Stay healthy at the hospital

Protect yourself to ensure a speedy recovery and avoid infections and readmission.

Whether you go in for surgery, testing, or an outpatient procedure, your hospital stay can pose further health risks if you are not careful.

“Your potential risks depend in part on why you have to go into the hospital and the facility itself, but there are steps you can take to minimize your risk, especially when it comes to developing hospital-acquired infections that can lead to a longer hospital stay or readmission,” says Dr. Erica Shenoy, an infectious diseases specialist and associate chief of infection control at Harvard-affiliated Massachusetts General Hospital.

Here are some steps to take to ensure a safe hospital visit before, during, and after your stay.


Ask questions.

It can be nerve-racking to ask questions, no matter how small they feel, but you need to muster up the courage and make the most of your interactions with medical staff and during consultation, says Dr. Shenoy. “Just like you, they want you to have a quick and uncomplicated recovery and are open to your inquiries — but you have to ask.”

What should you ask?
ere are some questions that can help you manage your own expectations and plan ahead for recovery:

How long will I be in the hospital?

What is the expected recovery time?

Am I likely to need rehab or at-home support? Do I have a choice between the two?

“If at all possible, bring your list of questions and a family member or friend with you during any question–and-answer session,” says Dr. Shenoy. “This will help you feel more confident, and your companion can take notes.”

Get screened for possible infections. Depending on your procedure, you could be at high risk of postoperative infections. For people undergoing knee or hip replacement, common bacteria they may have on their skin can increase the risk.

“About 30% of people carry the bacteria Staphylococcus aureus — or staph — on their skin, without it causing any problems or actual infection,” says Dr. Shenoy. “But this bacterium is implicated in many postoperative infections, which is why your doctor may ask you to get screened for staph colonization, which often involves using a cotton swab on the inside of your nose.”

If you do have staph on your skin, the doctor may prescribe several days of a special bath soap and nose ointment, which together have been shown to decrease — but not eliminate — the risk of developing this type of infection.

Review your medications.

Talk with your doctor about your medications— prescription and over-the-counter — to determine what you should stop taking before your procedure or whether you should change any dosages. “Some drugs, such as blood thinners, may require modifications,” says Dr. Shenoy. Your doctor may provide you with a pre-op checklist so you know what to take and what not to take.

Know the risks.

You may not be aware of all the potential risks. “Even the simplest of procedures has some risks, so it’s important to know what they are even if the odds are quite low,” says Dr. Shenoy. “Knowing the risks can help you make a more informed decision about whether or not to proceed, and also what signs of complications to look for during the recovery period.”


Practice good hygiene. Doorknobs, handrails, countertops — anything you can touch has the potential to harbor bacteria. Always wash your hands with water and soap before eating and after using the bathroom. Alcohol-based sanitizers are useful outside of those specific circumstances.

All doctors and nurses should wash their hands or use alcohol-base hand sanitizer before they examine you. If not, ask about it. “Many will perform hand hygiene in your presence, but don’t be afraid to ask if they’ve done so before they interact with you,” says Dr. Shenoy.

If your provider expects to encounter blood or body fluids when examining you, he or she may add other protective gear such as gloves and a gown. A clinician may also wear protective equipment if you have a history of harboring particular bacteria.

Know your contacts. Before you leave, get a list of contact information for anyone you need to call regarding your recovery. You’ll also need the dates, times, and locations of all follow-up appointments.

Look for warning signs.

When you return home, watch for red flags for when you should seek immediate care — for example, changes in pain, redness or swelling, or fever. “That’s where the list of contacts come in handy,” says Dr. Shenoy. “Reach out to your physicians if you experience symptoms that cause you concern. They can help determine the best next steps.”

Published: June, 2017

Antibiotic resistance to 'kill 90,000' in Britain over the next 30 years

More than 90,000 people will die due to antibiotic resistance in the UK over the next 30 years, estimates suggest.

The Organisation for Economic Co-operation and Development (OECD) warned that superbug infections will kill around 2.4 million people across Europe, North America and Australia by 2050 unless more is done to limit drug-resistant super bugs.

This includes around 1.3 million deaths across Europe.

The report estimates that 90,045 Britons will die over the next 30 years from infections which are resistant to treatment.

Simple measures such as hand washing and more prudent prescriptions of antibiotics could avert some of the deaths, the authors said.

Better hygiene, ending the “over-prescription” of antibiotics and enhancing rapid testing for patients to ensure they are being prescribed the right drugs are some of the measures that could overcome the threat, the OECD said.

