs) via patient transfers. SHIELD OC is a regional public health collaborative involving decolonization at 38 healthcare facilities selected based upon their high degree of patient sharing. We report baseline MDRO prevalence in 21 NH/LTACs. Methods A random sample of 50 adults for 21 NH/LTACs (18 NHs, 3 LTACs) were screened for methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus spp. (VRE), extended-spectrum beta-lactamase producing organisms (ESBL), and carbapenem-resistant Enterobacteriaceae (CRE) using nares, skin (axilla/groin), and peri-rectal swabs. Facility and resident characteristics associated with MDRO carriage were assessed using multivariable models clustering by person and facility. Results Prevalence of MDROs was 65% in NHs and 80% in LTACs. The most common MDROs in NHs were MRSA (42%) and ESBL (34%); in LTACs they were VRE (55%) and ESBL (38%). CRE prevalence was higher in facilities that manage ventilated LTAC patients and NH residents (8% vs. <1%, p<0.001). MDRO status was known for 18% of NH residents and 49% of LTAC patients. MDRO colonized adults commonly harbored additional MDROs (54% MDRO+ NH residents and 62% MDRO+ LTACs patients). History of MRSA [OR=1.7 C.I. (1.2, 2.4),p=0.004], VRE [OR=2.1 C.I.(1.2, 3.8),p=0.01], ESBL [OR=1.6 C.I.(1.1, 2.3),p=0.03] and diabetes [OR=1.3 C.I.(1.0, 1.7),p=0.03] were associated with any MDRO carriage. Conclusions The majority of NH residents and LTACs patients harbor MDROs. MDRO status is frequently unknown to the facility. The high MDRO prevalence highlights the need for prevention efforts in NH/LTACs as part of regional efforts to control MDRO spread.

BACKGROUND:Regions are considering the use of electronic registries to track patients who carry antibiotic-resistant bacteria, including carbapenem-resistant Enterobacteriaceae (CRE). Implementing such a registry can be challenging and requires time, effort, and resources; therefore, there is a need to better understand the potential impact.METHODS:We developed an agent-based model of all inpatient healthcare facilities (90 acute care hospitals, 9 long-term acute care hospitals, 351 skilled nursing facilities, and 12 ventilator-capable skilled nursing facilities) in the Chicago metropolitan area, surrounding communities, and patient flow using our Regional Healthcare Ecosystem Analyst software platform. Scenarios explored the impact of a registry that tracked patients carrying CRE to help guide infection prevention and control.RESULTS:When all Illinois facilities participated (n = 402), the registry reduced the number of new carriers by 11.7% and CRE prevalence by 7.6% over a 3-year period. When 75% of the largest Illinois facilities participated (n = 304), registry use resulted in a 11.6% relative reduction in new carriers (16.9% and 1.2% in participating and nonparticipating facilities, respectively) and 5.0% relative reduction in prevalence. When 50% participated (n = 201), there were 10.7% and 5.6% relative reductions in incident carriers and prevalence, respectively. When 25% participated (n = 101), there was a 9.1% relative reduction in incident carriers (20.4% and 1.6% in participating and nonparticipating facilities, respectively) and 2.8% relative reduction in prevalence.CONCLUSIONS:Implementing an extensively drug-resistant organism registry reduced CRE spread, even when only 25% of the largest Illinois facilities participated due to patient sharing. Nonparticipating facilities garnered benefits, with reductions in new carriers.

