Assessing the social cost and benefits of a national requirement establishing antibiotic stewardship programs to prevent Clostridioides difficile infection in US hospitals

R. Scott; Rachel Slayton; Fernanda Lessa; James Baggs; Steven Culler; L. McDonald; John Jernigan

doi:10.1186/s13756-018-0459-1

Assessing the social cost and benefits of a national requirement establishing antibiotic stewardship programs to prevent Clostridioides difficile infection in US hospitals

Scott, R.; Slayton, Rachel; Lessa, Fernanda; Baggs, James; Culler, Steven; McDonald, L.; Jernigan, John 2019-01-22 00:00:00 Backgound: Economic evaluations of interventions to prevent healthcare-associated infections in the United States rarely take the societal perspective and thus ignore the potential benefits of morbidity and mortality risk reductions. Using new Department of Health and Human Services guidelines for regulatory impact analysis, we developed a cost-benefit analyses of a national multifaceted, in-hospital Clostridioides difficile infection prevention program (including staffing an antibiotic stewardship program) that incorporated value of statistical life estimates to obtain economic values associated with morbidity and mortality risk reductions. Methods: We used a net present value model to assess costs and benefits associated with antibiotic stewardship programs. Model inputs included treatment costs, intervention costs, healthcare-associated Clostridioides difficile infection cases, attributable deaths, and the value of statistical life which was used to estimate the economic value of morbidity and mortality risk reductions. Results: From 2015 to 2020, total net benefits of the intervention to the healthcare system range from $300 million to $7.6 billion when values for morbidity and mortality risk reductions are ignored. Including these values, the net social benefits of the intervention range from $21 billion to $624 billion with the annualized net benefit of $25.5 billion under our most likely outcome scenario. Conclusions: Incorporating the economic value of morbidity and mortality risk reductions in economic evaluations of healthcare-associated infections will significantly increase the benefits resulting from prevention. Keywords: Healthcare-associated infections, Clostridioides difficile infection, Antibiotic stewardship, Regulatory impact analysis, Value of statistical life, Cost-benefit analysis * Correspondence: DScott1@cdc.gov Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Diseases, Centers for Disease Control and Prevention (CDC), Roybal Campus, 1600 Clifton Road MS H16-3, Atlanta, GA 30329-4027, USA Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 2 of 17 Introduction As required by executive order, US federal regulatory Healthcare-associated Infections (HAIs) pose a serious agencies conducting economic analyses of regulations health threat to hospitalized patients with an estimated 4% impacting human health must take the societal perspective, of hospitalized patients in the United States (US) infected which includes measuring the economic benefit of morta- at any given time [1]. To mitigate this threat, actions are lity risk reductions [16]. The US Office of Management and being taken by a myriad of public health organizations in- Budget (OMB) Circular A-4, Regulatory Analysis (2003) cluding government agencies, professional associations, also directs regulatory agencies on use of the value of statis- private industry, and consumer groups. These actions in- tical Life (VSL) which is a monetized measure of the clude mandatory public reporting of hospital HAI rates additional cost that individuals would be willing to pay for and the formation of prevention collaboratives composed a small reduction in the risk of mortality [17]. An example of multiple hospitals working together to prevent HAIs from Robinson (2007) illustrates the VSL concept [18]. As- [2]. The US Congress passed several acts to empower suming a population of 100,000, if each individual indicates agencies to implement polices to combat HAIs including they would be willing to pay an average of $50 to prevent the funding of states to develop HAI prevention plans, one death (a risk reduction of 1/100,000), the VSL would hospital reporting of select HAIs rates to the Centers for be $50 X 100,000 or $5 million. Medicare and Medicaid Services (CMS), and a penalty VSL estimates are derived from survey methods in system to reduce hospital Medicare reimbursements for which respondents are asked what they would be willing high rates of HAIs [3–5]. Additionally, the Executive to pay for small changes in the risk of premature death Branch has launched efforts to coordinate various volun- (referred to as state-preference studies) or from statistical tary activities of both public and private sector stake- models that evaluate wage differentials for occupations holders to achieve national goals of reducing HAIs and with varying job-related mortality risks (referred to as antibiotic resistant infections [6, 7]. revealed-preference studies). Concerns have been raised As policy actions to combat HAIs and antibiotic drug about the accuracy of VSL estimates from stated- resistance increase, economic evaluations of how to effi- preference studies, particularly related to the potential for ciently achieve national goals are needed to help inform hypothetical bias. As respondents are responding to a policy decisions. However, conducting economic evalua- hypothetical market for risk reductions described within a tions of policies affecting HAIs have been challenging questionnaire, respondents may provide “spurious” re- given (1) the quality of hospital cost data, (2) the diver- sponses that do not reflect how they would respond to an gent cost perspectives (i.e. of patients, providers, third actual market, and thus overstate (or understate) what party payers, or society) that the analysis can take, (3) they would actually pay for a reduction in risk [19–21]. the methodological difficulties of assessing both short However, there have been advances in both survey design term and long-term attributable morbidities and mortal- and the use of statistical methods to make adjustments to ity, and (4) difficulties in identifying patients with HAIs responses if needed to minimize potential bias [20, 22]. having their onset post-discharge [8–10]. Historically, Also, comparison of VSL estimates between revealed- most HAI economic analyses have taken the cost per- preference and stated-preference studies have found that spective of the healthcare provider and /or administrator estimates from revealed-preference studies tend to be [9, 11]. These studies attempt to demonstrate the cost higher, which may be a reflection of how the perception of savings to hospital budgets resulting from prevented risk may differ between those who actually face the risk cases ofHAIssoastomakea ‘business case’ that in- (as in wage studies), and those who may discount their vestments in infection control reduce treatment costs own perceived individual risk to a hazard described in a and improve outcomes [12–14]. Using peer-reviewed survey [21]. While further research is needed to under- attributable cost estimates for select HAIs, a 2012 stand the discrepancies between VSL estimates from the meta-analysis found that the range of total direct two types of studies, certain types of mortality risk cannot medical costs to the US healthcare system due to be assessed using wage data (i.e. cancer) and require the hospital-onset central-line associated bloodstream in- use of stated-preference methods [20, 21]. fections, catheter-associated urinary tract infections, Until recently, only the US Environmental Protec- ventilator-associated pneumonia, surgical site infections tion Agency (2014) and the US Department of and Clostridioides difficile infection (CDI) was $8.3–$11.5 Transportation (2015) had issued their own guide- billion (2012 dollars) [15]. While this work is still import- lines to ensure that their use of VSL in internal ant, relying on analyses based on the provider cost per- analyses complied with OMB directives [23, 24]. The spective ignores the cost impacts to patients (travel costs, US Department of Health and Human Services lost wages, long term morbidities, insurance co-pays), (HHS) has now published its own guidelines (2017) third party payers (increased per-patient reimbursements), to inform and advise department agencies on the and to society (mortality). methods used in conducting regulatory impact Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 3 of 17 analysis that are consistent with standing executive implementation of an AS program) to prevent CDI in orders and OMB recommendations [25]. Medicare patients in acute care hospitals over a 5 year time Prior to the HHS guideline publication, CMS had pro- horizon [27]. The simulation model incorporated informa- posed a new rule to require that all hospitals certified by tion on projected hospitals discharges, infection incidence Medicare and/or Medicaid (4900 hospitals and 1300 crit- rates, intervention effectiveness, prevention program costs, ical access hospitals) establish and maintain antibiotic and Medicare reimbursements saved per case averted, with stewardship (AS) programs by 2020 as called for in the each model representing a cohort of 1000 patients and out- National Action Plan for Combating Antibiotic-Resistant comes assessed for 1000 trials. Using the cost perspective Bacteria [7, 26]. While the rule also proposed other of the federal government (as a third party payer), the na- more modest requirements related to patients right to tional financial savings to the Medicare program (under the services (regardless of race, color or national origin), em- base case scenario of 50% program effectiveness and a 3% ployment of an infection preventionist/infection control discount rate) was estimated to be $2.5 billion (2011 professional, mandated review of current infection con- dollars) with a credible range of $1.2 billion to $4.0 billion trol programs, and other nursing and medical record over the five year study period. services, the requirement associated with the highest To expand this analysis from a Medicare cost perspec- costs and benefits in the corresponding regulatory im- tive to reflect a societal cost perspective, we developed pact analysis was for AS programs. Using results from national estimates of the benefits of averted cases and an economic evaluation of a multifaceted intervention reduced mortality risk for all ages using results from (including AS programs) for CDI in US hospitals based Lessa et al. which derived population-based estimates of on a Medicare cost perspective, the regulatory impact the incidence and disease burden for (1) health analysis concluded that requiring AS programs would care-associated Clostridioides difficile (HCA-CDI) result in annual savings of over 1 billion dollars to hospi- (which included community-onset health care–associ- tals from reduced incidence of CDIs and drug cost sav- ated, hospital-onset, and nursing home-onset infections) ings from reduce inappropriate antibiotic prescribing (2) recurrent HCA-CDI cases (within 14 to 56 days after [26, 27]. However, it was noted in the regulatory impact the initial occurrence) stemming from these infections, analysis that the potential societal benefits of reduced and (3) the number of deaths occurring within 30 days non-fatal CDI illness and the societal benefits and costs after the diagnosis of HCA-CDI [28]. The net present of reduced fatal CDI illness had been ignored due to lack value (NPV) model is defined as: of information on these benefits. A request for this 5 Benefits þ Costs information to be considered in the analysis of the fina- t NPV ¼ ð1Þ t¼0 lized rule was made. ðÞ 1 þ r Our objective is to provide estimates of both the morbid- ity and mortality risk reductions associated with an active where: hospital AS program and enhanced infection control by Benefits = the total benefits arising in year t (t = expanding the previously mentioned Medicare analysis to 0,1,2,3,4,5), incorporate a societal cost perspective. The VSL estimates, Costs = the total costs arising in year t (t = 0,1,2,3,4,5), the methods for assessing intervention costs, and the and. methods for deriving the value of morbidity risk reductions r = the social discount rate (3 and 7%). from the VSL are taken from the new HHS guidelines. The The analysis of the net present value model was con- possible ramifications of incorporating the societal perspec- ducted using Excel for Windows 2016. tive in evaluations of HAI prevention programs, and their The study horizon for the measurement of costs and interpretation by stakeholders and policy makers, will be benefits in our societal analysis is a 6 year period begin- discussed. ning in 2015 through 2020 to be consistent with the published CMS analysis [26]. Unlike the Slayton analysis, Methods we assumed a 3 month lag before benefits start to accrue This analysis relied on the results of two recent CDI studies in 2015 to account for the time needed to implement to develop a net present value model to assess the social the intervention. Also, in contrast to the regulatory im- costs and benefits of a multifaceted CDI prevention pro- pact analysis done by CMS, we calculated the cost of the gram including the economic value of reduced mortality intervention to cover expenses to all inpatient prospec- risks (see Appendix for model details) [27, 28]. Our tive payment system (IPPS) hospitals as opposed to just economic model is partly based on a decision analytic 60%. The CMS analysis took into account that 40% of (Markov) model developed by Slayton et al. to measure the IPPS hospitals had already established ongoing AS pro- net benefits of a multifaceted intervention (enhanced grams, but we would argue that without the proposed hospital infection control practices coupled with the requirement, hospitals would be free to suspend these Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 4 of 17 programs given changes in any clinical or financial con- infections, and HCA-CDI associated deaths were made ditions facing the hospital [29]. To avoid any potential using 2014 US Census Bureau projections for the US biases by limiting the coverage of the AS requirement, population for 2015 through 2020, in each of the four we fully assessed all cost and benefits that would accrue age strata: 1–17, 18–44, 45–64, and 65 and over (Table 2) to all hospitals subject to the rule. [32]. The estimates of the direct medical cost savings due to averted HCA-CDI and recurrent CDI cases were Cost of prevention made by multiplying the averted cases by estimates of The monetary unit costs of the CDI intervention program the attributable cost savings for hospital-onset and re- were taken from the Markov decision model and adjusted current cases found in Kwon et al. (2012) [33]. While to 2015 dollars using the Consumer Price Index for Urban using the cost savings of averted hospital-onset cases as Consumers (CPI-U) (see Table 1)[27, 30]. Total interven- a cost surrogate for all HCA-CDI cases, the Kwon ana- tion costs included the cost of implementing and staffing lysis showed that hospital-onset costs tend to be lower an AS program for HCA-CDI prevention (25% of total AS and thus serves as a conservative estimate of the cost labor costs), the cost of implementing the Antimicrobial savings from averted HCA-CDI cases. Use (AU) Option of the Antimicrobial Use and Resistance For valuing mortality risk reductions, the HHS guide- module of the National Healthcare Safety Network (the lines provide a range of VSL estimates to reflect the vari- required data platform from CMS for assessing antibiotic ability in these estimates from published studies and to prescribing), the federal government investment to hospi- promote the use of sensitivity analysis to assess the impact tals to support adoption of AS programs, the cost to hos- study on results. We used a low, a central and a high VSL pitals for patient isolation, hand hygiene, and enhanced estimate for the years 2015 to 2020, where we adjusted environmental cleaning. As investments in AS programs from 2014 VSL estimates to 2015 dollars using the CPI-U and the AU module