Three out of four deaths could be averted by spending just two US dollars (£1.50) per person a year, the OECD calculated.

A short-term investment would save money in the long run, they added, saying that dealing with antimicrobial resistance complications could cost up to 3.5 billion US dollars (£2.6 billion) each year on average across the 33 countries included in the analysis.

Resistance is already high and projected to grow even more rapidly in low and middle-income countries.

The report warns that southern Europe risks being particularly affected, with Italy, Greece and Portugal forecast to top the list of OECD countries with the highest mortality rates from antimicrobial resistance.

It adds that resistance to second and third-line antibiotics – used as back-ups to treat infections when common antibiotics do not work – is expected to grow over the coming decades.

The report comes after health officials in England launched a campaign to try to prevent people from asking for the drugs when they do not need them.

Public Health England said antibiotics are essential for treating serious bacterial infections but the drugs are frequently used to treat coughs, sore throats and ear aches, which usually get better without the medication.

The health body’s latest campaign reminds people that if they are feeling unwell, “antibiotics aren’t always needed”.

Tim Jinks, head of the Wellcome Trust’s Drug-Resistant Infections Priority Programme, said: “This new OECD report offers important insight into how simple, cost-effective surveillance, prevention and control methods could save lives globally.

“Drug-resistant superbugs are on the rise worldwide and represent a fundamental threat to global health and development. This report provides yet further evidence that investing to tackle the problem now will save lives and deliver big pay-offs in the future.”I am text block. Click edit button to change this text. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

Estimated hospital costs associated with preventable health care-associated infections if health care antiseptic products were unavailable

Objectives: Health care-associated infections (HAIs) pose a significant health care and cost burden. This study estimates annual HAI hospital costs in the US avoided through use of health care antiseptics (health care personnel hand washes and rubs; surgical hand scrubs and rubs; patient preoperative and preinjection skin preparations).

Methods: A spreadsheet model was developed with base case inputs derived from the published literature, supplemented with assumptions when data were insufficient. Five HAIs of interest were identified: catheter-associated urinary tract infections, central line-associated bloodstream infections, gastrointestinal infections caused by Clostridium difficile, hospital- or ventilator-associated pneumonia, and surgical site infections. A national estimate of the annual potential lost benefits from elimination of these products is calculated based on the number of HAIs, the proportion of HAIs that are preventable, the proportion of preventable HAIs associated with health care antiseptics, and HAI hospital costs. The model is designed to be user friendly and to allow assumptions about prevention across all infections to vary or stay the same. Sensitivity analyses provide low- and high-end estimates of costs avoided.

Results: Low- and high-end estimates of national, annual HAIs in hospitals avoided through use of health care antiseptics are 12,100 and 223,000, respectively, with associated hospital costs avoided of US$142 million and US$4.25 billion, respectively.

Conclusion: The model presents a novel approach to estimating the economic impact of health care antiseptic use for HAI avoidance, with the ability to vary model parameters to reflect spe-cific scenarios. While not all HAIs are avoidable, removing or limiting access to an effective preventive tool would have a substantial impact on patient well-being and infection costs. HAI avoidance through use of health care antiseptics has a demonstrable and substantial impact on health care expenditures; the costs here are exclusive of administrative penalties or long-term outcomes for patients and caregivers such as lost productivity or indirect costs.

Keywords: anti-infective agents, topical, costs and cost analysis, hospital infections, antiseptic agents