Abstract Background Patient movement between hospitals, nursing homes (NH), and long-term acute care facilities (LTACs) contributes to MDRO spread. SHIELD OC is a regional decolonization collaborative among adult facilities with high patient sharing designed to reduce countywide MDRO prevalence. We report pre- and post-intervention MDRO colonization prevalence. Methods Decolonization included chlorhexidine bath (CHG) (4% liquid or 2% cloth) and twice-daily nasal swab 10% povidone–iodine (PI). LTAC and NH used CHG for all baths and PI 5 days on admission and Monday–Friday every other week. Patients in contact precautions (CP) at hospitals had daily CHG and 5-days PI on admission. Point-prevalence screening for MRSA, VRE, ESBL, and CRE using nares, axilla/groin, and peri-rectal swabs was conducted pre-intervention (September 2016–March 2017) and post-intervention (August 2018–April 2019); 50 random LTAC and 50 CP hospitalized patients were sampled; for NH up to 50 were sampled at baseline and all residents post-intervention. Raw impact of the intervention was assessed by the average change in colonization prevalence, with each facility carrying equal weight. Generalized linear mixed models (GLM) stratified by facility type were used to assess the impact on MDRO colonization when clustering by facility. Results Across 35 facilities (16 hospitals, 16 NHs, 3 LTACs), the overall MDRO prevalence was reduced 22% in NHs (OR 0.58, P < 0.001), 34% LTACs (OR = 0.27, P < 0.001), and 11% CP patients (OR = 0.67, P < 0.001, Table 1). For MRSA, raw reductions were 31% NHs (OR = 0.58, P < 0.001), 39% LTACs (OR = 0.51, P = 0.01), and 3% CP patients (OR = 0.88, P = NS). For VRE, raw reductions were 40% NHs (OR = 0.62, P = 0.001), 55% LTACs (OR = 0.26, P < 0.001), and 15% CP patients (OR = 0.67, P = 0.004). For ESBLs, raw reductions were 24% NHs (OR = 0.65, P < 0.001), 34% LTACs (OR = 0.53, P = 0.01), and 26% CP patients (OR = 0.64, P < 0.001). For CRE, raw reductions were 24% NHs (OR = 0.70, P = NS), and 23% LTACs (OR = 0.75, P = NS). CRE increased by 26% in CP averaged across hospitals, although patient -level CRE declined 2.4% to 1.8% (OR = 0.74, P = NS). Conclusion MDRO carriage was common in highly inter-connected NHs, LTACs and hospitals. A regional collaborative of universal decolonization in long-term care and targeted decolonization of CP patients in hospitals led to sizeable reductions in MDRO carriage. Disclosures All Authors: No reported Disclosures.

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

OBJECTIVE While previous work showed that the Centers for Disease Control and Prevention toolkit for carbapenem-resistant Enterobacteriaceae (CRE) can reduce spread regionally, these interventions are costly, and decisions makers want to know whether and when economic benefits occur. DESIGN Economic analysis SETTING Orange County, California METHODS Using our Regional Healthcare Ecosystem Analyst (RHEA)-generated agent-based model of all inpatient healthcare facilities, we simulated the implementation of the CRE toolkit (active screening of interfacility transfers) in different ways and estimated their economic impacts under various circumstances. RESULTS Compared to routine control measures, screening generated cost savings by year 1 when hospitals implemented screening after identifying ≤20 CRE cases (saving $2,000–$9,000) and by year 7 if all hospitals implemented in a regional coordinated manner after 1 hospital identified a CRE case (hospital perspective). Cost savings was achieved only if hospitals independently screened after identifying 10 cases (year 1, third-party payer perspective). Cost savings was achieved by year 1 if hospitals independently screened after identifying 1 CRE case and by year 3 if all hospitals coordinated and screened after 1 hospital identified 1 case (societal perspective). After a few years, all strategies cost less and have positive health effects compared to routine control measures; most strategies generate a positive cost-benefit each year. CONCLUSIONS Active screening of interfacility transfers garnered cost savings in year 1 of implementation when hospitals acted independently and by year 3 if all hospitals collectively implemented the toolkit in a coordinated manner. Despite taking longer to manifest, coordinated regional control resulted in greater savings over time. Infect Control Hosp Epidemiol 2018;39:516–524