are broad-based interventions that (Table 2) as recommended by the HHS guidelines. The can target other healthcare-associated organisms and guidelines also suggest that VSL estimates be adjusted to reduce unnecessary drug use, only 25% of these costs are reflect changes in real income growth in future years attributed to CDI prevention. To be consistent with the (2015–2020), which we adjusted by 1.3% according to pro- HHS guidelines, all labor costs that did not include jections by the Congressional Budget Office [34]. Using overhead were doubled. Costs were expressed as costs per the low, central and high VSL estimates to provide a range hospital discharge using 2015 discharges from National of benefits, each estimate was applied to every observed Inpatient Sample from the Healthcare Cost and death regardless of age in accordance with the guidelines. Utilization Project as a base (35,232,942 discharges) and The total economic value of the reductions in the risk of were combined with trend projections of the number of death due to the prevention program is estimated by hospital discharges for 2015 through 2020 (see appendix multiplying the discounted estimate of the number of for additional details) to estimate total prevention costs deaths averted in each year (2015–2020) by the corre- [31]. As recommended by the HHS guidelines, the ana- sponding discounted estimate of VSL and then summing lysis used discount rates of 3 and 7%. across the years. The difference in the per-discharge cost of isolation For this analysis, we estimated the value of morbidity and infection control ($3.56 versus $6.41) is the result of risk reductions by assuming that the incidence of all the different effectiveness levels and their impact on the cases (including recurrent cases) was mild or moderate. number of HCA-CDI cases that will need enhanced While cases of HCA-CDI can result in fulminate colitis infection control. At 50% program effectiveness, the (approximately 16%) and recurrent cases can experience number of cases that will need enhanced infection con- up to 14 recurrent episodes, the source for our burden trol is reduced by half, while at the 90% effectiveness estimates did not categorize cases according to their dis- level, the number of cases that will need enhanced infec- ease severity or track when cases experienced multiple tion control is only reduced by 10%. occurrences through time [28, 35–38]. Thus, our valu- ation of the morbidity risk reductions are conservative Benefits and should be considered a lower bound. As such, each Total benefits include the attributable patient treatment case will only experience a decrease in utility once cost savings from averted HCA-CDI cases, an estimate within each analytical year. of the reduce hospital expenditures (cost savings) on an- To value morbidity risk reductions, the HHS guidelines tibiotics due to AS oversite, and the value of morbidity recommends the use of willingness-to-pay estimates that and mortality risk reductions reflected in reduced cases measure the maximum amount individuals would give up and deaths due to the prevention of HCA-CDI. Using in income to pay for reducing the risk of illness. As no the age-stratified incidence rates from the Lessa study, such estimates exists for HCA-CDI, the guidelines recom- estimates of the number of HCA-CDI, recurrent mend the use of monetized quality-adjusted life years Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 5 of 17 Table 1 Net Economic Benefits Model for CDI Prevention: Model Inputs (2015 $) Incidence Rates [28] HCA-CDI rate per Recurrence Rate Per Death Rate Per 100,000 persons 100,000 persons 100,000 persons Age Group 1–17 6.3 0.4 NA 18–44 18.3 3.0 NA 45–64 83.1 10.9 5.4 ≥ 65 481.5 117.6 55.1 Effectiveness of the Multifaceted CDI Intervention [27] 10 and 50% % of Total Deaths Due to CDI [28, 33] 35 and 50% Cost Inputs [27] Cost Per Hospital Discharge Infection Control and Isolation Costs $3.56 (50 effectiveness), –6.41 (10% effectiveness) Implementation of the Antimicrobial Use (AU) module Initial Cost (in 2015) $0.08 Ongoing Costs $0.03 Antibiotic Stewardship Personnel (1.2 Pharmacists +0.67 Infectious Disease Physician + 0.05 $20.58 Network Data Analysis) × 0.25 Federal Government Investment Initial Cost (2009–2014) $0.18 Ongoing Costs (2015) $0.03 Cost of Enhanced Cleaning $0.28 Total Initial cost (2015) 50% program effectiveness $24.68 10% program effectiveness $27.52 On-going cost (2016–2020) 50% program effectiveness $24.47 10% program effectiveness $27.32 Benefits of Prevention Attributable Patient Cost Savings HCA-CDI [27] $ 6844 (Per Prevented Case) Recurrent CDI [27] $12,703 (Per Prevented Case) Length Of Hospital Stay (LOS) Mild/Moderate HCA-CDI Disease 9.5 days Recurrent Disease 8.8 days QALY/ adjusted to QALD by LOS Mild/Moderate HCA-CDI Disease 0.80 / 0.005205479 Recurrent Disease 0.708 / 0.00704 HCA-CDI healthcare-associated Clostridioides difficile infection, CDI Clostridioides difficile infection, QALY quality-adjusted life year, QALD quality adjusted life day, LOS length of hospital stay, AU the Antimicrobial Use Option of the Antimicrobial Use and Resistance (AUR) module of the National Healthcare Safety Network (NHSN) Only 25% of total stewardship program and federal government investment costs were attributed to CDI prevention activities as these prevention efforts also involve other multi-drug resistant organisms Length of stay for mild/moderate HCA-CDI comes from Gabriel and Beriot-Mathiot while length of stay for recurrent CDI comes from McFarland et al. [37, 44] From Sullivan et al. the QALY weight selected for mild/moderate HCA-CDI disease is the 25th percentile EQ-5D index score which reflects a population that is older, with more comorbidities, and with a lower socio-demographic profile of respondents in the Medical Expenditure Panel Survey (MEPS) survey [42]. The QALY weight for recurrent HCA-CDI disease corresponds to the 25th percentile EQ-5D score for those older MEPS respondents with “Other Gastrointestinal Disorders” (clinical classification category 155). These weights are adjusted by the LOS associated with HCA-CDI and recurrent CDI disease to reflect the short term, acute nature of mild/moderate CDI disease (See the appendix for the adjustment formula to convert QALY to QALD). Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 6 of 17 Table 2 Model Inputs: Cases and Deaths Averted; VSL, Value Per QALY, Value per QALD 2015–2020 (2015 $) 7% discount rate 3% discount rate 7% discount rate 3% discount rate, 10% effectiveness 10% effectiveness 50% effectiveness 50% effectiveness Cases Averted Inpatient Cases Averted HCA-CDI 167,699 184,749 838,493 923,743 Recurrent 35,892 39,555 179,460 197,775 Total 203,591 224.304 1,017,953 1,121,518 Deaths Averted 35% Attributable Mortality 5625 6199 28,123 30,996 50% Attributable Mortality 8035 8856 40,176 44,280 VSL Estimates (2015 $) 3% DR 2015 2016 2017 2018 2019 2020 Low $4,500,000 $4,368,932 $4,335,941 $4,301,166 $4,175,889 $4,140,522 Central $9,400,000 $9,320,388 $9,143,180 $8,968,388 $8,884,870 $8,712,349 High $14,400,000 $14,174,757 $13,950,419 $13,727,125 $13,505,003 $13,370,436 7% DR 2015 2016 2017 2018 2019 2020 Low $4,500,000 $4,236,288 $3,959,148 $3,782,363 $3,611,765 $3,375,481 Central $9,400,000 $9,037,415 $8,534,163 $8,140,304 $7,684,607 $7,253,694 High $14,400,000 $13,744,402 $13,021,197 $12,333,794 $11,680,602 $11,060,088 Value Per QALY (2015 $) 3% DR 2015 2016 2017 2018 2019 2020 Low $222,833 $216,343 $214,709 $212,987 $206,784 $205,032 Central $465,474 $461,532 $452,757 $444,101 $439,966 $431,423 High $713,067 $701,913 $690,804 $679,747 $668,748 $662,084 7% DR 2015 2016 2017 2018 2019 2020 Low $394,645 $377,023 $352,358 $336,466 $321,144 $300,135 Central $833,140 $786,831 $750,676 $708,725 $669,051 $637,787 High $1,271,634 $1,204,835 $1,141,334 $1,080,985 $1,023,648 $969,186 Value Per QALD (2015 $) 3% DR - Cases 2015 2016 2017 2018 2019 2020 Low $1160 $1126 $1118 $1109 $1076 $1067 Central $2423 $2402 $2357 $2312 $2290 $2246 High $3712 $3654 $3596 $3538 $3481 $3446 3% DR - Recurrent 2015 2016 2017 2018 2019 2020 Low $1569 $1523 $1512 $1499 $1456 $1443 Central $3277 $3249 $3187 $3126 $3097 $3037 High $5020 $4941 $4863 $4785 $4708 $4661 7% DR - Cases 2015 2016 2017 2018 2019 2020 Low $2054 $1963 $1834 $1751 $1672 $1562 Central $4337 $4096 $3908 $3689 $3483 $3320 High $6619 $6272 $5941 $5627 $5329 $5045 7% DR - Recurrent 2015 2016 2017 2018 2019 2020 Low $2778 $2654 $2481 $2369 $2261 $2113 Central $5865 $5539 $5285 $4989 $4710 $4490 High $8952 $8482 $8035 $7610 $7206 $6823 VSL value of statistical life, QALY quality-adjusted life year, QALD quality-adjusted life day, DR discount rate. To calculate the number of cases, we took national incidence rates from Lessa et al. (2015) and applied them to projections of the US population (by year of age) for 2015–2020 (United States Census Bureau 2014)) [24, 28]. Rates of attributable CDI mortality were derived by the authors’ based on analysis by Kwon et al. [33]. The base estimates for the VSL were taken from the new HHS guidelines for conducting regulatory impact analysis (HHS 2017) [25]. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 7 of 17 (QALYs) as a surrogate measure. To construct this value for mild/moderate HCA-CDI disease ranged from $1067 for morbidity risk reductions in the future, four pieces of to $8952. (Table 2). information are needed: (1) the remaining years of life ex- Another potential benefit from AS programs is the re- pectancy, (2) a health-related quality of life (HRQL) asso- duction in hospital expenditures for antibiotics as these ciated with each expected year of life that declines in programs reduce antibiotic prescribing through more quality of life due to age, (3) the probability of survival in appropriate use [45, 46]. While the Slayton study did not each expected year of life, and (4) a monetary value per consider these benefits, the CMS analysis derived an an- expected (monetarized) QALY (derived from the VSL). nual estimate of $520 million (in 2003 dollars) in these Items 1–3 are used to calculate expected QALYs associ- savings based on information from a single study of a ated with age by first multiplying the HRQL in each ex- 124 bed hospital [47]. To obtain a more robust estimate pected year of life by the probability of living in that year of the expected reduced antibiotic expenditures by hos- (i.e., by the survival curve) [39, 40]. The discounted sum pitals, we used a 2009 estimate of total antibiotic expen- of these expected QALYs are calculated over the ditures by US acute care and long-term care hospitals remaining years of life expectancy using the same discount ($3.6 billion); adjusted it to 2015 dollars ($3.9 billion) rates of 3 and 7%. For our study population, we assumed using the CPI-U; and then multiplied this estimate by a an average age of 40 with a life expectancy of 50 years. To representative percentage savings in annual antibiotic get the monetarized QALY, the VSL estimates were then expenditures taken from published studies [26]. In divided by the expected QALYs [41]. Our estimates for reviewing studies of US hospitals, the cost reductions the monetary value per expected QALY for 2015–2020, ranged from 10 to 37% [47–54]. For our model, we used based on the low, central and high VSL estimates, ranged a reduction in antibiotic costs of 20% which resulted in from $223,000 to $1.27 billion (Table 2). an annual estimated savings of $787 million in antibiotic To derive an estimate of the monetary value of redu- expenditures. After adjusting for the 3-month lag in cing the risk of getting a mild/moderate disease, HRQL 2015, the total discounted value of these savings for weights associated with HCA-CDI and recurrent CDI 2015–2020 was $4.2 billion (3% discount rate) and $3.8 disease are needed to adjust the monetary value per billion (7% discount rate). QALY. As there are no published HRQL weights specif- ically for HCA-CDI disease, we selected surrogate com- Sensitivity analysis munity preference-based EQ-5D index weights from The model is evaluated using two intervention effective- Sullivan et al. (2006) [42]. For mild/moderate HCA-CDI ness scenarios of 50 and 10% (the same base and lower cases, we selected the unadjusted 25th percentile EQ-5D bound model values as used in Slayton) while assuming score (taken from the Medical Expenditure Panel Survey full program implementation costs for each level [27]. 2000–2002) of 0.80 which can be interpreted as reflect- While the projected number of HCA-CDI associated ing a patient population that is older with more comor- cases and deaths were adjusted by 50 and 10% to reflect bidities, which have been found to be risk factors for program effectiveness, associated deaths were further HCA- CDI disease [43]. For mild/moderate recurrent adjusted by 50 and 35% to provide a credible range of cases, we selected the unadjusted 25th percentile EQ-5D the attributable proportion of HCA-CDI deaths due to score for other gastrointestinal disorders (Chronic Clas- CDI disease that reflects the limitations of current sification Condition 155) of 0.708, which reflects an methods used to attribute these outcomes [28, 33, 55]. older patient profile with these diseases, to act as a sur- Consistent with HHS guidance, our sensitivity analysis rogate for patients with recurrent CDI disease. The also includes calculations using a low ($4.5 million), cen- EQ-5D weights are adjusted to quality-adjusted life days tral ($9.4 million) and high VSL estimate ($14.4 million) (QALD) and then multiplied by the associated hospital for 2015 (Table 2). As derived from the low, central and lengths of stay for acute episodes of HCA-CDI and re- high estimates of the monetary value for a QALY, the current cases (9.5 and 8.8 days respectively from Table 1) monetary value of QALD used in value reduction of to obtain an adjusted QALD lost from avoided morbidity risks ranged from $1160 to $8952 to reflect HCA-CDI and recurrent infections (0.0052 and 0.007 re- the short duration of mild/moderate CDI disease. As spectively from Table 1 and Appendix)[37, 44]. These recommended by the HHS guidelines, the analysis used weights are then used to adjust the monetary value per discount rates of 3 and 7%. QALY to get a monetary value per lost QALD from As VSL estimates in the HHS guidelines are derived mild/moderate HCA-CDI and recurrent disease. To ob- for a population between the ages of 18 to 65, the guide- tain the total value of morbidity risk reductions, the lines also recommend that when the affected population number of HCA-CDI and recurrent cases are then is very old, additional sensitivity analysis should be done multiplied by their respective monetary value per lost using monetized QALY values. These values are then QALD. Our estimates for the monetary value of QALD multiplied by the expected value of the number of life Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 8 of 17 years gained (which will be smaller for older popula- intervention costs and the benefits of both the direct tions). As 62% of cases and 84% of deaths in the Lessa medical cost savings and the value of morbidity risk re- study occurred in patients 65 and over, we developed ductions as the total net benefits are substantial. The monetized QALY values using an age range of 65 to 90 proportion of the economic value of mortality risk re- years (as opposed to ages 40 to 90 years used to obtain the ductions to total benefits ranged from 80% (lowest total monetized QALYs for the morbidity risk values described benefit estimate) to 97% (highest total benefit estimate). above) to be used in place of both the VSL estimates and Even with VSL estimates that have been age adjusted the previous estimates for the value of morbidity risk re- to reflect the older age distribution of HCA-CDI pa- ductions. A description of how these alternative estimates tients, the intervention still produced a range of total were derived can be found in the Appendix.From Table 6, net benefits of $3.7 billion to $14.5 billion under our the monetarized QALYs for mortality risk reductions lowest effectiveness scenario (10% program effectiveness, ranged from $342,948 to $1,578,597, while the monetized 35% attributable mortality proportion) (Table 7). As