Health care-associated infections (HAIs), which the Centers for Disease Control and Prevention (CDC) estimates occur in one of every 25 acute care hospitalizations,1 are of paramount interest in the US. HAIs in hospitals tracked by the CDC1 include central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections (SSIs), hospital-acquired pneumonia (HAP), including ventilator-associated pneumonia (VAP), and gastrointestinal infec-tions caused by Clostridium difficile. HAIs are an important metric for evaluating quality in health care institutions such that they are tracked by the Centers for Medicare and Medicaid Services (CMS) in its Hospital Compare program.2 High scores (poor per-formance) can lead to penalties, such as those associated with the Hospital-Acquired Condition Reduction Program established by the 2010 Patient Protection and Affordable Care Act; specifically, patients with certain infection types cannot have the diagnosis-related group for their hospital admission changed to a more complex code to obtain a greater reimbursement from CMS to cover the increased hospital costs associated with the infection. Consumer-focused hospital ratings may also consider HAI rates in their evaluations. With an increase in the prevalence of resistant organisms and incentives to discharge patients quickly while minimizing readmission rates, concerns about HAIs will likely continue to increase.
Despite an obvious public health mandate to minimize the occurrence and impact of HAIs, identifying the most cost-effective or even effective strategies to do so is a source of uncertainty. A number of strategies have been proposed, ranging from environmental controls and modifications, to changing physical contact (eg, avoiding handshaking), to educating patients and health care providers on hand hygiene techniques, to using biosensors to identify areas in need of disinfection. Invariably, hand hygiene is a part of any effort to control HAIs. Hand hygiene programs typically include multiple components, including the more obvious ones, like well-placed cleansers and sinks, and structural elements, such as compliance assessments and feedback mechanisms.3
Recently, in the US, there has been discussion about the merits of various over-the-counter antiseptics,4 including those used in health care settings, such as health care person-nel hand washes and rubs, surgical hand scrubs and rubs, and patient preoperative and preinjection skin preparations.
Introducing new interventions to decrease HAIs has inher-ent costs. For example, replacing surfaces with nonconductive copper has been shown to be effective5 but likely requires a substantial initial capital investment. Costs may be distrib-uted by replacing surfaces one floor or ward at a time, yet there is likely to be both cost and interruption to care. Other interventions, like adding reminders about hand washing and stronger messaging, may be less costly to implement but may require a steady stream of funding to maintain.
The research question underlying this paper is regarding the cost of not maintaining the status quo: what is the cost associated with removing an existing effective component of programs to avoid HAIs – the use of health care anti-septic products? The objective of this project is to estimate the incremental hospital costs associated with preventable illnesses that would no longer be prevented if certain health care antiseptics were to be eliminated. A total national esti-mate of the potential lost benefits from elimination of these products is based on a national number of cases of HAIs, assumptions about the proportion of all HAIs that are overall preventable, assumptions about the proportion of preventable HAIs that are associated with health care antiseptics, and the hospital costs for these illnesses (specific to each infection) obtained from the published literature. The end product of this effort is a spreadsheet model that incorporates various input parameters and can be used to test and explore potential outcomes of limiting health care antiseptic products. The model accounts for the sources of uncertainty in several ways – it provides a range of input values rather than a single base case and also allows the user to input alternative values should the available selections be inadequate.

The model is designed as a simple spreadsheet tool without a single set of default values; instead, a range of plausible input parameters based on the published literature is provided, from which a user can select preferred input values. Four basic types of information are required to populate the model: first, the number of cases of each type of HAI of interest; second, the proportion of all HAIs that are preventable; third, the proportion of preventable HAIs attributable to the use of health care antiseptics; and finally, the average hospital cost associated with each HAI. Essentially, the calculation starts with an estimate of the number of HAIs in the US in 2011 (the most recently published data), reduces that number to account for the proportion of infections that are considered unpreventable overall and those that are preventable through use of health care antiseptics, and then assigns corresponding hospital costs to each of the remaining HAI cases. The result-ing total infection count and cost equals the annual national estimate of potentially lost benefits that would be expected to occur if health care antiseptic products were eliminated.
Literature searches focusing on clinical efficacy and hospital costs were conducted to identify published values for model input parameters using the National Library of Medicine’s PubMed database. After PubMed searches, targeted searches of authors whose works are prominent in the field and government or quasi-government bodies that engage in documenting or improving the performance of health care systems (eg, Centers for Disease Control and Prevention, World Health Organization, and Agency for Healthcare Research and Quality) were also conducted.
Reviews and meta-analyses were examined for evidence of original data relevant to this analysis. For both the clinical and economic searches, reference lists of identified papers were also reviewed for relevant literature.
For the clinical efficacy component of the search, designed to identify papers that could provide information on the number of HAIs, preventability of HAIs, and the proportion of prevention attributable to the use of health care antiseptics, initial search terms (Medical Subject Headings [MeSH], keywords, and text fields) including “handwash”, “healthcare”, “hospital”, and “rate” were used to identify papers published in the previous 25 years in English with human subjects. Studies on the number of HAIs were limited to the US, but for identifying estimates of preventability and proportion attributable to health care antiseptic use, no country or region limitations were used, as it was determined that these should not be excluded a priori but rather reviewed on a case-by-case basis.
For the economic component of the search, search terms (MeSH, keywords, and text fields) included “healthcare”, “hospital”, “infection”, “costs and cost analysis”, and related subheadings suggested by PubMed; filters were applied to identify papers published in the previous 10 years in English with human subjects. A shorter time frame was selected than that for the clinical efficacy search to minimize variation in treatments and associated costs that could occur over a longer time frame. Papers on costs were limited to those providing estimates for the US. Studies were considered for this analysis if they presented hospital costs per case, rather than per household or total expenditures associated with an outbreak, and if they reported on a broad mix of patients. Costs were inflated to October 2015 US$ using the Consumer Price Index for medical care published by the Bureau of Labor Statistics (series ID CUUR0000SAM).
Abstraction of the cost estimates was a multistep process. Most papers provided a high and low estimate, rather than a single point estimate or average. To be consistent with the model’s approach of providing a range of estimates, an average of all the low estimates for each HAI and an aver-age of all the high estimates for each HAI were estimated.