Abstract Background MDROs can spread between hospitals, nursing homes (NH), and long-term acute care facilities (LTACs) via shared patients. SHIELD OC is a regional decolonization collaborative involving 38 of 104 countywide adult facilities identified by their high degree of direct and indirect patient sharing with one another. We report baseline MDRO prevalence in these facilities. Methods Adult patients in 38 facilities (17 hospitals, 18 NHs, 3 LTACs) underwent point-prevalence screening between September 2016–April 2017 for MRSA, VRE, ESBL, and CRE using nares, skin (axilla/groin), and peri-rectal swabs. In NHs and LTACs, residents were randomly selected until 50 sets of swabs were obtained. Swabbing in hospitals involved all patients in contact precautions. An additional set of swabs were also performed for all LTAC admissions from November 2016–February 2017. Results The overall prevalence of any MDRO among patients was 64% (44%–88%) in NHs, 80% (range 72%–86%) in LTACs, and 64% (54–84%) in hospitals (contact precaution patients) (Table 1). Only 25%, 64%, and 81% of patients were already known to harbor an MDRO in NHs, LTACs, and hospitals, respectively. Known MDRO patients also harbored another MDRO 49%, 63%, and 34% of the time for NHs, LTACs, and hospitals, respectively. In LTACs, MDRO point prevalence was 38% higher than the usual admission prevalence (65% higher for MRSA, 34% higher for VRE, 95% higher for ESBL, and 50% higher for CRE). Conclusion MDRO carriage in highly inter-connected NHs and LTACs was widespread, rivaling that found in hospitalized patients on contact precautions. MRSA, VRE, and ESBL carriage far outnumbered CRE carriage. A history of MDRO was insensitive for identifying MDRO carriers, and many patients carried multiple MDROs. The extensive MDRO burden and transmission in long-term care settings suggests that regional MDRO prevention efforts must include MDRO control in long-term care facilities. Disclosures R. D. Singh, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; J. A. McKinnell, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; L. G. Miller, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; K. Kleinman, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; L. Heim, Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; T. D. Dutciuc, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; M. Estevez, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; G. Gussin, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; L’Oreal: Consultant, Consulting fee; J. Chang, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; E. M. Peterson, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; B. Y. Lee, GSK: Consultant, Consulting fee; R. A. Weinstein, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; OpGen Company: Study support, Provided services at no charge; M. K. Hayden, Sage Products: Receipt of contributed product, Sage is contributing product to healthcare facilities participating in a regional collaborative on which I am a co-investigator. Neither I nor my hospital receive product.; Clorox: Receipt of contributed product, Research support; CDC: Grant Investigator and Receipt of contributed product, Research grant; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; OpGen Company: Study support, Provided services at no charge for studies; S. K. Gohil, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; S. Park, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; S. Tam, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; R. Saavedra, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; S. Yamaguchi, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; H. Custodio, Xttrium Laboratories: Study coordination, Conducting studies in healthcare facilities that are receiving contributed product; Sage Products: Study coordination, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Study coordination, Conducting studies in healthcare facilities that are receiving contributed product; J. Nguyen, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; T. Tjoa, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; J. He, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; M. H. Coady, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; R. Platt, Sage Products: Receipt of contributed product, Conducting clinical studies in which participating healthcare facilities are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting clinical studies in which participating healthcare facilities are receiving contributed product; Clorox: Receipt of contributed product, Conducting clinical studies in which participating healthcare facilities are receiving contributed product; receive research funds from Clorox, but Clorox has no role in the design; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; S. S. Huang, Sage Products: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; Clorox: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; 3M: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; Molnlycke: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product

Delays often occur between CLSI and FDA revisions of antimicrobial interpretive criteria. Using our Regional Healthcare Ecosystem Analyst (RHEA) simulation model, we found that the 32-month delay in changing carbapenem-resistant Enterobacteriaceae (CRE) breakpoints might have resulted in 1,821 additional carriers in Orange County, CA, an outcome that could have been avoided by identifying CRE and initiating contact precautions. Policy makers should aim to minimize the delay in the adoption of new breakpoints for antimicrobials against emerging pathogens when containment of spread is paramount; delays of <1.5 years are ideal.

OBJECTIVE:To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS:Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS:Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION:The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION:Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements.

Vaccines reside in a complex multiscale system that includes biological, clinical, behavioral, social, operational, environmental, and economical relationships. Not accounting for these systems when making decisions about vaccines can result in changes that have little effect rather than solutions, lead to unsustainable solutions, miss indirect (e.g., secondary, tertiary, and beyond) effects, cause unintended consequences, and lead to wasted time, effort, and resources. Mathematical and computational modeling can help better understand and address complex systems by representing all or most of the components, relationships, and processes. Such models can serve as "virtual laboratories" to examine how a system operates and test the effects of different changes within the system. Here are ten lessons learned from using computational models to bring more of a systems approach to vaccine decision making: (i) traditional single measure approaches may overlook opportunities; (ii) there is complex interplay among many vaccine, population, and disease characteristics; (iii) accounting for perspective can identify synergies; (iv) the distribution system should not be overlooked; (v) target population choice can have secondary and tertiary effects; (vi) potentially overlooked characteristics can be important; (vii) characteristics of one vaccine can affect other vaccines; (viii) the broader impact of vaccines is complex; (ix) vaccine administration extends beyond the provider level; and (x) the value of vaccines is dynamic.

BACKGROUND:Many of the world's vaccine supply chains do not adequately provide vaccines, prompting several questions: how are vaccine supply chains currently structured, are these structures closely tailored to individual countries, and should these supply chains be radically redesigned?METHODS:We segmented the 57 GAVI-eligible countries' vaccine supply chains based on their structure/morphology, analyzed whether these segments correlated with differences in country characteristics, and then utilized HERMES to develop a detailed simulation model of three sample countries' supply chains and explore the cost and impact of various alternative structures.RESULTS:The majority of supply chains (34 of 57) consist of four levels, despite serving a wide diversity of geographical areas and population sizes. These four-level supply chains loosely fall into three clusters [(1) 18 countries relatively more bottom-heavy, i.e., many more storage locations lower in the supply chain, (2) seven with relatively more storage locations in both top and lower levels, and (3) nine comparatively more top-heavy] which do not correlate closely with any of the country characteristics considered. For all three cluster types, our HERMES modeling found that simplified systems (a central location shipping directly to immunization locations with a limited number of Hubs in between) resulted in lower operating costs.CONCLUSION:A standard four-tier design template may have been followed for most countries and raises the possibility that simpler and more tailored designs may be warranted.