this QALYs for morbidity risk reduction ranged from $1643 to same intervention program achieved an 80% reduction $11,113. in hospital CDI cases in England from 2008 to 2012, the credible range of net benefits is $21.2 billion to $625.8 Results billion which are associated with the 50% program ef- In our high estimate scenario based on 50% program ef- fectiveness scenarios (Tables 3 and 7)[56–58]. Given the fectiveness and attributable mortality, the national inter- evidence on program effectiveness and the theoretical vention was projected to avert 1,017,953 total inpatient and empirical uncertainties associated with age-adjusted cases and prevent 40,176 deaths using a 7% discount VSL estimates, we suggest that a likely, but conservative, rate, and 1,121,518 total inpatient cases and 44,280 scenario outcome from a public policy perspective is deaths using a 3% discount rate over the study period $121.4 billion in total net benefits (50% program effect- (Table 2). Using the low estimates of program effective- iveness, 3% discount rate, 35% attributable mortality ness and attributable mortality (10 and 35% respect- proportion, and the low VSL estimate). This translates ively), the model projects that 203,591 total inpatient to an annualized net benefit of approximately $25.5 cases and 5625 deaths were averted at the 7% discount billion. rate, and 224,304 total inpatient cases and 6199 deaths were averted at the 3% discount rate. Discussion Also from Table 2, the projected cost of the prevention The societal cost perspective has rarely been considered program ranged from $3.3 billion (7% discount rate) to in economic evaluations of HAI prevention programs, $4.2 (3% discount rate) at 50% program effectiveness. At but doing so in accordance with HHS guidelines for 10% program effectiveness, the program costs ranged conducting regulatory impact analysis may provide from $3.7 billion (7% discount rate) to $4.7 billion (3% stakeholders and policy makers a broader view on the discount rate). The higher costs under the 10% program benefits of such programs. As our intervention costs effectiveness scenario arise from the increased costs ranged from $3.3 billion to $4.2 billion, these costs associated with implementing the enhanced infection would have to be at least 28 times larger to overlap with control practices (to avoid transmission) around the our lowest benefit estimate of $121.4 under the 50% pro- remaining 90% of cases (as opposed to 50% of cases). gram effectiveness scenario (Table 3) and would have to Without considering the value of morbidity and mor- quintuple to overlap with our lowest benefit estimate of tality risk reductions, the net benefits in reduced patient $21.2 when using age-adjusted VSL estimates and the care costs and reduced antibiotic expenditures from the 50% program effectiveness scenario (Table 7). intervention ranged from $8.1 billion to $9.1 billion While the benefits of reducing mortality risks comprised (subtracting direct medical cost savings of $13.3 billion a vast majority of total net social benefits in our model, a and $11.4 billion from the intervention costs of $4.2 and number of other relevant benefits (cost savings) associated $3.3 billion respectively) under the 50% intervention ef- with CDI disease are ignored in this analysis. The most fectiveness scenarios, but these net benefits decreased in important of these include non-hospital medical costs the 10% effectiveness scenarios to $1.3 billion (3% dis- (e.g., outpatient treatment and pharmacy costs), medical count rate) to $1.6 billion (7% discount rate) (Table 3). costs and the value of morbidity risks due to severe or However, when the values for morbidity and mortality long-term morbidities, lost labor productivity, and the risk reductions were included, net benefits from the economic impacts on family/caregivers. More broadly, AS intervention ranged from $24 billion to $626 billion programs also produce spillover benefits in terms of redu- across all scenarios (Table 3). The inclusion of the eco- cing rates of antibiotic resistance, although such economic nomic value of mortality risk reductions using VSL esti- impacts are diffuse and difficult to quantify and require mates overwhelmed the difference between the more research to understand their full effects. However, Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 9 of 17 Table 3 Benefits and Costs of a Comprehensive CDI Prevention Program 2015–2020 (2015 $) 7% discount rate, 3% discount rate, 7% discount rate, 3% discount rate, 10% effectiveness 10% effectiveness 50% effectiveness 50% effectiveness Total Intervention Costs (in billions) $3.7 $4.7 $3.3 $4.2 Total Benefits (in billions) Direct Medical Cost Savings Savings in Patient Care Costs $1.5 $1.8 $7.6 $9.1 Savings in Hospital Expenditures $3.8 $4.2 $3.8 $4.2 for Antibiotics Benefits of Morbidity Risk Reduction Low $/Lost QALY from HCA-CDI $0.4 $0.3 $2.1 $1.3 Central $/ Lost QALY from HCA-CDI $0.8 $0.6 $4.1 $2.8 High $/ Lost QALY from HCA-CDI $1.3 $0.8 $6.3 $4.2 Benefits of Mortality Risk Reduction Low VSL 35% Attributable Mortality Proportion $22.1 $26.6 $111.2 $133.2 50% Attributable Mortality Proportion $31.5 $38.0 $157.7 $190.2 Central VSL 35% Attributable Mortality Proportion $46.6 $56.2 $232.8 $280.8 50% Attributable Mortality Proportion $66.5 $80.2 $332.6 $401.1 High VSL 35% Attributable Mortality Proportion $71.1 $85.7 $355.7 $428.7 50% Attributable Mortality Proportion $102.7 $122.5 $507.8 $612.5 Range of Total Net Benefits Low $24.1 - $33.5 $28.2 - $ 39.6 $121.4 - $167.9 $143.6 - $200.6 Central $49.0 - $68.9 $58.1 - $ 82.1 $245.0 - $344.8 $292.7 - $413.0 High $74.0 - $105.6 $87.8 - $124.6 $370.1 - $522.2 $442.0 - $625.8 HCA-CDI healthcare-associated Clostridioides difficile infection, VSL value of a statistical life, QALY quality-adjusted life year. the addition of these potential benefits just provides more their infection control program and (2) conduct a review support for the proposed rule. As the range of credible of their infection control program [26]. Other changes costs and benefits did not overlap, we did not perform a affecting the writing of restraint and seclusion orders for Monte Carlo simulation in our sensitivity analysis. violent/self-destructive patients by a licensed practi- Regardless of the type of interventions taken for HAI tioner, the granting of dietary ordering privileges to prevention, the inclusion of monetary valuations for qualified dieticians or nutritionist professionals (in crit- morbidity and mortality risk reductions in cost-benefit ical access hospitals), and the implementation of quality analyses of HAI disease prevention potentially provide assessment and improvement programs were also pro- economic justification for interventions that might not posed. While lacking data on the potential cost savings otherwise be considered cost saving, as illustrated by this associated with many of these changes and ignoring the analysis. Our model shows that the total net benefits economic value of morbidity and mortality risk reduc- from having AS programs and enhanced infection con- tions from averted infections, the accompanying regula- trol are significantly large enough to cover the total in- tory impact analysis concluded that the overall proposal vestment costs in AS programs (as opposed to just 25% resulted in a net benefit to society of $284 million with in our model) as the total annualized intervention cost the majority of costs coming from changes affecting in- (totaling $14.6 billion for 2015–2020 at a 3% discount fection control programs and the addition of AS pro- rate) is only $2.7 billion. grams. Regardless of the differences in the calculated Along with the requirement to include AS programs, cost of the proposed CMS rule and the cost of our CDI CMS also proposed additional changes to the conditions prevention program, the additional economic benefits of of participation including a requirement that hospitals, morbidity risk reductions alone can be readily applied to (1) identify a qualified infection preventionist or infec- the CMS analysis and would significantly increase the tion control professional as an officer responsible for magnitude of net benefits. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 10 of 17 A potential limitation to VSL studies of HAI prevention estimates, especially for mortality risk reductions related is the variability in methodology and quality of studies that to air quality improvements, have been developed for generate attributable mortality estimates given the range many locations, including Europe, Asia and Australia of published estimates currently available [33]. The dom- [67–69]. While the National Health Service in the inance of VSL estimates in the calculation of benefits United Kingdom relies on cost-effectiveness analysis highlights the need for attributable mortality measures in their decision-making for assessing adoption of new that accurately and consistently reflect the mortality im- healthcare interventions, HM Treasury has had guide- pacts of HAIs. Additional research is needed to improve lines (referred to as “The Green Book”)for govern- the measurement of attributable mortality associated not ment ministries on how to incorporate the “Value of a only with HAI but with any cause of disease-related death. Prevented Fatality” (another name for the VSL) in pol- Given the decades of research on the VSL, the meas- icy assessments and evaluations of government actions urement of the VSL and the estimates currently used that involve risks to life and health [70]. To better are generally accepted for injury-related risk reduction understand the characteristics of VSL estimates from while the evidence suggests that VSL estimates for different countries, researchers at the Organization for illness-related risks are probably similar [59]. How- Economic Co-operation and Development (OECD) ever, the quality of data on mortality associated with have conducted a meta-analysis of VSL estimates re- disease should also be scrutinized as the new HHS lated to environmental, health and transport polices guidelines do not directly address this issue. Use of (from stated-preference studies) that have been done unadjusted-crude mortality estimates, as opposed to around the globe [69, 71]. OECD has also published a age-adjusted or risk-adjusted estimates can produce VSL user’s guide to better inform policy makers on significantly different estimates of the benefits of mor- how to incorporate VSL in policy decisions [72]. The tality risk reductions in cases of HAI [55, 60]. median and mean VSL values (using the full dataset) In the case of CDI disease, an important factor that of $2.4 and $7.4 million (in 2005 US dollars), along likely increased attributable mortality from 2000 through with other evidence from this study, can potentially be 2010 was the emergence and spread of the epidemic, adopted for use in a global economic assessments of hyper-virulent, North American Pulse-field type 1 antimicrobial resistance [69]. (NAP1) or ribotype 027 strain [61, 62]. Although de- clines of the NAP1/027 strain in the United States have not been as dramatic as that seen in places like England, Conclusion where declines have occurred, it is likely that declines in Although progress has been made, HAIs still pose a attributable mortality have followed [58, 63]. Ironically, serious threat to patients across healthcare settings. A however, it may be AS, alone or in combination with in- recent study on the prevalence of HAIs in US acute care fection control, that has led to most dramatic declines in hospitals estimated that the total number of HAIs NAP1/027 [64]. In addition, the development of new occurring annually was 722,000, of which there were therapies and recommendations for generally more ag- 75,000 HAI-associated deaths [1, 73]. Along with CDI, gressive treatment of HAIs, like CDI, may also result in carbapenem-resistant Enterobacteriaceae (CRE) has been declines in attributable mortality but also add their own classified as an urgent threat to the public health in the costs that must be considered [64, 65]. However, in the National Action Plan for Combating Antibiotic-resistant case of AS there may be additional impacts on morbidity Bacteria (White House 2015) [7]. The Plan has set tar- and mortality that are yet to be fully understood as gets for a reduction in incidence of both CRE and CDI, growing evidence suggests that the effect of unnecessary calling for 60% reduction in hospital-acquired CRE and antibiotics on the microbiome may result in other ad- 50% in overall CDI infections by 2020. verse outcomes among hospitalized patients such as sep- Our study accounted for the economic value of sis [66]. Such impacts, even if only partially realized, morbidity and mortality risk reductions, components could even more dramatically sway the cost benefits in of the total societal health benefits that have not pre- favor of aggressive stewardship interventions. viously been included in cost-benefit analyses of HAI While our analysis illustrates the potential impact on prevention programs. As the US federal government benefits measurement when the value of mortality risks intensifies its efforts to control antibiotic resistant in- are considered with regulations that impact health, this fections, our results suggest that these ambitious analysis also illustrates the role that cost-benefit analysis, goals can produce very large net societal benefits. As as opposed to cost-effectiveness analysis, has in rule these benefits accrue mostly to patients, policy making that involves public health and safety by the US makers can address how the burden for the additional Federal Government. The cost-benefit analysis described prevention costs should be shared among patients, here can easily be adopted by other countries as VSL third party payers and healthcare providers. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 11 of 17 Appendix their proportion to total population (see Table 4). The data used to calculate the probability of survival were taken I. Hospital discharges used to calculate intervention from 2013 life tables for males and females in the US costs. (actually expressed as the probability of dying between ages x to x + 1 in Table 5)[36]. Table 5 also shows the data The number of hospital discharges used to help calcu- used to calculate Pop_QALY where the SF-6D is multi- late intervention costs were from the National Inpatient plied by (1) probability of surviving to the next year Sample (NIS).