In this manner, the estimates in the model not only inherently reflect uncertainty in the literature but also benefit from some aggregation of the estimates available.


Specification of input parameters

Number of HAIs
Three recent studies provide estimates of the number of HAIs annually observed in the US.1,6,7 The estimates from these papers are provided in Table 1. These studies estimate the number of cases of various infections but do not attempt to link infections to specific causal organisms. The model similarly makes the simplifying assumption that the distribu-tion of pathogens within and across HAIs is not relevant to the number of HAIs. This is necessary given the lack of data on the distribution of pathogens on a national level and the lack of detail on other input parameters (eg, prevention and costs) by pathogen. It is not unreasonable to think that there could be differences in the preventability of HAIs based on changes in the distribution of the causal organisms, if health care antiseptic products are more effective against some pathogens than others, and the costs of treating the same HAI caused by different pathogens could vary. However, none of these data are available and therefore the model does not allow for specification of pathogens.

Proportion of HAIs that are preventable
There are various estimates in the literature for the propor-tion of HAIs that are preventable;8–10 best practices, including hand hygiene and many other interventions, do not eliminate HAIs entirely. Cases that are not preventable are eliminated from this analysis at this stage of the calculations, as the use of health care antiseptics could not have an effect on these already-existing infections. For example, Umscheid et al10 estimate that only 65%–70% of CLABSIs and CAUTIs and 55% of cases of VAP and SSIs are preventable. In their comprehensive review of the impact of various interventions, Harbarth et al also found wide variation in the proportion of preventable infections across settings and patient types, but they suggest that 20% is a reasonable proportion of HAIs that are preventable.9 Based on the wide range of values in the literature, the model includes multiple options for the propor-tion of HAIs that are preventable (20%, 35%, 50%, and 70%). A prespecified common value can be applied to all infection types or prespecified individual values can be applied to each type of infection. Alternatively, the model can be customized by providing a common user-specified value to be applied to all infections or by providing individual user-specified values to be applied to each type of infection.

Number of prevented cases attributable to health care antiseptics
Multiple studies were considered in developing reasonable model inputs for attributable cases.11–16 The range of values provided in these studies was used in the model, rather than a point estimate (eg, the average of all values provided in the studies), for the reduction of cases associated with health care antiseptic use. As with other model inputs, the simplifying assumption that use of health care antiseptics would prevent cases of all types of HAIs equally, regardless of pathogen, was made. At this time, there are insufficient data to assign different patterns of prevention by pathogen. The model allows the user to choose between providing individual values for each HAI type in addition to the common value for all HAI types, and selecting from prespecified values. These prespecified values, 10%, 20%, and 30%, were not based on specific studies but are intended to reflect a conservative range of estimates in the literature.

Costs for each HAI
A number of reviews and summary papers were found during the clinical portion of the literature search that helped guide the search for primary data sources. For example, Scott7 pub-lished a national estimate of HAI counts that also estimated hospital costs in the US. To account for variation of cost estimates and methods in the reviewed literature, a range of costs for each type of HAI (inflated to October 2015 US$) was used in the model. Table 2 shows these ranges and the studies from which they were obtained. Several studies identified in the search were excluded, because they aggregated infections rather than presenting the infections of interest separately, or included a very specific population (eg, only pediatric or only elderly) or a small set of surgi-cal interventions or settings, or did not include the year in which costs were presented. After inflating cost values to October 2015 US$, estimates were aggregated by infection by taking the average of available low and high estimates for each infection type.