Summary The definition of a methicillin-resistant Staphylococcus aureus (MRSA) outbreak in a healthcare facility is either the appearance or increase of 'several' (e.g., typically three or more) cases epidemiologically linked by person, time or place, or a substantial increase in the number of cases in a location. In many ways, the definition of a MRSA outbreak is an operational one: a situation in which a change in procedure is required to prevent further increases in the number of cases. Computational simulation modeling (generated through the Regional Healthcare Ecosystem Analyst) has demonstrated how outbreaks in a healthcare facility can propagate to other healthcare facilities in a region through patient sharing. Timely detection of outbreaks can be challenging, especially when routine MRSA surveillance is not being performed. Once an outbreak is detected, infection control specialists may conduct contact tracings to determine the source of the outbreak. Reporting of outbreaks varies considerably among facilities, locations and states. A facility experiencing a MRSA outbreak may not always report the outbreak to other facilities connected by patient sharing. Different factors can make an outbreak more likely to occur: unregulated antibiotic use, connections among locations and populations with MRSA, extensive and close person-to-person interactions, poor hygiene and sanitation, compromised skin integrity, and populations with comorbidities and immunosuppression. Each of the aforementioned outbreak risk factors has corresponding prevention and control measures: regulation of antibiotic prescribing, reduction of overcrowding, maintenance of appropriate hygiene and sanitation, MRSA surveillance, contact precautions, decolonization, better tracking of patient movement, regional coordination of outbreak control measures and additional safeguards for at-risk populations.

Although vaccine supply chains in many countries require additional stationary storage and transport capacity to meet current and future needs, international donors tend to donate stationary storage devices far more often than transport equipment. To investigate the impact of only adding stationary storage equipment on the capacity requirements of transport devices and vehicles, we used HERMES (Highly Extensible Resource for Modeling Supply Chains) to construct a discrete event simulation model of the Niger vaccine supply chain. We measured the transport capacity requirement for each mode of transport used in the Niger vaccine cold chain, both before and after adding cold rooms and refrigerators to relieve all stationary storage constraints in the system. With the addition of necessary stationary storage, the average transport capacity requirement increased from 88% to 144% for cold trucks, from 101% to 197% for pickup trucks, and from 366% to 420% for vaccine carriers. Therefore, adding stationary storage alone may worsen or create new transport bottlenecks as more vaccines flow through the system, preventing many vaccines from reaching their target populations. Dynamic modeling can reveal such relationships between stationary storage capacity and transport constraints.

Background: When addressing the urgent task of improving vaccine supply chains, especially to accommodate the introduction of new vaccines, there is often a heavy emphasis on stationary storage. Currently, donations to vaccine supply chains occur largely in the form of storage equipment.Methods: This study utilized a HERMES-generated detailed, dynamic, discrete event simulation model of the Niger vaccine supply chain to compare the impacts on vaccine availability of adding stationary cold storage versus transport capacity at different levels and to determine whether adding stationary storage capacity alone would be enough to relieve potential bottlenecks when pneumococcal and rotavirus vaccines are introduced by 2015.Results: Relieving regional level storage bottlenecks increased vaccine availability (by 4%) more than relieving storage bottlenecks at the district (1% increase), central (no change), and clinic (no change) levels alone. Increasing transport frequency (or capacity) yielded far greater gains (e.g., 15% increase in vaccine availability when doubling transport frequency to the district level and 18% when tripling). In fact, relieving all stationary storage constraints could only increase vaccine availability by 11%, whereas doubling the transport frequency throughout the system led to a 26% increase and tripling the frequency led to a 30% increase. Increasing transport frequency also reduced the amount of stationary storage space needed in the supply chain. The supply chain required an additional 61,269L of storage to relieve constraints with the current transport frequency, 55,255L with transport frequency doubled, and 51,791L with transport frequency tripled.Conclusions: When evaluating vaccine supply chains, it is important to understand the interplay between stationary storage and transport. The HERMES-generated dynamic simulation model showed how augmenting transport can result in greater gains than only augmenting stationary storage and can reduce stationary storage needs.