[27] We fitted a linear trend line over the (Age_QALY which is calculated by the formula 1 - number of yearly discharges for 2003 To 2014 which was probability of dying between ages x to x + 1), and (2) the then used to extrapolate annual discharges for 2015 to population weight for male and females respectively 2020. The equation for the estimated fitted trend line was: (Pop_Wt which is the population weighted QALY for females and males (F_QALY and M_QALY). The final y ¼ −188; 575ðÞ discharges QALY for the population (Pop_QALY) is the addition of þ 40; 000; 000 where i F_QALY and M_QALY. Once applied to formula 2,the ¼ðÞ 2003; 2004;:…; 2014 ; ð1Þ willingness-to-pay per QALY were calculated. For 2 example, the 2016 VSL low estimate of $4,368,932 (3% R (coefficient of determination) = (0.51) discount rate) is divided by the discounted sum of The extrapolated number of discharges used to calculate Pop_QALY. This calculation ($4,368,932/ 20.1945) results intervention costs per discharge for 2015–2020 was in a willingness-to-pay for a QALY of $216,343. At the 7% 35,170,243; 34,981,668; 34,793,093; 34,604,518; 34,415,943; discount rate, the discounted sum of Pop_QALY is and 34,227,368 respectively. To calculate the per discharge 11.4027. These results are presented in Table 2. initial year costs (2015) from federal investments to pro- mote AS (which took place between 2009 and 2014), the III. Deriving the Quality-Adjusted Life Days and the total costs of these investments over these years was di- Value of Morbidity Risk Reductions for Mild/Mod- vided by the predicted number of discharges for 2015 erate CDI Disease (35,232,942) based on the trend analysis. As we lack the incidence data to predict the cases of II. Deriving the Monetarized Value or Willingness-to- severe HCA-CDI, we have assumed that, at a minimum, Pay per Quality-Adjusted Life Year (QALY). all cases are at least mild or moderate. To estimate the value of morbidity risk reductions for mild/moderate We followed HHS guidelines to calculate the HCA-CDI disease, we selected published community willingness-to-pay (WTP) or dollars for an expected preference-based QALY weights (EQ-5D scores) to rep- QALY. The WTP for a QALY formula (where r is the resent the decline in quality of life due to a case of discount rate) from Hirth et al. is: HCA-CDI and also recurrent disease [38]. Lacking a QALY weight specifically for HCA-CDI disease, the sur- Pop QALY X tþ50 Value of Stisitical LifeðÞ VSL ¼ ; rogate measures used included (1) 0.80 - the unadjusted ðÞ 1 þ r t¼0 EQ-5D score for the 25% percentile for the general sam- ð2Þ ple from the Medical Panel Expenditure Study (MEPS) where we have assumed the average population age is 40 to represent the baseline quality of life for patients that and expected life expectancy (probability of survival) is cal- could get an HCA-CDI infection, and (2) 0.704 – the culated for the next 50 years [37]. To derive the final qual- unadjusted EQ-5D for the 25th percentile for MEPS ity weights (Pop_QALY ) to be used in the above respondent reported having other gastrointestinal disor- t+ 50 formula, each additional year of life must be adjusted ders (Chronic Disease Classication 155). As the length of (multiplied) by (1) a QALY weight to reflect the decline in hospital stay for either a case or recurrence of CDI is on quality of life with age, (2) the conditional probability of average 9.5 days and 8.8 days respectively, these QALYs survival into the next year, and (3) a population weight must be adjusted down due to the acute nature of that is a weighted average of the QALY weights for males HCA-CDI and recurrent CDI disease. Eqs. 3 and 4 were and females based on proportion of the population. The used to make these adjustments QALY weights associated with male (M_QALY) and fe- males (F_QALY) age 40 to 90 are the SF-6D scores taken Lost QALYs from HCA−CDI Disease ¼ from Hanmer and Kaplan [35]. As our study did not have Length of Hospital Stay gender-stratified incidence rates for HCA-CDI, we con- ½ 1 −ðÞ EQ − 5D case structed a population weighted QALY weight (Pop_QALY) ð3Þ that combined the QALYs for males and females based on Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 12 of 17 Table 4 Population by Age and Sex: 2013 (Numbers in thousands, civilian noninstitutionalized population ) Age Both sexes Male Female Number Cell Percent Number Cell Percent Row Percent Number Cell Percent Row Percent All ages 311,116 100.0 152,335 100.0 0.490 158,781 100.0 0.510 .40 to 44 years 20,657 6.6 10,162 6.7 0.492 10,495 6.6 0.508 .45 to 49 years 21,060 6.8 10,319 6.8 0.490 10,742 6.8 0.510 .50 to 54 years 22,386 7.2 10,926 7.2 0.488 11,460 7.2 0.512 .55 to 59 years 20,880 6.7 10,099 6.6 0.484 10,781 6.8 0.516 .60 to 64 years 17,611 5.7 8224 5.4 0.467 9387 5.9 0.533 .65 to 69 years 14,437 4.6 6900 4.5 0.478 7537 4.7 0.522 .70 to 74 years 10,264 3.3 4704 3.1 0.458 5561 3.5 0.542 .75 to 79 years 7598 2.4 3233 2.1 0.426 4364 2.7 0.574 .80 to 84 years 5692 1.8 2490 1.6 0.437 3202 2.0 0.563 .85 years and over 5296 1.7 1971 1.3 0.372 3325 2.1 0.628 Details may not sum to totals because of rounding US Census Bureau, Current Population Survey, Annual Social and Economic Supplement, 2013. Internet release date: March 2016, https://www2.census.gov/programs-surveys/demo/tables/age-and-sex/2013/age-sex-composition/ Plus armed forces living off post or with their families on post Table 5 Calculation of Population QALY Weights Age Female Probability Male Probability Females Males Weighted (years) of dying between of dying between Population a b ages x and x +1 ages x and x +1 QALY (F_QALY + M_QALY) q q SF-6D Age_QALY Pop_WT F_QALY SF-6D Age_QALY Pop_WT M_QALY Pop_QALY x x 40–41 0.001299 0.002105 0.770 0.769 0.508 0.391 0.808 0.806 0.492 0.397 0.787 41–42 0.001403 0.002253 0.770 0.769 0.508 0.391 0.808 0.806 0.492 0.397 0.787 42–43 0.001523 0.002425 0.770 0.769 0.508 0.391 0.808 0.806 0.492 0.397 0.787 43–44 0.001663 0.002631 0.770 0.769 0.508 0.391 0.808 0.806 0.492 0.396 0.787 44–45 0.001827 0.002875 0.770 0.769 0.508 0.390 0.808 0.806 0.492 0.396 0.787 45–46 0.002004 0.003143 0.770 0.768 0.510 0.392 0.808 0.805 0.490 0.395 0.787 46–47 0.002197 0.003443 0.770 0.768 0.510 0.392 0.808 0.805 0.490 0.395 0.786 47–48 0.002421 0.003798 0.770 0.768 0.510 0.392 0.808 0.805 0.490 0.394 0.786 48–49 0.002674 0.004205 0.770 0.768 0.510 0.392 0.808 0.805 0.490 0.394 0.786 49–50 0.002941 0.004645 0.770 0.768 0.510 0.392 0.808 0.804 0.490 0.394 0.786 50–51 0.003212 0.005090 0.756 0.754 0.512 0.386 0.787 0.783 0.488 0.382 0.768 51–52 0.003484 0.005541 0.756 0.753 0.512 0.386 0.787 0.783 0.488 0.382 0.768 52–53 0.003760 0.006026 0.756 0.753 0.512 0.386 0.787 0.782 0.488 0.382 0.767 53–54 0.004046 0.006565 0.756 0.753 0.512 0.385 0.787 0.782 0.488 0.382 0.767 54–55 0.004351 0.007158 0.756 0.753 0.512 0.385 0.787 0.781 0.488 0.381 0.767 55–56 0.004680 0.007794 0.756 0.752 0.516 0.389 0.787 0.781 0.484 0.378 0.766 56–57 0.005028 0.008451 0.756 0.752 0.516 0.388 0.787 0.780 0.484 0.377 0.766 57–58 0.005391 0.009124 0.756 0.752 0.516 0.388 0.787 0.780 0.484 0.377 0.765 58–59 0.005766 0.009803 0.756 0.752 0.516 0.388 0.787 0.779 0.484 0.377 0.765 59–60 0.006166 0.010500 0.756 0.751 0.516 0.388 0.787 0.779 0.484 0.377 0.765 60–61 0.006598 0.011256 0.756 0.751 0.533 0.400 0.781 0.772 0.467 0.361 0.761 61–62 0.007083 0.012076 0.756 0.751 0.533 0.400 0.781 0.772 0.467 0.360 0.760 62–63 0.007638 0.012921 0.756 0.750 0.533 0.400 0.781 0.771 0.467 0.360 0.760 63–64 0.008279 0.013773 0.756 0.750 0.533 0.400 0.781 0.770 0.467 0.360 0.759 64–65 0.009003 0.014646 0.756 0.749 0.533 0.399 0.781 0.770 0.467 0.359 0.759 Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 13 of 17 Table 5 Calculation of Population QALY Weights (Continued) Age Female Probability Male Probability Females Males Weighted (years) of dying between of dying between Population a b ages x and x +1 ages x and x +1 QALY (F_QALY + M_QALY) q q SF-6D Age_QALY Pop_WT F_QALY SF-6D Age_QALY Pop_WT M_QALY Pop_QALY x x 65–66 0.009813 0.015569 0.756 0.749 0.522 0.391 0.781 0.769 0.478 0.367 0.758 66–67 0.010703 0.016603 0.756 0.748 0.522 0.390 0.781 0.768 0.478 0.367 0.758 67–68 0.011675 0.017800 0.756 0.747 0.522 0.390 0.781 0.767 0.478 0.367 0.757 68–69 0.012753 0.019228 0.756 0.746 0.522 0.390 0.781 0.766 0.478 0.366 0.756 69–70 0.013958 0.020906 0.756 0.745 0.522 0.389 0.781 0.765 0.478 0.365 0.755 70–71 0.015325 0.022826 0.738 0.727 0.542 0.394 0.757 0.740 0.458 0.339 0.733 71–72 0.016892 0.024998 0.738 0.726 0.542 0.393 0.757 0.738 0.458 0.338 0.731 72–73 0.018650 0.027356 0.738 0.724 0.542 0.392 0.757 0.736 0.458 0.337 0.730 73–74 0.020487 0.029913 0.738 0.723 0.542 0.392 0.757 0.734 0.458 0.337 0.728 74–75 0.022554 0.032679 0.738 0.721 0.542 0.391 0.757 0.732 0.458 0.336 0.726 75–76 0.024831 0.035524 0.738 0.720 0.574 0.413 0.757 0.730 0.426 0.311 0.724 76–77 0.027514 0.039010 0.738 0.718 0.574 0.412 0.757 0.727 0.426 0.310 0.722 77–78 0.030684 0.043116 0.738 0.715 0.574 0.411 0.757 0.724 0.426 0.308 0.719 78–79 0.034250 0.047647 0.738 0.713 0.574 0.409 0.757 0.721 0.426 0.307 0.716 79–80 0.038265 0.052626 0.738 0.710 0.574 0.408 0.757 0.717 0.426 0.305 0.713 80–81 0.042554 0.058301 0.698 0.668 0.563 0.376 0.725 0.683 0.437 0.299 0.675 81–82 0.047066 0.064637 0.698 0.665 0.563 0.374 0.725 0.678 0.437 0.297 0.671 82–83 0.052561 0.071412 0.698 0.661 0.563 0.372 0.725 0.673 0.437 0.295 0.667 83–84 0.058864 0.079031 0.698 0.657 0.563 0.370 0.725 0.668 0.437 0.292 0.662 84–85 0.066285 0.087905 0.698 0.652 0.563 0.367 0.725 0.661 0.437 0.289 0.656 85–86 0.074167 0.098958 0.698 0.646 0.628 0.406 0.725 0.653 0.372 0.243 0.649 86–87 0.083469 0.110149 0.698 0.640 0.628 0.402 0.725 0.645 0.372 0.240 0.642 87–88 0.093753 0.122333 0.698 0.633 0.628 0.397 0.725 0.636 0.372 0.237 0.634 88–89 0.105076 0.135536 0.698 0.625 0.628 0.392 0.725 0.627 0.372 0.233 0.625 89–90 0.117487 0.149773 0.698 0.616 0.628 0.387 0.725 0.616 0.372 0.229 0.616 a,b NCHS, National Vital Statistics System, Mortality www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_03.pdf Lost QALYs from Recurrent Disease ¼ ($216,343 * 0.005205479). The final calculations for these values are also in Table 2. Length of Hospital Stay ½ 1 −ðÞ EQ − 5D recurrent IV. Sensitivity Analysis Using Quality-Adjusted Life Days ð4Þ and the Value of Morbidity Risk Reductions From Mild/Moderate CDI Disease Based on Ages 65 to 90 The calculations for HCA-CDI and recurrent cases are as follows: Recognizing that the age distribution of patients HCA-CDI: 0.005205479 = (1–0.80)*(9.5/365) with HCA-CDI is older than for the general population, we Recurrent: 0.00704 = (1–0.708)*(8.8/365) recalculated the value of morbidity risk reductions based The results are used to adjust the on a population age range of 65 to 90. From Table 5,the re- willingness-to-pay for QALY to provide the monetary vised discounted years of life gained (Pop_QALY) to be estimate of the value of reducing the risk of a mild/ used in the calculations are now 13.1215 (3% discount rate) moderate case of HCA-CDI. For example, the and 9.1220 (7% discount rate). The revised values for mor- willingness-to-pay for a QALY for HCA-CDI using tality and morbidity risk reductions are in Table 6.The net the 2016 low VSL estimate (3% discount rate) of benefit calculations based on the age-adjusted VSL or value $216,343 results in a lost QALY estimate of $1126 per QALY estimates are presented in Table 7. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 14 of 17 Table 6 Model Inputs: Cases and Deaths Averted; Value Per QALY, Value per QALD 2015–2020 (2015 $) for Ages 65 to 90 Years Old 7% discount rate 3% discount rate 7% discount rate 3% discount rate, 10% effectiveness 10% effectiveness 50% effectiveness 50% effectiveness Cases Averted Inpatient Cases Averted HCA-CDI 167,699 184,749 838,493 923,743 Recurrent 35,892 39,555 179,460 197,775 Total 203,591 224,304 1,017,953 1,121,518 Deaths Averted 35% Attributable Mortality 5625 6199 28,123 30,996 50% Attributable Mortality 8035 8856 40,176 44,280 VSL Estimates (2015 $) 3% DR 2015 2016 2017 2018 2019 2020 Low $4,500,000 $4,368,932 $4,335,941 $4,301,166 $4,175,889 $4,140,522 Central $9,400,000 $9,320,388 $9,143,180 $8,968,388 $8,884,870 $8,712,349 High $14,400,000 $14,174,757 $13,950,419 $13,727,125 $13,505,003 $13,370,436 7% DR 2015 2016 2017 2018 2019 2020 Low $4,500,000 $4,236,288 $3,959,148 $3,782,363 $3,611,765 $3,375,481 Central $9,400,000 $9,037,415 $8,534,163 $8,140,304 $7,684,607 $7,253,694 High $14,400,000 $13,744,402 $13,021,197 $12,333,794 $11,680,602 $11,060,088 Value Per QALY (2015 $) 3% DR 2015 2016 2017 2018 2019 2020 Low $342,948 $332,959 $330,445 $327,795 $318,248 $315,552 Central $716,381 $710,313 $696,808 $683,487 $677,122 $663,974 High $1,097,434 $1,080,268 $1,063,171 $1,046,154 $1,029,226 $1,018,971 7% DR 2015 2016 2017 2018 2019 2020 Low $493,311 $461,039 $440,452 $420,586 $393,071 $375,173 Central $1,030,473 $983,549 $928,780 $876,967 $836,322 $789,426 High $1,578,597 $1,495,815 $1,417,108 $1,342,297 $1,271,210 $1,211,495 Value Per QALD (2015 $) 3% DR - Case 2015 2016 2017 2018 2019 2020 Low $1785 $1733 $1720 $1706 $1657 $1643 Central $3729 $3698 $3627 $3558 $3525 $3456 High $5713 $5623 $5534 $5446 $5358 $5304 3% DR - Recurrent 2015 2016 2017 2018 2019 2020 Low $2414 $2344 $2326 $2308 $2240 $2221 Central $5043 $5001 $4906 $4812 $4767 $4674 High $7726 $7605 $7485 $7365 $7246 $7174 7% DR - Cases 2015 2016 2017 2018 2019 2020 Low $2568 $2400 $2293 $2189 $2046 $1953 Central $5364 $5120 $4835 $4565 $4353 $4109 High $8217 $7786 $7377 $6987 $6617 $6306 7% DR - Recurrent 2015 2016 2017 2018 2019 2020 Low $3473 $3246 $3101 $2961 $2767 $2641 Central $7255 $6924 $6539 $6174 $5888 $5558 High $11,113 $10,531 $9976 $9450 $8949 $8529 VSL value of statistical life, QALY quality-adjusted life year, QALD quality-adjusted life day, DR discount rate To calculate the number of cases, we took national incidence rates from Lessa et al. (2015) and applied them to projections of the US population (by year of age) for 2015–2020 (United States Census Bureau 2016) [24, 28]. Rates of attributable CDI mortality were derived by the authors’ based on analysis by Kwon et al. (2015) [29]. The base estimates for the VSL were taken from the new HHS guidelines for conducting regulatory impact analysis (US Department of Health and Human Services 2017) [21]. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 15 of 17 Table 7 Benefits and Costs of a Comprehensive CDI Prevention Program 2015–2020 (2015 $) Using Age Adjusted VSL Estimates 7% discount rate, 10% 3% discount rate, 10% 7% discount rate, 50% 3% discount rate, 50% effectiveness effectiveness effectiveness effectiveness Total Intervention Costs (in $3.7 $4.7 $3.3 $4.2 billions) Total Benefits (in billions) Direct Medical Cost Savings Savings in Patient Care Costs $1.5 $1.8 $7.6 $9.1 Savings in Expenditures for $3.8 $4.2 $3.8 $4.2 Antibiotics Benefits of Morbidity Risk Reduction Low $/Lost QALY from HCA- $0.5 $0.4 $2.6 $2.0 CDI Central $/ Lost QALY from $1.0 $0.9 $5.1 $4.3 HCA-CDI High $/ Lost QALY from $1.6 $1.3 $7.8 $6.5 HCA-CDI Benefits of Mortality Risk Reduction Low VSL 35% Attributable Mortality $2.4 $2.0 $12.2 $10.1 Proportion 50% Attributable Mortality $3.5 $2.9 $17.3 $14.5 Proportion Central VSL 35% Attributable Mortality $5.1 $4.3 $25.5 $21.4 Proportion 50% Attributable Mortality $7.3 $6.1 $35.0 $30.6 Proportion High VSL 35% Attributable Mortality $7.8 $6.5 $39.0 $32.7 Proportion 50% Attributable Mortality $11.3 $9.3 $55.7 $46.7 Proportion Range of Total Net Benefits Low $4.5 - $5.6 $3.7 - $4.6 $22.9 - $28.0 $21.2 - $25.6 Central $7.7 - $9.9 $6.5 - $8.3 $38.7 - $48.2 $34.8 - $44.0 High $11.0 - $14.5 $9.1 - $11.9 $54.9 - $71.6 $48.3 - $62.3 HCA-CDI healthcare-associated Clostridioides difficile infection, VSL value of a statistical life, QALY quality-adjusted life year Abbreviations Availability of data and materials AS: Antibiotic stewardship; AU: Antibiotic utilization option of the The datasets used and/or analyzed during the current study come from antimicrobial use and resistance module of the national healthcare safety secondary sources which have been included in the list of references. network; CDI: Clostridioides difficile infection; CMS: Centers for medicare and medicaid services; CPI-U: Consumer price index for urban consumers; Disclaimer HAI: Healthcare-associated infection; HCA-CDI: Healthcare-associated The findings and conclusions in this report are those of the authors and do Clostridioides difficile infection; HHS: Department of Health and Human not necessarily represent the views of the Centers for Disease Control and Services; HRQL: Health-related quality of life; OMB: Office of management Prevention. and budget; QALD: Quality-adjusted life day; QALY: Quality-adjusted life year; US: United States; VSL: Value of statistical life Authors’ contributions RS, RS, JB, SC, and JJ were involved in the conception and design. RS, RS, and FL were involved in data collection. RS, RS, FL, JB, CM and were involved Acknowledgements in data interpretation. RS, JB, SC, CM and JJ were involved in writing the The authors would like to thank Ashley Rose for reviewing the manuscript. manuscript. All authors read and approved the final manuscript. Funding Ethics approval and consent to participate Not applicable. Not applicable. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 16 of 17 Consent for publication 18. Robinson LA. How U.S. government agencies value mortality risk reductions. Has been obtained by all authors. Rev Environ Econ Policy. 2007;1(2):283–99. 19. Hausman J. Contingent valuation: from dubious to hopeless. J Econ Perspect. 2012;26(4):43–56. Competing interests 20. Cropper M, Hammitt JK, Robinson LA. Valuing mortality risk reductions: The authors declare that they have no competing interests. progress and challenges. Annu Rev Resour Econ. 2011;3:313–36. 21. Viscusi WK. 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Publisher: Springer Journals
Copyright: Copyright © 2019 by The Author(s).