Based on the findings, the potential incremental hospital cost burden of hospital-acquired infections avoided by the use of health care antiseptics is between US$142 million and US$4.25 billion annually in the US. These results are presented in Table 5.
The results presented here provide a low and high estimate of the potential increase in cases and medical expenditures associated with elimination of health care antiseptic use. It is expected that actual potential increases would fall somewhere between these low and high estimates.
Given the uncertainty around many of the estimates in this model and our decision to use low and high estimates for model inputs rather than single values, traditional sensitivity analyses are not appropriate. Instead, we used values from Zimlichman et al’s 2013 meta-analysis17 as a comparison for hospital costs (number of cases prevented was not compared). The estimated avoided costs based on Zimlichman et al’s meta-analysis range from US$308 million to US$3.33 billion, which fall within the range of our model results. As with the low and high values discussed previously, the low end of this range is estimated using the number of current annual cases from Magill et al1 and the high end using the estimate from Scott.7

The purpose of this model is to help guide decision-making in the face of uncertainty. The model is a representation of the complex real-world relationships among changing rates of infections, hospital costs, and the potential impact of health care antiseptics. In the face of uncertainty about the continued availability of health care antiseptics, the model reflects the current state of knowledge while providing the opportunity to explore a variety of scenarios. The low and high scenarios presented in this paper can be used to understand the potential economic impact of a change in availability of health care antiseptics on human health and hospital costs in the US. Though the findings estimated here cover a broad range due to the breadth of existing data used in the model, they are indicative of the potential impact of changes in availability of health care antiseptics at the national level. The range of

estimates can be narrowed as new data become available for the model. In addition, the uncertainty in the model may be substantially minimized when applied to local, institution- or system-specific situations, since input data are generally better understood at the local level. Ideally, the model could be applied to explore the local impact of antiseptic avail-ability to aid in decision-making, in addition to projecting values nationally.
There are multiple sources of uncertainty associated with the estimates used as model input parameters. We address how the model incorporates and manages this uncertainty across each of the four main model inputs in turn. First, there are challenges in identifying the total number of HAIs nationally. The studies selected for use in this model are based on reported infections as part of surveillance programs rather than administrative claims data to identify events. A recent review and meta-analysis of the accuracy of using administrative data to identify HAIs18 found inconsistencies across types of infections in terms of sensitivity and specific-ity. This, as well as earlier work that suggests “traditional” surveillance reporting is superior to other approaches for identifying HAIs,19 supports our avoidance of administrative claims data in the base case estimates but points to difficul-ties in quantifying the number of HAIs. The same surveil-lance reports suggest that rates of HAIs are higher among patient populations who are younger, older, or otherwise compromised,20 which both validates our decision not to include data from these studies and suggests that once these more severe patients are included, their higher hospital costs might mean our general population approach underestimates infections and costs. Additionally, the process of attribution on the part of the hospital is complex; determining whether an infection is health care-associated can be challenging, particularly for patients who have had multiple health care encounters prior to the hospital admission. The model is limited to infections that are treated in a hospital setting, but the problem of infections acquired in long-term care is known to be substantial.21 If it were possible to quantify the number and treatment costs for health care-associated infections in other settings, estimates of the national impact of HAIs would increase. Lastly, also related to the count of HAIs included in this model, the analysis was limited to bacterial infections only. However, health care antiseptics, particularly alcohol-based hand rubs and gels, may have a role in preventing viral conditions.22 Thus, the findings of this study could be considered to be conservative and benefits would increase if viral conditions were included in the model. At this point, there are insufficient data to add the estimates for viral infections to the model but future studies may permit it.
A second source of uncertainty is related to estimating the proportion of cases prevented by the use of health care antiseptics. This aspect of uncertainty is challenging because, in accordance with the World Health Organization guide-lines,23 the use of health care antiseptics is only one com-ponent of typical multipart strategies to address hygiene. In their meta-analysis, Schweizer et al point out that more than three-fourths of interventions included bundles with multiple components rather than the single-intervention studies that have been observed in previous reviews (see Schweizer et al for a full listing of the studies reviewed).24 Rarely do studies report on a comparison between similar cohorts in which the use of health care antiseptics is the only difference. The effect of introduction or elimination of antiseptics alone is not addressed sufficiently in the literature for each of the infection types of interest. The model acknowledges this and is conservative in eliminating a number of infections that are deemed to be not preventable by any means and by assuming, in our high scenario, that no more than 30% of preventable infections could be prevented based on health care antiseptic use. The structure and form of the model are designed with the assumption that there is some effect of health care antiseptic use on the rate of HAIs, consistent with real-world findings,25,26 but it accepts the user’s input about what fraction of HAIs can be prevented rather than endorsing a particular value. It would have been possible to split the prevention component of the model into two separate pieces, one of which would apply values for the potential preventive effect of the antiseptics, and the second of which would allow the user to assume the level of performance to moderate the potential effect. Given the uncertainty in these inputs as well as the fact that they would simply be multiplied, we chose to handle this issue as a single model input. Further, the model only estimates incremental hospital costs for infections that can reasonably be attributed to the use of hand hygiene rather than a more comprehensive set of benefits. Thus, the estimates here are likely to be conservative.
Third, there is uncertainty about the financial impact of HAIs, although a variety of methods and approaches have been used to develop estimates. The health care facilities and sites that were used for the estimates in the model may have had an older/younger or sicker/healthier population than an average hospital. In most cases, the model used an aver-age of available cost estimates, which should minimize the influence of particular factors associated with an individual study or site on the final estimate used. Even if the cost for each type of HAI were known, it is important to recognize the difference between reimbursements, herein referred to as costs, and the actual costs that a hospital requires to treat a patient. Although insurers may not directly bear the costs for HAIs in the future given the trend toward not reimburs-ing hospitals for a growing list of preventable infections, hospitals will still need to provide the additional resources required to treat the infections.
The scope of this model includes only initial hospital costs associated with HAIs in the US. As such, the model inputs that required use of US data included the counts of HAIs and hospital costs. The model integrated data on pre-ventive potential and attributability to antiseptics from any study worldwide, with the assumption that the preventive effect of any agent would be similar regardless of the region in which it was used. There are a number of additional costs relevant to calculating the full impact of removing antiseptic products from the market not captured here. These costs include but are not limited to hospital readmission, short-term rehabilitation, long-term follow-up care, co-pays and out-of-pocket fees, lost wages, caregiver assistance, lost productivity, and transportation. General estimates for these elements are available in the published literature and could be combined with the HAI-specific inputs to this model to produce a more comprehensive evaluation of costs.27,28 As the costs of long-term morbidity and mortality are not cur-rently included in this analysis, the model’s estimates are conservative.
In addition to these sources of uncertainty, there is also a layer of regional variation that should be considered in applying these findings. Various hospitals, regions, and pay-ers may have different input assumptions than those used in this model based on the pathogens present in their facilities. Certain pathogens, resistance patterns, and infection types are more prevalent at some facilities and in particular regions than others, and thus while this model is designed to reflect the US as a whole, results cannot directly be scaled down to reflect a smaller region or population.
Not only are there differences in terms of HAIs and causes across sites but there may also be differences in populations. HAIs may cause disproportionate burden in certain racial and ethnic groups. Findings from an analysis of the Medicare Patient Safety Monitoring System suggest that the rate of HAIs is significantly higher among Asian and Hispanic patients.29 Because there are insufficient data to add patient characteristics to the model, it has not been included; however, the inclination to scale these estimates to subpopulations should be resisted for this reason, also.

It is important to recognize that this analysis does not challenge the idea that some hospital-acquired infections are not preventable. Even in the low scenario (which uses the most conservative estimates), the model assumes that some HAIs cannot be prevented. However, the reported number of HAIs may be influenced by lack of reimbursement. In their efforts to minimize rates of HAIs, hospitals may conduct more screening at admission to understand whether patients are already colonized at admission to determine whether infec-tions should be considered hospital-acquired,8 which could result in a decrease in infections determined to be hospital-acquired. Further, lack of reimbursement for some of these infections may encourage proactive antibiotic treatment and the unintended negative consequence of contributing to develop ment of resistance, making HAIs more expensive to treat. Regardless of these uncertainties, the underlying frame-work of this model assumes that there is some proportion of HAIs that are currently avoided as a result of the use of health care antiseptics, and that limiting availability of these types of products would be associated with an increase in the rate of these types of infections and associated hospital costs.

Although multiple sources of uncertainty exist, this model uses a range of estimates to effectively identify the plausible effect of health care antiseptic use on the number of HAIs and associated hospital costs in the US. Low- and high-end estimates of the number of national, annual HAI cases avoided through use of health care antiseptics are 12,100 and 223,000, respectively, with associated avoided hospital costs of US$142 million and US$4.25 billion, respectively.


Author contributions
JKS and CKH took primary responsibility for review of literature, JKS and SS took responsibility for model design and construction, and JAK, PCD, RS, and PAC provided inputs and interpretations. All authors contributed toward data analysis, drafting and revising the paper and agree to be accountable for all aspects of the work.

JK Schmier, CK Hulme-Lowe, S Semenova, and JA Klenk are employees of Exponent, a consulting company that has received a grant from the American Cleaning Institute for this research. PC DeLeo and R Sedlak are employees of the American Cleaning Institute. PA Carlson is an employee of Ecolab. The authors report no other conflicts of interest in this work.