Subject: Biomedicine; Medical Microbiology; Drug Resistance; Infectious Diseases
eISSN: 2047-2994
DOI: 10.1186/s13756-018-0459-1
Publisher site: See Article on Publisher Site

Abstract

Backgound: Economic evaluations of interventions to prevent healthcare-associated infections in the United States rarely take the societal perspective and thus ignore the potential benefits of morbidity and mortality risk reductions. Using new Department of Health and Human Services guidelines for regulatory impact analysis, we developed a cost-benefit analyses of a national multifaceted, in-hospital Clostridioides difficile infection prevention program (including staffing an antibiotic stewardship program) that incorporated value of statistical life estimates to obtain economic values associated with morbidity and mortality risk reductions. Methods: We used a net present value model to assess costs and benefits associated with antibiotic stewardship programs. Model inputs included treatment costs, intervention costs, healthcare-associated Clostridioides difficile infection cases, attributable deaths, and the value of statistical life which was used to estimate the economic value of morbidity and mortality risk reductions. Results: From 2015 to 2020, total net benefits of the intervention to the healthcare system range from $300 million to $7.6 billion when values for morbidity and mortality risk reductions are ignored. Including these values, the net social benefits of the intervention range from $21 billion to $624 billion with the annualized net benefit of $25.5 billion under our most likely outcome scenario. Conclusions: Incorporating the economic value of morbidity and mortality risk reductions in economic evaluations of healthcare-associated infections will significantly increase the benefits resulting from prevention. Keywords: Healthcare-associated infections, Clostridioides difficile infection, Antibiotic stewardship, Regulatory impact analysis, Value of statistical life, Cost-benefit analysis * Correspondence: DScott1@cdc.gov Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Diseases, Centers for Disease Control and Prevention (CDC), Roybal Campus, 1600 Clifton Road MS H16-3, Atlanta, GA 30329-4027, USA Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 2 of 17 Introduction As required by executive order, US federal regulatory Healthcare-associated Infections (HAIs) pose a serious agencies conducting economic analyses of regulations health threat to hospitalized patients with an estimated 4% impacting human health must take the societal perspective, of hospitalized patients in the United States (US) infected which includes measuring the economic benefit of morta- at any given time [1]. To mitigate this threat, actions are lity risk reductions [16]. The US Office of Management and being taken by a myriad of public health organizations in- Budget (OMB) Circular A-4, Regulatory Analysis (2003) cluding government agencies, professional associations, also directs regulatory agencies on use of the value of statis- private industry, and consumer groups. These actions in- tical Life (VSL) which is a monetized measure of the clude mandatory public reporting of hospital HAI rates additional cost that individuals would be willing to pay for and the formation of prevention collaboratives composed a small reduction in the risk of mortality [17]. An example of multiple hospitals working together to prevent HAIs from Robinson (2007) illustrates the VSL concept [18]. As- [2]. The US Congress passed several acts to empower suming a population of 100,000, if each individual indicates agencies to implement polices to combat HAIs including they would be willing to pay an average of $50 to prevent the funding of states to develop HAI prevention plans, one death (a risk reduction of 1/100,000), the VSL would hospital reporting of select HAIs rates to the Centers for be $50 X 100,000 or $5 million. Medicare and Medicaid Services (CMS), and a penalty VSL estimates are derived from survey methods in system to reduce hospital Medicare reimbursements for which respondents are asked what they would be willing high rates of HAIs [3–5]. Additionally, the Executive to pay for small changes in the risk of premature death Branch has launched efforts to coordinate various volun- (referred to as state-preference studies) or from statistical tary activities of both public and private sector stake- models that evaluate wage differentials for occupations holders to achieve national goals of reducing HAIs and with varying job-related mortality risks (referred to as antibiotic resistant infections [6, 7]. revealed-preference studies). Concerns have been raised As policy actions to combat HAIs and antibiotic drug about the accuracy of VSL estimates from stated- resistance increase, economic evaluations of how to effi- preference studies, particularly related to the potential for ciently achieve national goals are needed to help inform hypothetical bias. As respondents are responding to a policy decisions. However, conducting economic evalua- hypothetical market for risk reductions described within a tions of policies affecting HAIs have been challenging questionnaire, respondents may provide “spurious” re- given (1) the quality of hospital cost data, (2) the diver- sponses that do not reflect how they would respond to an gent cost perspectives (i.e. of patients, providers, third actual market, and thus overstate (or understate) what party payers, or society) that the analysis can take, (3) they would actually pay for a reduction in risk [19–21]. the methodological difficulties of assessing both short However, there have been advances in both survey design term and long-term attributable morbidities and mortal- and the use of statistical methods to make adjustments to ity, and (4) difficulties in identifying patients with HAIs responses if needed to minimize potential bias [20, 22]. having their onset post-discharge [8–10]. Historically, Also, comparison of VSL estimates between revealed- most HAI economic analyses have taken the cost per- preference and stated-preference studies have found that spective of the healthcare provider and /or administrator estimates from revealed-preference studies tend to be [9, 11]. These studies attempt to demonstrate the cost higher, which may be a reflection of how the perception of savings to hospital budgets resulting from prevented risk may differ between those who actually face the risk cases ofHAIssoastomakea ‘business case’ that in- (as in wage studies), and those who may discount their vestments in infection control reduce treatment costs own perceived individual risk to a hazard described in a and improve outcomes [12–14]. Using peer-reviewed survey [21]. While further research is needed to under- attributable cost estimates for select HAIs, a 2012 stand the discrepancies between VSL estimates from the meta-analysis found that the range of total direct two types of studies, certain types of mortality risk cannot medical costs to the US healthcare system due to be assessed using wage data (i.e. cancer) and require the hospital-onset central-line associated bloodstream in- use of stated-preference methods [20, 21]. fections, catheter-associated urinary tract infections, Until recently, only the US Environmental Protec- ventilator-associated pneumonia, surgical site infections tion Agency (2014) and the US Department of and Clostridioides difficile infection (CDI) was $8.3–$11.5 Transportation (2015) had issued their own guide- billion (2012 dollars) [15]. While this work is still import- lines to ensure that their use of VSL in internal ant, relying on analyses based on the provider cost per- analyses complied with OMB directives [23, 24]. The spective ignores the cost impacts to patients (travel costs, US Department of Health and Human Services lost wages, long term morbidities, insurance co-pays), (HHS) has now published its own guidelines (2017) third party payers (increased per-patient reimbursements), to inform and advise department agencies on the and to society (mortality). methods used in conducting regulatory impact Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 3 of 17 analysis that are consistent with standing executive implementation of an AS program) to prevent CDI in orders and OMB recommendations [25]. Medicare patients in acute care hospitals over a 5 year time Prior to the HHS guideline publication, CMS had pro- horizon [27]. The simulation model incorporated informa- posed a new rule to require that all hospitals certified by tion on projected hospitals discharges, infection incidence Medicare and/or Medicaid (4900 hospitals and 1300 crit- rates, intervention effectiveness, prevention program costs, ical access hospitals) establish and maintain antibiotic and Medicare reimbursements saved per case averted, with stewardship (AS) programs by 2020 as called for in the each model representing a cohort of 1000 patients and out- National Action Plan for Combating Antibiotic-Resistant comes assessed for 1000 trials. Using the cost perspective Bacteria [7, 26]. While the rule also proposed other of the federal government (as a third party payer), the na- more modest requirements related to patients right to tional financial savings to the Medicare program (under the services (regardless of race, color or national origin), em- base case scenario of 50% program effectiveness and a 3% ployment of an infection preventionist/infection control discount rate) was estimated to be $2.5 billion (2011 professional, mandated review of current infection con- dollars) with a credible range of $1.2 billion to $4.0 billion trol programs, and other nursing and medical record over the five year study period. services, the requirement associated with the highest To expand this analysis from a Medicare cost perspec- costs and benefits in the corresponding regulatory im- tive to reflect a societal cost perspective, we developed pact analysis was for AS programs. Using results from national estimates of the benefits of averted cases and an economic evaluation of a multifaceted intervention reduced mortality risk for all ages using results from (including AS programs) for CDI in US hospitals based Lessa et al. which derived population-based estimates of on a Medicare cost perspective, the regulatory impact the incidence and disease burden for (1) health analysis concluded that requiring AS programs would care-associated Clostridioides difficile (HCA-CDI) result in annual savings of over 1 billion dollars to hospi- (which included community-onset health care–associ- tals from reduced incidence of CDIs and drug cost sav- ated, hospital-onset, and nursing home-onset infections) ings from reduce inappropriate antibiotic prescribing (2) recurrent HCA-CDI cases (within 14 to 56 days after [26, 27]. However, it was noted in the regulatory impact the initial occurrence) stemming from these infections, analysis that the potential societal benefits of reduced and (3) the number of deaths occurring within 30 days non-fatal CDI illness and the societal benefits and costs after the diagnosis of HCA-CDI [28]. The net present of reduced fatal CDI illness had been ignored due to lack value (NPV) model is defined as: of information on these benefits. A request for this 5 Benefits þ Costs information to be considered in the analysis of the fina- t NPV ¼ ð1Þ t¼0 lized rule was made. ðÞ 1 þ r Our objective is to provide estimates of both the morbid- ity and mortality risk reductions associated with an active where: hospital AS program and enhanced infection control by Benefits = the total benefits arising in year t (t = expanding the previously mentioned Medicare analysis to 0,1,2,3,4,5), incorporate a societal cost perspective. The VSL estimates, Costs = the total costs arising in year t (t = 0,1,2,3,4,5), the methods for assessing intervention costs, and the and. methods for deriving the value of morbidity risk reductions r = the social discount rate (3 and 7%). from the VSL are taken from the new HHS guidelines. The The analysis of the net present value model was con- possible ramifications of incorporating the societal perspec- ducted using Excel for Windows 2016. tive in evaluations of HAI prevention programs, and their The study horizon for the measurement of costs and interpretation by stakeholders and policy makers, will be benefits in our societal analysis is a 6 year period begin- discussed. ning in 2015 through 2020 to be consistent with the published CMS analysis [26]. Unlike the Slayton analysis, Methods we assumed a 3 month lag before benefits start to accrue This analysis relied on the results of two recent CDI studies in 2015 to account for the time needed to implement to develop a net present value model to assess the social the intervention. Also, in contrast to the regulatory im- costs and benefits of a multifaceted CDI prevention pro- pact analysis done by CMS, we calculated the cost of the gram including the economic value of reduced mortality intervention to cover expenses to all inpatient prospec- risks (see Appendix for model details) [27, 28]. Our tive payment system (IPPS) hospitals as opposed to just economic model is partly based on a decision analytic 60%. The CMS analysis took into account that 40% of (Markov) model developed by Slayton et al. to measure the IPPS hospitals had already established ongoing AS pro- net benefits of a multifaceted intervention (enhanced grams, but we would argue that without the proposed hospital infection control practices coupled with the requirement, hospitals would be free to suspend these Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 4 of 17 programs given changes in any clinical or financial con- infections, and HCA-CDI associated deaths were made ditions facing the hospital [29]. To avoid any potential using 2014 US Census Bureau projections for the US biases by limiting the coverage of the AS requirement, population for 2015 through 2020, in each of the four we fully assessed all cost and benefits that would accrue age strata: 1–17, 18–44, 45–64, and 65 and over (Table 2) to all hospitals subject to the rule. [32]. The estimates of the direct medical cost savings due to averted HCA-CDI and recurrent CDI cases were Cost of prevention made by multiplying the averted cases by estimates of The monetary unit costs of the CDI intervention program the attributable cost savings for hospital-onset and re- were taken from the Markov decision model and adjusted current cases found in Kwon et al. (2012) [33]. While to 2015 dollars using the Consumer Price Index for Urban using the cost savings of averted hospital-onset cases as Consumers (CPI-U) (see Table 1)[27, 30]. Total interven- a cost surrogate for all HCA-CDI cases, the Kwon ana- tion costs included the cost of implementing and staffing lysis showed that hospital-onset costs tend to be lower an AS program for HCA-CDI prevention (25% of total AS and thus serves as a conservative estimate of the cost labor costs), the cost of implementing the Antimicrobial savings from averted HCA-CDI cases. Use (AU) Option of the Antimicrobial Use and Resistance For valuing mortality risk reductions, the HHS guide- module of the National Healthcare Safety Network (the lines provide a range of VSL estimates to reflect the vari- required data platform from CMS for assessing antibiotic ability in these estimates from published studies and to prescribing), the federal government investment to hospi- promote the use of sensitivity analysis to assess the impact tals to support adoption of AS programs, the cost to hos- study on results. We used a low, a central and a high VSL pitals for patient isolation, hand hygiene, and enhanced estimate for the years 2015 to 2020, where we adjusted environmental cleaning. As investments in AS programs from 2014 VSL estimates to 2015 dollars using the CPI-U and the AU module are broad-based interventions that (Table 2) as recommended by the HHS guidelines. The can target other healthcare-associated organisms and guidelines also suggest that VSL estimates be adjusted to reduce unnecessary drug use, only 25% of these costs are reflect changes in real income growth in future years attributed to CDI prevention. To be consistent with the (2015–2020), which we adjusted by 1.3% according to pro- HHS guidelines, all labor costs that did not include jections by the Congressional Budget Office [34]. Using overhead were doubled. Costs were expressed as costs per the low, central and high VSL estimates to provide a range hospital discharge using 2015 discharges from National of benefits, each estimate was applied to every observed Inpatient Sample from the Healthcare Cost and death regardless of age in accordance with the guidelines. Utilization Project as a base (35,232,942 discharges) and The total economic value of the reductions in the risk of were combined with trend projections of the number of death due to the prevention program is estimated by hospital discharges for 2015 through 2020 (see appendix multiplying the discounted estimate of the number of for additional details) to estimate total prevention costs deaths averted in each year (2015–2020) by the corre- [31]. As recommended by the HHS guidelines, the ana- sponding discounted estimate of VSL and then summing lysis used discount rates of 3 and 7%. across the years. The difference in the per-discharge cost of isolation For this analysis, we estimated the value of morbidity and infection control ($3.56 versus $6.41) is the result of risk reductions by assuming that the incidence of all the different effectiveness levels and their impact on the cases (including recurrent cases) was mild or moderate. number of HCA-CDI cases that will need enhanced While cases of HCA-CDI can result in fulminate colitis infection control. At 50% program effectiveness, the (approximately 16%) and recurrent cases can experience number of cases that will need enhanced infection con- up to 14 recurrent episodes, the source for our burden trol is reduced by half, while at the 90% effectiveness estimates did not categorize cases according to their dis- level, the number of cases that will need enhanced infec- ease severity or track when cases experienced multiple tion control is only reduced by 10%. occurrences through time [28, 35–38]. Thus, our valu- ation of the morbidity risk reductions are conservative Benefits and should be considered a lower bound. As such, each Total benefits include the attributable patient treatment case will only experience a decrease in utility once cost savings from averted HCA-CDI cases, an estimate within each analytical year. of the reduce hospital expenditures (cost savings) on an- To value morbidity risk reductions, the HHS guidelines tibiotics due to AS oversite, and the value of morbidity recommends the use of willingness-to-pay estimates that and mortality risk reductions reflected in reduced cases measure the maximum amount individuals would give up and deaths due to the prevention of HCA-CDI. Using in income to pay for reducing the risk of illness. As no the age-stratified incidence rates from the Lessa study, such estimates exists for HCA-CDI, the guidelines recom- estimates of the number of HCA-CDI, recurrent mend the use of monetized quality-adjusted life years Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 5 of 17 Table 1 Net Economic Benefits Model for CDI Prevention: Model Inputs (2015 $) Incidence Rates [28] HCA-CDI rate per Recurrence Rate Per Death Rate Per 100,000 persons 100,000 persons 100,000 persons Age Group 1–17 6.3 0.4 NA 18–44 18.3 3.0 NA 45–64 83.1 10.9 5.4 ≥ 65 481.5 117.6 55.1 Effectiveness of the Multifaceted CDI Intervention [27] 10 and 50% % of Total Deaths Due to CDI [28, 33] 35 and 50% Cost Inputs [27] Cost Per Hospital Discharge Infection Control and Isolation Costs $3.56 (50 effectiveness), –6.41 (10% effectiveness) Implementation of the Antimicrobial Use (AU) module Initial Cost (in 2015) $0.08 Ongoing Costs $0.03 Antibiotic Stewardship Personnel (1.2 Pharmacists +0.67 Infectious Disease Physician + 0.05 $20.58 Network Data Analysis) × 0.25 Federal Government Investment Initial Cost (2009–2014) $0.18 Ongoing Costs (2015) $0.03 Cost of Enhanced Cleaning $0.28 Total Initial cost (2015) 50% program effectiveness $24.68 10% program effectiveness $27.52 On-going cost (2016–2020) 50% program effectiveness $24.47 10% program effectiveness $27.32 Benefits of Prevention Attributable Patient Cost Savings HCA-CDI [27] $ 6844 (Per Prevented Case) Recurrent CDI [27] $12,703 (Per Prevented Case) Length Of Hospital Stay (LOS) Mild/Moderate HCA-CDI Disease 9.5 days Recurrent Disease 8.8 days QALY/ adjusted to QALD by LOS Mild/Moderate HCA-CDI Disease 0.80 / 0.005205479 Recurrent Disease 0.708 / 0.00704 HCA-CDI healthcare-associated Clostridioides difficile infection, CDI Clostridioides difficile infection, QALY quality-adjusted life year, QALD quality adjusted life day, LOS length of hospital stay, AU the Antimicrobial Use Option of the Antimicrobial Use and Resistance (AUR) module of the National Healthcare Safety Network (NHSN) Only 25% of total stewardship program and federal government investment costs were attributed to CDI prevention activities as these prevention efforts also involve other multi-drug resistant organisms Length of stay for mild/moderate HCA-CDI comes from Gabriel and Beriot-Mathiot while length of stay for recurrent CDI comes from McFarland et al. [37, 44] From Sullivan et al. the QALY weight selected for mild/moderate HCA-CDI disease is the 25th percentile EQ-5D index score which reflects a population that is older, with more comorbidities, and with a lower socio-demographic profile of respondents in the Medical Expenditure Panel Survey (MEPS) survey [42]. The QALY weight for recurrent HCA-CDI disease corresponds to the 25th percentile EQ-5D score for those older MEPS respondents with “Other Gastrointestinal Disorders” (clinical classification category 155). These weights are adjusted by the LOS associated with HCA-CDI and recurrent CDI disease to reflect the short term, acute nature of mild/moderate CDI disease (See the appendix for the adjustment formula to convert QALY to QALD). Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 6 of 17 Table 2 Model Inputs: Cases and Deaths Averted; VSL, Value Per QALY, Value per QALD 2015–2020 (2015 $) 7% discount rate 3% discount rate 7% discount rate 3% discount rate, 10% effectiveness 10% effectiveness 50% effectiveness 50% effectiveness Cases Averted Inpatient Cases Averted HCA-CDI 167,699 184,749 838,493 923,743 Recurrent 35,892 39,555 179,460 197,775 Total 203,591 224.304 1,017,953 1,121,518 Deaths Averted 35% Attributable Mortality 5625 6199 28,123 30,996 50% Attributable Mortality 8035 8856 40,176 44,280 VSL Estimates (2015 $) 3% DR 2015 2016 2017 2018 2019 2020 Low $4,500,000 $4,368,932 $4,335,941 $4,301,166 $4,175,889 $4,140,522 Central $9,400,000 $9,320,388 $9,143,180 $8,968,388 $8,884,870 $8,712,349 High $14,400,000 $14,174,757 $13,950,419 $13,727,125 $13,505,003 $13,370,436 7% DR 2015 2016 2017 2018 2019 2020 Low $4,500,000 $4,236,288 $3,959,148 $3,782,363 $3,611,765 $3,375,481 Central $9,400,000 $9,037,415 $8,534,163 $8,140,304 $7,684,607 $7,253,694 High $14,400,000 $13,744,402 $13,021,197 $12,333,794 $11,680,602 $11,060,088 Value Per QALY (2015 $) 3% DR 2015 2016 2017 2018 2019 2020 Low $222,833 $216,343 $214,709 $212,987 $206,784 $205,032 Central $465,474 $461,532 $452,757 $444,101 $439,966 $431,423 High $713,067 $701,913 $690,804 $679,747 $668,748 $662,084 7% DR 2015 2016 2017 2018 2019 2020 Low $394,645 $377,023 $352,358 $336,466 $321,144 $300,135 Central $833,140 $786,831 $750,676 $708,725 $669,051 $637,787 High $1,271,634 $1,204,835 $1,141,334 $1,080,985 $1,023,648 $969,186 Value Per QALD (2015 $) 3% DR - Cases 2015 2016 2017 2018 2019 2020 Low $1160 $1126 $1118 $1109 $1076 $1067 Central $2423 $2402 $2357 $2312 $2290 $2246 High $3712 $3654 $3596 $3538 $3481 $3446 3% DR - Recurrent 2015 2016 2017 2018 2019 2020 Low $1569 $1523 $1512 $1499 $1456 $1443 Central $3277 $3249 $3187 $3126 $3097 $3037 High $5020 $4941 $4863 $4785 $4708 $4661 7% DR - Cases 2015 2016 2017 2018 2019 2020 Low $2054 $1963 $1834 $1751 $1672 $1562 Central $4337 $4096 $3908 $3689 $3483 $3320 High $6619 $6272 $5941 $5627 $5329 $5045 7% DR - Recurrent 2015 2016 2017 2018 2019 2020 Low $2778 $2654 $2481 $2369 $2261 $2113 Central $5865 $5539 $5285 $4989 $4710 $4490 High $8952 $8482 $8035 $7610 $7206 $6823 VSL value of statistical life, QALY quality-adjusted life year, QALD quality-adjusted life day, DR discount rate. To calculate the number of cases, we took national incidence rates from Lessa et al. (2015) and applied them to projections of the US population (by year of age) for 2015–2020 (United States Census Bureau 2014)) [24, 28]. Rates of attributable CDI mortality were derived by the authors’ based on analysis by Kwon et al. [33]. The base estimates for the VSL were taken from the new HHS guidelines for conducting regulatory impact analysis (HHS 2017) [25]. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 7 of 17 (QALYs) as a surrogate measure. To construct this value for mild/moderate HCA-CDI disease ranged from $1067 for morbidity risk reductions in the future, four pieces of to $8952. (Table 2). information are needed: (1) the remaining years of life ex- Another potential benefit from AS programs is the re- pectancy, (2) a health-related quality of life (HRQL) asso- duction in hospital expenditures for antibiotics as these ciated with each expected year of life that declines in programs reduce antibiotic prescribing through more quality of life due to age, (3) the probability of survival in appropriate use [45, 46]. While the Slayton study did not each expected year of life, and (4) a monetary value per consider these benefits, the CMS analysis derived an an- expected (monetarized) QALY (derived from the VSL). nual estimate of $520 million (in 2003 dollars) in these Items 1–3 are used to calculate expected QALYs associ- savings based on information from a single study of a ated with age by first multiplying the HRQL in each ex- 124 bed hospital [47]. To obtain a more robust estimate pected year of life by the probability of living in that year of the expected reduced antibiotic expenditures by hos- (i.e., by the survival curve) [39, 40]. The discounted sum pitals, we used a 2009 estimate of total antibiotic expen- of these expected QALYs are calculated over the ditures by US acute care and long-term care hospitals remaining years of life expectancy using the same discount ($3.6 billion); adjusted it to 2015 dollars ($3.9 billion) rates of 3 and 7%. For our study population, we assumed using the CPI-U; and then multiplied this estimate by a an average age of 40 with a life expectancy of 50 years. To representative percentage savings in annual antibiotic get the monetarized QALY, the VSL estimates were then expenditures taken from published studies [26]. In divided by the expected QALYs [41]. Our estimates for reviewing studies of US hospitals, the cost reductions the monetary value per expected QALY for 2015–2020, ranged from 10 to 37% [47–54]. For our model, we used based on the low, central and high VSL estimates, ranged a reduction in antibiotic costs of 20% which resulted in from $223,000 to $1.27 billion (Table 2). an annual estimated savings of $787 million in antibiotic To derive an estimate of the monetary value of redu- expenditures. After adjusting for the 3-month lag in cing the risk of getting a mild/moderate disease, HRQL 2015, the total discounted value of these savings for weights associated with HCA-CDI and recurrent CDI 2015–2020 was $4.2 billion (3% discount rate) and $3.8 disease are needed to adjust the monetary value per billion (7% discount rate). QALY. As there are no published HRQL weights specif- ically for HCA-CDI disease, we selected surrogate com- Sensitivity analysis munity preference-based EQ-5D index weights from The model is evaluated using two intervention effective- Sullivan et al. (2006) [42]. For mild/moderate HCA-CDI ness scenarios of 50 and 10% (the same base and lower cases, we selected the unadjusted 25th percentile EQ-5D bound model values as used in Slayton) while assuming score (taken from the Medical Expenditure Panel Survey full program implementation costs for each level [27]. 2000–2002) of 0.80 which can be interpreted as reflect- While the projected number of HCA-CDI associated ing a patient population that is older with more comor- cases and deaths were adjusted by 50 and 10% to reflect bidities, which have been found to be risk factors for program effectiveness, associated deaths were further HCA- CDI disease [43]. For mild/moderate recurrent adjusted by 50 and 35% to provide a credible range of cases, we selected the unadjusted 25th percentile EQ-5D the attributable proportion of HCA-CDI deaths due to score for other gastrointestinal disorders (Chronic Clas- CDI disease that reflects the limitations of current sification Condition 155) of 0.708, which reflects an methods used to attribute these outcomes [28, 33, 55]. older patient profile with these diseases, to act as a sur- Consistent with HHS guidance, our sensitivity analysis rogate for patients with recurrent CDI disease. The also includes calculations using a low ($4.5 million), cen- EQ-5D weights are adjusted to quality-adjusted life days tral ($9.4 million) and high VSL estimate ($14.4 million) (QALD) and then multiplied by the associated hospital for 2015 (Table 2). As derived from the low, central and lengths of stay for acute episodes of HCA-CDI and re- high estimates of the monetary value for a QALY, the current cases (9.5 and 8.8 days respectively from Table 1) monetary value of QALD used in value reduction of to obtain an adjusted QALD lost from avoided morbidity risks ranged from $1160 to $8952 to reflect HCA-CDI and recurrent infections (0.0052 and 0.007 re- the short duration of mild/moderate CDI disease. As spectively from Table 1 and Appendix)[37, 44]. These recommended by the HHS guidelines, the analysis used weights are then used to adjust the monetary value per discount rates of 3 and 7%. QALY to get a monetary value per lost QALD from As VSL estimates in the HHS guidelines are derived mild/moderate HCA-CDI and recurrent disease. To ob- for a population between the ages of 18 to 65, the guide- tain the total value of morbidity risk reductions, the lines also recommend that when the affected population number of HCA-CDI and recurrent cases are then is very old, additional sensitivity analysis should be done multiplied by their respective monetary value per lost using monetized QALY values. These values are then QALD. Our estimates for the monetary value of QALD multiplied by the expected value of the number of life Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 8 of 17 years gained (which will be smaller for older popula- intervention costs and the benefits of both the direct tions). As 62% of cases and 84% of deaths in the Lessa medical cost savings and the value of morbidity risk re- study occurred in patients 65 and over, we developed ductions as the total net benefits are substantial. The monetized QALY values using an age range of 65 to 90 proportion of the economic value of mortality risk re- years (as opposed to ages 40 to 90 years used to obtain the ductions to total benefits ranged from 80% (lowest total monetized QALYs for the morbidity risk values described benefit estimate) to 97% (highest total benefit estimate). above) to be used in place of both the VSL estimates and Even with VSL estimates that have been age adjusted the previous estimates for the value of morbidity risk re- to reflect the older age distribution of HCA-CDI pa- ductions. A description of how these alternative estimates tients, the intervention still produced a range of total were derived can be found in the Appendix.From Table 6, net benefits of $3.7 billion to $14.5 billion under our the monetarized QALYs for mortality risk reductions lowest effectiveness scenario (10% program effectiveness, ranged from $342,948 to $1,578,597, while the monetized 35% attributable mortality proportion) (Table 7). As this QALYs for morbidity risk reduction ranged from $1643 to same intervention program achieved an 80% reduction $11,113. in hospital CDI cases in England from 2008 to 2012, the credible range of net benefits is $21.2 billion to $625.8 Results billion which are associated with the 50% program ef- In our high estimate scenario based on 50% program ef- fectiveness scenarios (Tables 3 and 7)[56–58]. Given the fectiveness and attributable mortality, the national inter- evidence on program effectiveness and the theoretical vention was projected to avert 1,017,953 total inpatient