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Jordana K Schmier1
Carolyn K Hulme-Lowe1
Svetlana Semenova2
Juergen A Klenk3
Paul C DeLeo4
Richard Sedlak5
Pete A Carlson6
1Health Sciences, Exponent, Inc., Alexandria, VA, 2EcoSciences, Exponent, Inc., Maynard, MA, 3Health Sciences, Exponent, Inc., Alexandria, VA, 4Environmental Safety, 5Technical and International Affairs, American Cleaning Institute, Washington, DC, 6Regulatory Affairs, Ecolab, Saint Paul, MN, USA

Is hand sanitizer better at preventing the flu than soap and water?

Frequent use of hand sanitizer, instead of soap and water, may lead to fewer respiratory infections, fewer sick days, and less antibiotic use — at least if youʼre a toddler. A Spanish study enrolled 911 children who attended day care, from newborns up to three-year-olds, and randomly assigned them to one of three groups.

In the control group, parents and caregivers continued usual hand care for the toddlers. In the two intervention groups, children were assigned to either labor-intensive hand sanitizer use or soap and water handwashing. Parents and caregivers were instructed to either apply hand sanitizer or wash the toddlersʼ hands when they arrived at the classroom in the morning; before and after lunch; after playing outside; after coughing, sneezing, or blowing their noses; after diapering; and before they left for home. In both groups, handwashing with soap and water was mandatory after using the toilet or when hands were grossly soiled.

Outcomes in the hand sanitizer group were significantly better than either the soap and water group or the control group. The hand sanitizer group had lower rates of respiratory infections and missed fewer days of school, compared to the other two groups. Kids in the hand sanitizer group were also less likely to be prescribed antibiotics for respiratory infections.

The families or day care providers in the hand sanitizer group went through 1,660 liters of hand sanitizer during the eight-month study. Based on this, the researchers estimated that each toddler used hand sanitizer six to eight times daily, on average.

There are reasons to take the results of this study with a grain of salt. A great deal of time and effort went into reinforcing the importance of hand hygiene. Researchers visited the day care centers every two weeks to tell stories and sing songs about germs and cleanliness. This probably led to levels of hand sanitizer use that would be difficult to duplicate in a real-world situation. As well, some, but not all previous studies of hand sanitizer use in preschoolers have shown lower rates of cold and flu infections.

The researchers did not assess how often the kids in the handwashing group actually washed their hands. It is possible that the better outcomes in the hand sanitizer group were related to the greater ease of use of hand sanitizer, compared to handwashing, which usually requires a little more time and effort.

Take-home points
• Hand sanitizer use in toddlers may be associated with lower rates of respiratory infections than handwashing with soap and water alone.

• Hand sanitizer use probably has to be fairly compulsive for users to see significant benefits.

• Hand sanitizer should contain 70% ethyl alcohol to reliably kill bacteria and viruses; some bacteria have shown tolerance to lower amounts of ethyl alcohol.

• Although there is little high-quality evidence on the benefits of hand sanitizer use in the community at large, the use of hand sanitizer, along with handwashing and flu vaccination, is a reasonable measure to reduce the risk of respiratory infections in adults at risk.

Hands Are Vehicles for Transmission of Streptococcus pneumoniae

Hands can be vehicles for transmission of pneumococcus and lead to acquisition of nasopharyngeal colonization, according to research published in the European Respiratory Journal.

Streptococcus pneumoniae (pneumococcus) is a major cause of acute otitis media, sinusitis, pneumonia, and meningitis worldwide, with more than 1.2 million attributed deaths annually. Colonization of the nasopharynx with these bacteria is a prerequisite for infection and it is the primary reservoir for transmission. It is theorized that transmission of pneumococcus occurs primarily through indirect contact through inhalation of airborne droplets and is associated with living in higher-density populations. For upper respiratory tract infections in general, direct contact is implicated in disease transmission, which can be interrupted by hand washing. However, the relative contribution of direct and indirect transmission modes to pneumococcal colonization and disease are unknown. Therefore, this study sought to assess the potential for pneumococcal hand-to-nose transmission to cause nasopharyngeal colonization.
A total of 63 healthy adult participants were enrolled into a controlled Experimental Pneumococcal Challenge model that was modified to assess
“hand-to-nose” (ISRCTN identifier: 12909224). Participants were divided into 4 transmission groups and administered pneumococcus (3.2X106 mid-log phase colony-forming units of S pneumoniae serotype 6B) onto their hand and asked to either sniff or make direct contact with the nasal mucosal surface with the bacterial residue either immediately after exposure (wet) or when visibly dry (1-2 minutes after exposure). The 4 groups were: (1) sniffing wet bacterial suspension (wet sniff), (2) sniffing bacterial suspension after air-drying (dry sniff), (3) pick/poke nose with finger exposed to wet bacterial suspension (wet poke), and (4) pick/poke nose with finger exposed to bacterial suspension after air-drying (dry poke). After 9 days of exposure, nasopharyngeal colonization was assessed through nasal washes and all samples were tested by quantitative polymerase chain reaction (qPCR) with primers for lytA and for S pneumoniae serotype 6A/b.