and empirical uncertainties associated with age-adjusted cases and prevent 40,176 deaths using a 7% discount VSL estimates, we suggest that a likely, but conservative, rate, and 1,121,518 total inpatient cases and 44,280 scenario outcome from a public policy perspective is deaths using a 3% discount rate over the study period $121.4 billion in total net benefits (50% program effect- (Table 2). Using the low estimates of program effective- iveness, 3% discount rate, 35% attributable mortality ness and attributable mortality (10 and 35% respect- proportion, and the low VSL estimate). This translates ively), the model projects that 203,591 total inpatient to an annualized net benefit of approximately $25.5 cases and 5625 deaths were averted at the 7% discount billion. rate, and 224,304 total inpatient cases and 6199 deaths were averted at the 3% discount rate. Discussion Also from Table 2, the projected cost of the prevention The societal cost perspective has rarely been considered program ranged from $3.3 billion (7% discount rate) to in economic evaluations of HAI prevention programs, $4.2 (3% discount rate) at 50% program effectiveness. At but doing so in accordance with HHS guidelines for 10% program effectiveness, the program costs ranged conducting regulatory impact analysis may provide from $3.7 billion (7% discount rate) to $4.7 billion (3% stakeholders and policy makers a broader view on the discount rate). The higher costs under the 10% program benefits of such programs. As our intervention costs effectiveness scenario arise from the increased costs ranged from $3.3 billion to $4.2 billion, these costs associated with implementing the enhanced infection would have to be at least 28 times larger to overlap with control practices (to avoid transmission) around the our lowest benefit estimate of $121.4 under the 50% pro- remaining 90% of cases (as opposed to 50% of cases). gram effectiveness scenario (Table 3) and would have to Without considering the value of morbidity and mor- quintuple to overlap with our lowest benefit estimate of tality risk reductions, the net benefits in reduced patient $21.2 when using age-adjusted VSL estimates and the care costs and reduced antibiotic expenditures from the 50% program effectiveness scenario (Table 7). intervention ranged from $8.1 billion to $9.1 billion While the benefits of reducing mortality risks comprised (subtracting direct medical cost savings of $13.3 billion a vast majority of total net social benefits in our model, a and $11.4 billion from the intervention costs of $4.2 and number of other relevant benefits (cost savings) associated $3.3 billion respectively) under the 50% intervention ef- with CDI disease are ignored in this analysis. The most fectiveness scenarios, but these net benefits decreased in important of these include non-hospital medical costs the 10% effectiveness scenarios to $1.3 billion (3% dis- (e.g., outpatient treatment and pharmacy costs), medical count rate) to $1.6 billion (7% discount rate) (Table 3). costs and the value of morbidity risks due to severe or However, when the values for morbidity and mortality long-term morbidities, lost labor productivity, and the risk reductions were included, net benefits from the economic impacts on family/caregivers. More broadly, AS intervention ranged from $24 billion to $626 billion programs also produce spillover benefits in terms of redu- across all scenarios (Table 3). The inclusion of the eco- cing rates of antibiotic resistance, although such economic nomic value of mortality risk reductions using VSL esti- impacts are diffuse and difficult to quantify and require mates overwhelmed the difference between the more research to understand their full effects. However, Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 9 of 17 Table 3 Benefits and Costs of a Comprehensive CDI Prevention Program 2015–2020 (2015 $) 7% discount rate, 3% discount rate, 7% discount rate, 3% discount rate, 10% effectiveness 10% effectiveness 50% effectiveness 50% effectiveness Total Intervention Costs (in billions) $3.7 $4.7 $3.3 $4.2 Total Benefits (in billions) Direct Medical Cost Savings Savings in Patient Care Costs $1.5 $1.8 $7.6 $9.1 Savings in Hospital Expenditures $3.8 $4.2 $3.8 $4.2 for Antibiotics Benefits of Morbidity Risk Reduction Low $/Lost QALY from HCA-CDI $0.4 $0.3 $2.1 $1.3 Central $/ Lost QALY from HCA-CDI $0.8 $0.6 $4.1 $2.8 High $/ Lost QALY from HCA-CDI $1.3 $0.8 $6.3 $4.2 Benefits of Mortality Risk Reduction Low VSL 35% Attributable Mortality Proportion $22.1 $26.6 $111.2 $133.2 50% Attributable Mortality Proportion $31.5 $38.0 $157.7 $190.2 Central VSL 35% Attributable Mortality Proportion $46.6 $56.2 $232.8 $280.8 50% Attributable Mortality Proportion $66.5 $80.2 $332.6 $401.1 High VSL 35% Attributable Mortality Proportion $71.1 $85.7 $355.7 $428.7 50% Attributable Mortality Proportion $102.7 $122.5 $507.8 $612.5 Range of Total Net Benefits Low $24.1 - $33.5 $28.2 - $ 39.6 $121.4 - $167.9 $143.6 - $200.6 Central $49.0 - $68.9 $58.1 - $ 82.1 $245.0 - $344.8 $292.7 - $413.0 High $74.0 - $105.6 $87.8 - $124.6 $370.1 - $522.2 $442.0 - $625.8 HCA-CDI healthcare-associated Clostridioides difficile infection, VSL value of a statistical life, QALY quality-adjusted life year. the addition of these potential benefits just provides more their infection control program and (2) conduct a review support for the proposed rule. As the range of credible of their infection control program [26]. Other changes costs and benefits did not overlap, we did not perform a affecting the writing of restraint and seclusion orders for Monte Carlo simulation in our sensitivity analysis. violent/self-destructive patients by a licensed practi- Regardless of the type of interventions taken for HAI tioner, the granting of dietary ordering privileges to prevention, the inclusion of monetary valuations for qualified dieticians or nutritionist professionals (in crit- morbidity and mortality risk reductions in cost-benefit ical access hospitals), and the implementation of quality analyses of HAI disease prevention potentially provide assessment and improvement programs were also pro- economic justification for interventions that might not posed. While lacking data on the potential cost savings otherwise be considered cost saving, as illustrated by this associated with many of these changes and ignoring the analysis. Our model shows that the total net benefits economic value of morbidity and mortality risk reduc- from having AS programs and enhanced infection con- tions from averted infections, the accompanying regula- trol are significantly large enough to cover the total in- tory impact analysis concluded that the overall proposal vestment costs in AS programs (as opposed to just 25% resulted in a net benefit to society of $284 million with in our model) as the total annualized intervention cost the majority of costs coming from changes affecting in- (totaling $14.6 billion for 2015–2020 at a 3% discount fection control programs and the addition of AS pro- rate) is only $2.7 billion. grams. Regardless of the differences in the calculated Along with the requirement to include AS programs, cost of the proposed CMS rule and the cost of our CDI CMS also proposed additional changes to the conditions prevention program, the additional economic benefits of of participation including a requirement that hospitals, morbidity risk reductions alone can be readily applied to (1) identify a qualified infection preventionist or infec- the CMS analysis and would significantly increase the tion control professional as an officer responsible for magnitude of net benefits. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 10 of 17 A potential limitation to VSL studies of HAI prevention estimates, especially for mortality risk reductions related is the variability in methodology and quality of studies that to air quality improvements, have been developed for generate attributable mortality estimates given the range many locations, including Europe, Asia and Australia of published estimates currently available [33]. The dom- [67–69]. While the National Health Service in the inance of VSL estimates in the calculation of benefits United Kingdom relies on cost-effectiveness analysis highlights the need for attributable mortality measures in their decision-making for assessing adoption of new that accurately and consistently reflect the mortality im- healthcare interventions, HM Treasury has had guide- pacts of HAIs. Additional research is needed to improve lines (referred to as “The Green Book”)for govern- the measurement of attributable mortality associated not ment ministries on how to incorporate the “Value of a only with HAI but with any cause of disease-related death. Prevented Fatality” (another name for the VSL) in pol- Given the decades of research on the VSL, the meas- icy assessments and evaluations of government actions urement of the VSL and the estimates currently used that involve risks to life and health [70]. To better are generally accepted for injury-related risk reduction understand the characteristics of VSL estimates from while the evidence suggests that VSL estimates for different countries, researchers at the Organization for illness-related risks are probably similar [59]. How- Economic Co-operation and Development (OECD) ever, the quality of data on mortality associated with have conducted a meta-analysis of VSL estimates re- disease should also be scrutinized as the new HHS lated to environmental, health and transport polices guidelines do not directly address this issue. Use of (from stated-preference studies) that have been done unadjusted-crude mortality estimates, as opposed to around the globe [69, 71]. OECD has also published a age-adjusted or risk-adjusted estimates can produce VSL user’s guide to better inform policy makers on significantly different estimates of the benefits of mor- how to incorporate VSL in policy decisions [72]. The tality risk reductions in cases of HAI [55, 60]. median and mean VSL values (using the full dataset) In the case of CDI disease, an important factor that of $2.4 and $7.4 million (in 2005 US dollars), along likely increased attributable mortality from 2000 through with other evidence from this study, can potentially be 2010 was the emergence and spread of the epidemic, adopted for use in a global economic assessments of hyper-virulent, North American Pulse-field type 1 antimicrobial resistance [69]. (NAP1) or ribotype 027 strain [61, 62]. Although de- clines of the NAP1/027 strain in the United States have not been as dramatic as that seen in places like England, Conclusion where declines have occurred, it is likely that declines in Although progress has been made, HAIs still pose a attributable mortality have followed [58, 63]. Ironically, serious threat to patients across healthcare settings. A however, it may be AS, alone or in combination with in- recent study on the prevalence of HAIs in US acute care fection control, that has led to most dramatic declines in hospitals estimated that the total number of HAIs NAP1/027 [64]. In addition, the development of new occurring annually was 722,000, of which there were therapies and recommendations for generally more ag- 75,000 HAI-associated deaths [1, 73]. Along with CDI, gressive treatment of HAIs, like CDI, may also result in carbapenem-resistant Enterobacteriaceae (CRE) has been declines in attributable mortality but also add their own classified as an urgent threat to the public health in the costs that must be considered [64, 65]. However, in the National Action Plan for Combating Antibiotic-resistant case of AS there may be additional impacts on morbidity Bacteria (White House 2015) [7]. The Plan has set tar- and mortality that are yet to be fully understood as gets for a reduction in incidence of both CRE and CDI, growing evidence suggests that the effect of unnecessary calling for 60% reduction in hospital-acquired CRE and antibiotics on the microbiome may result in other ad- 50% in overall CDI infections by 2020. verse outcomes among hospitalized patients such as sep- Our study accounted for the economic value of sis [66]. Such impacts, even if only partially realized, morbidity and mortality risk reductions, components could even more dramatically sway the cost benefits in of the total societal health benefits that have not pre- favor of aggressive stewardship interventions. viously been included in cost-benefit analyses of HAI While our analysis illustrates the potential impact on prevention programs. As the US federal government benefits measurement when the value of mortality risks intensifies its efforts to control antibiotic resistant in- are considered with regulations that impact health, this fections, our results suggest that these ambitious analysis also illustrates the role that cost-benefit analysis, goals can produce very large net societal benefits. As as opposed to cost-effectiveness analysis, has in rule these benefits accrue mostly to patients, policy making that involves public health and safety by the US makers can address how the burden for the additional Federal Government. The cost-benefit analysis described prevention costs should be shared among patients, here can easily be adopted by other countries as VSL third party payers and healthcare providers. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 11 of 17 Appendix their proportion to total population (see Table 4). The data used to calculate the probability of survival were taken I. Hospital discharges used to calculate intervention from 2013 life tables for males and females in the US costs. (actually expressed as the probability of dying between ages x to x + 1 in Table 5)[36]. Table 5 also shows the data The number of hospital discharges used to help calcu- used to calculate Pop_QALY where the SF-6D is multi- late intervention costs were from the National Inpatient plied by (1) probability of surviving to the next year Sample (NIS).[27] We fitted a linear trend line over the (Age_QALY which is calculated by the formula 1 - number of yearly discharges for 2003 To 2014 which was probability of dying between ages x to x + 1), and (2) the then used to extrapolate annual discharges for 2015 to population weight for male and females respectively 2020. The equation for the estimated fitted trend line was: (Pop_Wt which is the population weighted QALY for females and males (F_QALY and M_QALY). The final y ¼ −188; 575ðÞ discharges QALY for the population (Pop_QALY) is the addition of þ 40; 000; 000 where i F_QALY and M_QALY. Once applied to formula 2,the ¼ðÞ 2003; 2004;:…; 2014 ; ð1Þ willingness-to-pay per QALY were calculated. For 2 example, the 2016 VSL low estimate of $4,368,932 (3% R (coefficient of determination) = (0.51) discount rate) is divided by the discounted sum of The extrapolated number of discharges used to calculate Pop_QALY. This calculation ($4,368,932/ 20.1945) results intervention costs per discharge for 2015–2020 was in a willingness-to-pay for a QALY of $216,343. At the 7% 35,170,243; 34,981,668; 34,793,093; 34,604,518; 34,415,943; discount rate, the discounted sum of Pop_QALY is and 34,227,368 respectively. To calculate the per discharge 11.4027. These results are presented in Table 2. initial year costs (2015) from federal investments to pro- mote AS (which took place between 2009 and 2014), the III. Deriving the Quality-Adjusted Life Days and the total costs of these investments over these years was di- Value of Morbidity Risk Reductions for Mild/Mod- vided by the predicted number of discharges for 2015 erate CDI Disease (35,232,942) based on the trend analysis. As we lack the incidence data to predict the cases of II. Deriving the Monetarized Value or Willingness-to- severe HCA-CDI, we have assumed that, at a minimum, Pay per Quality-Adjusted Life Year (QALY). all cases are at least mild or moderate. To estimate the value of morbidity risk reductions for mild/moderate We followed HHS guidelines to calculate the HCA-CDI disease, we selected published community willingness-to-pay (WTP) or dollars for an expected preference-based QALY weights (EQ-5D scores) to rep- QALY. The WTP for a QALY formula (where r is the resent the decline in quality of life due to a case of discount rate) from Hirth et al. is: HCA-CDI and also recurrent disease [38]. Lacking a QALY weight specifically for HCA-CDI disease, the sur- Pop QALY X tþ50 Value of Stisitical LifeðÞ VSL ¼ ; rogate measures used included (1) 0.80 - the unadjusted ðÞ 1 þ r t¼0 EQ-5D score for the 25% percentile for the general sam- ð2Þ ple from the Medical Panel Expenditure Study (MEPS) where we have assumed the average population age is 40 to represent the baseline quality of life for patients that and expected life expectancy (probability of survival) is cal- could get an HCA-CDI infection, and (2) 0.704 – the culated for the next 50 years [37]. To derive the final qual- unadjusted EQ-5D for the 25th percentile for MEPS ity weights (Pop_QALY ) to be used in the above respondent reported having other gastrointestinal disor- t+ 50 formula, each additional year of life must be adjusted ders (Chronic Disease Classication 155). As the length of (multiplied) by (1) a QALY weight to reflect the decline in hospital stay for either a case or recurrence of CDI is on quality of life with age, (2) the conditional probability of average 9.5 days and 8.8 days respectively, these QALYs survival into the next year, and (3) a population weight must be adjusted down due to the acute nature of that is a weighted average of the QALY weights for males HCA-CDI and recurrent CDI disease. Eqs. 3 and 4 were and females based on proportion of the population. The used to make these adjustments QALY weights associated with male (M_QALY) and fe- males (F_QALY) age 40 to 90 are the SF-6D scores taken Lost QALYs from HCA−CDI Disease ¼ from Hanmer and Kaplan [35]. As our study did not have Length of Hospital Stay gender-stratified incidence rates for HCA-CDI, we con- ½ 1 −ðÞ EQ − 5D case structed a population weighted QALY weight (Pop_QALY) ð3Þ that combined the QALYs for males and females based on Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 12 of 17 Table 4 Population by Age and Sex: 2013 (Numbers in thousands, civilian noninstitutionalized population ) Age Both sexes Male Female Number Cell Percent Number Cell Percent Row Percent Number Cell Percent Row Percent All ages 311,116 100.0 152,335 100.0 0.490 158,781 100.0 0.510 .40 to 44 years 20,657 6.6 10,162 6.7 0.492 10,495 6.6 0.508 .45 to 49 years 21,060 6.8 10,319 6.8 0.490 10,742 6.8 0.510 .50 to 54 years 22,386 7.2 10,926 7.2 0.488 11,460 7.2 0.512 .55 to 59 years 20,880 6.7 10,099 6.6 0.484 10,781 6.8 0.516 .60 to 64 years 17,611 5.7 8224 5.4 0.467 9387 5.9 0.533 .65 to 69 years 14,437 4.6 6900 4.5 0.478 7537 4.7 0.522 .70 to 74 years 10,264 3.3 4704 3.1 0.458 5561 3.5 0.542 .75 to 79 years 7598 2.4 3233 2.1 0.426 4364 2.7 0.574 .80 to 84 years 5692 1.8 2490 1.6 0.437 3202 2.0 0.563 .85 years and over 5296 1.7 1971 1.3 0.372 3325 2.1 0.628 Details may not sum to totals because of rounding US Census Bureau, Current Population Survey, Annual Social and Economic Supplement, 2013. Internet release date: March 2016, https://www2.census.gov/programs-surveys/demo/tables/age-and-sex/2013/age-sex-composition/ Plus armed forces living off post or with their families on post Table 5 Calculation of Population QALY Weights Age Female Probability Male Probability Females Males Weighted (years) of dying between of dying between Population a b ages x and x +1 ages x and x +1 QALY (F_QALY + M_QALY) q q SF-6D Age_QALY Pop_WT F_QALY SF-6D Age_QALY Pop_WT M_QALY Pop_QALY x x 40–41 0.001299 0.002105 0.770 0.769 0.508 0.391 0.808 0.806 0.492 0.397 0.787 41–42 0.001403 0.002253 0.770 0.769 0.508 0.391 0.808 0.806 0.492 0.397 0.787 42–43 0.001523 0.002425 0.770 0.769 0.508 0.391 0.808 0.806 0.492 0.397 0.787 43–44 0.001663 0.002631 0.770 0.769 0.508 0.391 0.808 0.806 0.492 0.396 0.787 44–45 0.001827 0.002875 0.770 0.769 0.508 0.390 0.808 0.806 0.492 0.396 0.787 45–46 0.002004 0.003143 0.770 0.768 0.510 0.392 0.808 0.805 0.490 0.395 0.787 46–47 0.002197 0.003443 0.770 0.768 0.510 0.392 0.808 0.805 0.490 0.395 0.786 47–48 0.002421 0.003798 0.770 0.768 0.510 0.392 0.808 0.805 0.490 0.394 0.786 48–49 0.002674 0.004205 0.770 0.768 0.510 0.392 0.808 0.805 0.490 0.394 0.786 49–50 0.002941 0.004645 0.770 0.768 0.510 0.392 0.808 0.804 0.490 0.394 0.786 50–51 0.003212 0.005090 0.756 0.754 0.512 0.386 0.787 0.783 0.488 0.382 0.768 51–52 0.003484 0.005541 0.756 0.753 0.512 0.386 0.787 0.783 0.488 0.382 0.768 52–53 0.003760 0.006026 0.756 0.753 0.512 0.386 0.787 0.782 0.488 0.382 0.767 53–54 0.004046 0.006565 0.756 0.753 0.512 0.385 0.787 0.782 0.488 0.382 0.767 54–55 0.004351 0.007158 0.756 0.753 0.512 0.385 0.787 0.781 0.488 0.381 0.767 55–56 0.004680 0.007794 0.756 0.752 0.516 0.389 0.787 0.781 0.484 0.378 0.766 56–57 0.005028 0.008451 0.756 0.752 0.516 0.388 0.787 0.780 0.484 0.377 0.766 57–58 0.005391 0.009124 0.756 0.752 0.516 0.388 0.787 0.780 0.484 0.377 0.765 58–59 0.005766 0.009803 0.756 0.752 0.516 0.388 0.787 0.779 0.484 0.377 0.765 59–60 0.006166 0.010500 0.756 0.751 0.516 0.388 0.787 0.779 0.484 0.377 0.765 60–61 0.006598 0.011256 0.756 0.751 0.533 0.400 0.781 0.772 0.467 0.361 0.761 61–62 0.007083 0.012076 0.756 0.751 0.533 0.400 0.781 0.772 0.467 0.360 0.760 62–63 0.007638 0.012921 0.756 0.750 0.533 0.400 0.781 0.771 0.467 0.360 0.760 63–64 0.008279 0.013773 0.756 0.750 0.533 0.400 0.781 0.770 0.467 0.360 0.759 64–65 0.009003 0.014646 0.756 0.749 0.533 0.399 0.781 0.770 0.467 0.359 0.759 Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 13 of 17 Table 5 Calculation of Population QALY Weights (Continued) Age Female Probability Male Probability Females Males Weighted (years) of dying between of dying between Population a b ages x and x +1 ages x and x +1 QALY (F_QALY + M_QALY) q q SF-6D Age_QALY Pop_WT F_QALY SF-6D Age_QALY Pop_WT M_QALY Pop_QALY x x 65–66 0.009813 0.015569 0.756 0.749 0.522 0.391 0.781 0.769 0.478 0.367 0.758 66–67 0.010703 0.016603 0.756 0.748 0.522 0.390 0.781 0.768 0.478 0.367 0.758 67–68 0.011675 0.017800 0.756 0.747 0.522 0.390 0.781 0.767 0.478 0.367 0.757 68–69 0.012753 0.019228 0.756 0.746 0.522 0.390 0.781 0.766 0.478 0.366 0.756 69–70 0.013958 0.020906 0.756 0.745 0.522 0.389 0.781 0.765 0.478 0.365 0.755 70–71 0.015325 0.022826 0.738 0.727 0.542 0.394 0.757 0.740 0.458 0.339 0.733 71–72 0.016892 0.024998 0.738 0.726 0.542 0.393 0.757 0.738 0.458 0.338 0.731 72–73 0.018650 0.027356 0.738 0.724 0.542 0.392 0.757 0.736 0.458 0.337 0.730 73–74 0.020487 0.029913 0.738 0.723 0.542 0.392 0.757 0.734 0.458 0.337 0.728 74–75 0.022554 0.032679 0.738 0.721 0.542 0.391 0.757 0.732 0.458 0.336 0.726 75–76 0.024831 0.035524 0.738 0.720 0.574 0.413 0.757 0.730 0.426 0.311 0.724 76–77 0.027514 0.039010 0.738 0.718 0.574 0.412 0.757 0.727 0.426 0.310 0.722 77–78 0.030684 0.043116 0.738 0.715 0.574 0.411 0.757 0.724 0.426 0.308 0.719 78–79 0.034250 0.047647 0.738 0.713 0.574 0.409 0.757 0.721 0.426 0.307 0.716 79–80 0.038265 0.052626 0.738 0.710 0.574 0.408 0.757 0.717 0.426 0.305 0.713 80–81 0.042554 0.058301 0.698 0.668 0.563 0.376 0.725 0.683 0.437 0.299 0.675 81–82 0.047066 0.064637 0.698 0.665 0.563 0.374 0.725 0.678 0.437 0.297 0.671 82–83 0.052561 0.071412 0.698 0.661 0.563 0.372 0.725 0.673 0.437 0.295 0.667 83–84 0.058864 0.079031 0.698 0.657 0.563 0.370 0.725 0.668 0.437 0.292 0.662 84–85 0.066285 0.087905 0.698 0.652 0.563 0.367 0.725 0.661 0.437 0.289 0.656 85–86 0.074167 0.098958 0.698 0.646 0.628 0.406 0.725 0.653 0.372 0.243 0.649 86–87 0.083469 0.110149 0.698 0.640 0.628 0.402 0.725 0.645 0.372 0.240 0.642 87–88 0.093753 0.122333 0.698 0.633 0.628 0.397 0.725 0.636 0.372 0.237 0.634 88–89 0.105076 0.135536 0.698 0.625 0.628 0.392 0.725 0.627 0.372 0.233 0.625 89–90 0.117487 0.149773 0.698 0.616 0.628 0.387 0.725 0.616 0.372 0.229 0.616 a,b NCHS, National Vital Statistics System, Mortality www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_03.pdf Lost QALYs from Recurrent Disease ¼ ($216,343 * 0.005205479). The final calculations for these values are also in Table 2. Length of Hospital Stay ½ 1 −ðÞ EQ − 5D recurrent IV. Sensitivity Analysis Using Quality-Adjusted Life Days ð4Þ and the Value of Morbidity Risk Reductions From Mild/Moderate CDI Disease Based on Ages 65 to 90 The calculations for HCA-CDI and recurrent cases are as follows: Recognizing that the age distribution of patients HCA-CDI: 0.005205479 = (1–0.80)*(9.5/365) with HCA-CDI is older than for the general population, we Recurrent: 0.00704 = (1–0.708)*(8.8/365) recalculated the value of morbidity risk reductions based The results are used to adjust the on a population age range of 65 to 90. From Table 5,the re- willingness-to-pay for QALY to provide the monetary vised discounted years of life gained (Pop_QALY) to be estimate of the value of reducing the risk of a mild/ used in the calculations are now 13.1215 (3% discount rate) moderate case of HCA-CDI. For example, the and 9.1220 (7% discount rate). The revised values for mor- willingness-to-pay for a QALY for HCA-CDI using tality and morbidity risk reductions are in Table 6.The net the 2016 low VSL estimate (3% discount rate) of benefit calculations based on the age-adjusted VSL or value $216,343 results in a lost QALY estimate of $1126 per QALY estimates are presented in Table 7. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 14 of 17 Table 6 Model Inputs: Cases and Deaths Averted; Value Per QALY, Value per QALD 2015–2020 (2015 $) for Ages 65 to 90 Years Old 7% discount rate 3% discount rate 7% discount rate 3% discount rate, 10% effectiveness 10% effectiveness 50% effectiveness 50% effectiveness Cases Averted Inpatient Cases Averted HCA-CDI 167,699 184,749 838,493 923,743 Recurrent 35,892 39,555 179,460 197,775 Total 203,591 224,304 1,017,953 1,121,518 Deaths Averted 35% Attributable Mortality 5625 6199 28,123 30,996 50% Attributable Mortality 8035 8856 40,176 44,280 VSL Estimates (2015 $) 3% DR 2015 2016 2017 2018 2019 2020 Low $4,500,000 $4,368,932 $4,335,941 $4,301,166 $4,175,889 $4,140,522 Central $9,400,000 $9,320,388 $9,143,180 $8,968,388 $8,884,870 $8,712,349 High $14,400,000 $14,174,757 $13,950,419 $13,727,125 $13,505,003 $13,370,436 7% DR 2015 2016 2017 2018 2019 2020 Low $4,500,000 $4,236,288 $3,959,148 $3,782,363 $3,611,765 $3,375,481 Central $9,400,000 $9,037,415 $8,534,163 $8,140,304 $7,684,607 $7,253,694 High $14,400,000 $13,744,402 $13,021,197 $12,333,794 $11,680,602 $11,060,088 Value Per QALY (2015 $) 3% DR 2015 2016 2017 2018 2019 2020 Low $342,948 $332,959 $330,445 $327,795 $318,248 $315,552 Central $716,381 $710,313 $696,808 $683,487 $677,122 $663,974 High $1,097,434 $1,080,268 $1,063,171 $1,046,154 $1,029,226 $1,018,971 7% DR 2015 2016 2017 2018 2019 2020 Low $493,311 $461,039 $440,452 $420,586 $393,071 $375,173 Central $1,030,473 $983,549 $928,780 $876,967 $836,322 $789,426 High $1,578,597 $1,495,815 $1,417,108 $1,342,297 $1,271,210 $1,211,495 Value Per QALD (2015 $) 3% DR - Case 2015 2016 2017 2018 2019 2020 Low $1785 $1733 $1720 $1706 $1657 $1643 Central $3729 $3698 $3627 $3558 $3525 $3456 High $5713 $5623 $5534 $5446 $5358 $5304 3% DR - Recurrent 2015 2016 2017 2018 2019 2020 Low $2414 $2344 $2326 $2308 $2240 $2221 Central $5043 $5001 $4906 $4812 $4767 $4674 High $7726 $7605 $7485 $7365 $7246 $7174 7% DR - Cases 2015 2016 2017 2018 2019 2020 Low $2568 $2400 $2293 $2189 $2046 $1953 Central $5364 $5120 $4835 $4565 $4353 $4109 High $8217 $7786 $7377 $6987 $6617 $6306 7% DR - Recurrent 2015 2016 2017 2018 2019 2020 Low $3473 $3246 $3101 $2961 $2767 $2641 Central $7255 $6924 $6539 $6174 $5888 $5558 High $11,113 $10,531 $9976 $9450 $8949 $8529 VSL value of statistical life, QALY quality-adjusted life year, QALD quality-adjusted life day, DR discount rate To calculate the number of cases, we took national incidence rates from Lessa et al. (2015) and applied them to projections of the US population (by year of age) for 2015–2020 (United States Census Bureau 2016) [24, 28]. Rates of attributable CDI mortality were derived by the authors’ based on analysis by Kwon et al. (2015) [29]. The base estimates for the VSL were taken from the new HHS guidelines for conducting regulatory impact analysis (US Department of Health and Human Services 2017) [21]. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 15 of 17 Table 7 Benefits and Costs of a Comprehensive CDI Prevention Program 2015–2020 (2015 $) Using Age Adjusted VSL Estimates 7% discount rate, 10% 3% discount rate, 10% 7% discount rate, 50% 3% discount rate, 50% effectiveness effectiveness effectiveness effectiveness Total Intervention Costs (in $3.7 $4.7 $3.3 $4.2 billions) Total Benefits (in billions) Direct Medical Cost Savings Savings in Patient Care Costs $1.5 $1.8 $7.6 $9.1 Savings in Expenditures for $3.8 $4.2 $3.8 $4.2 Antibiotics Benefits of Morbidity Risk Reduction Low $/Lost QALY from HCA- $0.5 $0.4 $2.6 $2.0 CDI Central $/ Lost QALY from $1.0 $0.9 $5.1 $4.3 HCA-CDI High $/ Lost QALY from $1.6 $1.3 $7.8 $6.5 HCA-CDI Benefits of Mortality Risk Reduction Low VSL 35% Attributable Mortality $2.4 $2.0 $12.2 $10.1 Proportion 50% Attributable Mortality $3.5 $2.9 $17.3 $14.5 Proportion Central VSL 35% Attributable Mortality $5.1 $4.3 $25.5 $21.4 Proportion 50% Attributable Mortality $7.3 $6.1 $35.0 $30.6 Proportion High VSL 35% Attributable Mortality $7.8 $6.5 $39.0 $32.7 Proportion 50% Attributable Mortality $11.3 $9.3 $55.7 $46.7 Proportion Range of Total Net Benefits Low $4.5 - $5.6 $3.7 - $4.6 $22.9 - $28.0 $21.2 - $25.6 Central $7.7 - $9.9 $6.5 - $8.3 $38.7 - $48.2 $34.8 - $44.0 High $11.0 - $14.5 $9.1 - $11.9 $54.9 - $71.6 $48.3 - $62.3 HCA-CDI healthcare-associated Clostridioides difficile infection, VSL value of a statistical life, QALY quality-adjusted life year Abbreviations Availability of data and materials AS: Antibiotic stewardship; AU: Antibiotic utilization option of the The datasets used and/or analyzed during the current study come from antimicrobial use and resistance module of the national healthcare safety secondary sources which have been included in the list of references. network; CDI: Clostridioides difficile infection; CMS: Centers for medicare and medicaid services; CPI-U: Consumer price index for urban consumers; Disclaimer HAI: Healthcare-associated infection; HCA-CDI: Healthcare-associated The findings and conclusions in this report are those of the authors and do Clostridioides difficile infection; HHS: Department of Health and Human not necessarily represent the views of the Centers for Disease Control and Services; HRQL: Health-related quality of life; OMB: Office of management Prevention. and budget; QALD: Quality-adjusted life day; QALY: Quality-adjusted life year; US: United States; VSL: Value of statistical life Authors’ contributions RS, RS, JB, SC, and JJ were involved in the conception and design. RS, RS, and FL were involved in data collection. RS, RS, FL, JB, CM and were involved Acknowledgements in data interpretation. RS, JB, SC, CM and JJ were involved in writing the The authors would like to thank Ashley Rose for reviewing the manuscript. manuscript. All authors read and approved the final manuscript. Funding Ethics approval and consent to participate Not applicable. Not applicable. Scott et al. Antimicrobial Resistance and Infection Control (2019) 8:17 Page 16 of 17 Consent for publication 18. Robinson LA. How U.S. government agencies value mortality risk reductions. Has been obtained by all authors. Rev Environ Econ Policy. 2007;1(2):283–99. 19. Hausman J. Contingent valuation: from dubious to hopeless. J Econ Perspect. 2012;26(4):43–56. Competing interests 20. Cropper M, Hammitt JK, Robinson LA. Valuing mortality risk reductions: The authors declare that they have no competing interests. progress and challenges. Annu Rev Resour Econ. 2011;3:313–36. 21. Viscusi WK. 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Assessing the social cost and benefits of a national requirement establishing antibiotic stewardship programs to prevent Clostridioides difficile infection in US hospitals

Assessing the social cost and benefits of a national requirement establishing antibiotic stewardship programs to prevent Clostridioides difficile infection in US hospitals

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Assessing the social cost and benefits of a national requirement establishing antibiotic stewardship programs to prevent Clostridioides difficile infection in US hospitals

Assessing the social cost and benefits of a national requirement establishing antibiotic stewardship programs to prevent Clostridioides difficile infection in US hospitals

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