Of the 40 participants, 20% were found by culture at follow up to be experimentally colonized with pneumococcus (6B), with the highest rates in the wet poke (40%) and wet sniff (30%) groups. In the dry sniff and dry poke groups, 10% and 0% of participants, respectively, were found to be experimentally colonized. When wet and dry groups were compared, olonization rates in the wet groups were significantly higher than in the dry groups (P =.04, Fisher exact test). Molecular detection via lytA qPCR identified higher colonization rates compared with culture (P <.0001). This was most apparent in the dry poke group, which had a colonization rate of 0% by culture and 70% using qPCR. Samples that were only positive with qPCR, such as the dry poke group, tended to have lower densities of carriage compared with samples that were positive with both methods, such as both of the wet groups.
Overall, the results provide a better understanding of the duration of survival of pneumococci in nasal secretions on the hands and the entire transmission process. The study authors concluded that, “This modification of the Experimental Pneumococcal Challenge model has several potential uses, including testing of current or new hand cleaning interventions to ensure reduction in transmission of this important bacterial pathogen.”


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Connor V, German E, Pojar S, et al. Hands are vehicles for transmission of Streptococcus pneumoniae in novel controlled human infection study. Eur Respir J. 2018;52(4):1800599.

The bacterial horror of hot-air hand dryers

If youʼre the kind of person who avoids public bathrooms at all costs, you may feel validated, as well as disturbed, by a new study from researchers at the University of Connecticut and Quinnipiac University. They suspected that hot-air hand dryers in public restrooms might be sucking up bacteria from the air, and dumping them on the newly washed hands of unsuspecting patrons.

To test this theory, scientists exposed petri dishes to bathroom air under different conditions and took them back to the microbiology laboratory to look for bacterial growth. Petri dishes exposed to bathroom air for two minutes with the hand dryers off only grew one colony of bacteria, or none at all. However, petri dishes exposed to hot air from a bathroom hand dryer for 30 seconds grew up to 254 colonies of bacteria (though most had from 18 to 60 colonies of bacteria).

Were the bacteria multiplying inside the hand dryers, or were they being pulled into the hand dryers from the air inside the bathroom? To answer this question, the researchers attached high-efficiency particulate air (HEPA) filters to the dryers, which would eliminate most of the bacteria from the air passing through the dryer. When they exposed petri dishes to air from the hand dryers again, the quantity of bacteria in the dishes had fallen by 75%. As well, the researchers found minimal amounts of bacteria on the nozzles of the hand dryers. They concluded that most of the bacterial splatter from the hand dryers had come from the washroom air.

How did the bacteria get into the air in the first place? Unfortunately, every time a lidless toilet is flushed, it aerosolizes a fine mist of microbes. This fecal cloud may disperse over an area as large as six square meters (65 square feet). Aerosols from flushed toilets may be especially harmful in the hospital setting as a means of spreading Clostridium difficile.

Is there any good news from this study? Well, the vast majority of the microbes that were detected do not cause disease in healthy people, with the exception of Staphylococcus aureus. Some of the bathroom bacteria, such as Acinetobacter, only cause infections in people in the hospital, or in those with weak immune systems. The others that were found are relatively harmless. In addition, air from real-world bathrooms may contain fewer bacteria than the bathrooms in the study. The sampled restrooms were located in a university health sciences building, and at least some of the bacteria came from experiments going on in laboratories within the building.

So whatʼs a person to do to avoid picking up bacteria in a bathroom? You should still dry your hands, as not drying them after washing them helps bacteria to survive on them. Paper towels are the most hygienic way to dry your hands. For this reason, use of paper towels is already routine in health care settings. You may also wish to avoid jet air dryers, which have also been associated with the spread of germs in bathrooms. And remember that your chances of picking up a serious pathogen in a restroom are small. Direct contact with other people is much more likely as a means of acquiring infection