The Department of Health and Human Services (DHHS) strives to reduce cases of infections at care facilities, but with mixed results. In this study, healthcare-associated infections (HAIs) were reviewed based on broad global and social determinants of health; epidemiologic data; the effectiveness of clinical prevention interventions; patient centered and culturally responsive strategies; and clinical prevention concepts.
specifically for you
for only $16.05 $11/page
It was noted that HAIs contribute largely to the increased percentage of deaths in the US. Although guidelines for HAIs have been developed, some findings suggest that some infections are poorly defined, understood and may not be a suitable categorization tool for patients with multidrug-resistant (MDR) etiology, such as health-care associated pneumonia. Nonetheless, it is shown that HAIs identification should be based on individual patients, local factors, and risk scoring tools for appropriate determination of infections and provide recommended interventions.
The primary goal of the Department of Health and Human Services (DHHS) is to reduce cases of healthcare–associated infections and notably, substantial achievements in prevention have been documented for some infections (Magill et al., 2014). In this research, the focus is on all healthcare-associated infections based on broad global and social determinants of health; epidemiologic data; the effectiveness of clinical prevention interventions; patient centered and culturally responsive strategies; and clinical prevention concepts.
HAIs are a major cause of death in the United States. Data from the Centers for Disease Control and Prevention (2016) show that there were an estimated 722,000 HAIs in the US acute care hospitals, and nearly 75,000 patients with HAIs died during their hospitalization. Further, over half of all HAIs did not take place in the intensive care unit (ICU) (Centers for Disease Control and Prevention, 2016).
HAIs reflect latest exposure of patients to the healthcare services systems who are at enhanced danger of harboring multidrug-resistant organisms (MDROs) (Rothberg et al., 2014). Based on the available evidence, patients who lately visited care facilities have been identified as having elevated possibility of infection with MDR microbes (Russo, Falcone, Giuliano, Guastalegname, & Venditti, 2014).
In some instances, however, previous studies and antimicrobial treatments recommended in guidelines did not account for some HAIs, for instance, healthcare-acquired pneumonia (HCAP). Additionally, majorities of doctors are also not aware of the risk factors associated with some HIAs, fail to inform their patients, and the clinical importance of identifying them from other known cases (Bo, Amprino, Dalmasso, & Zotti, 2017).
|Table 1: HAI Estimates Occurring in US Acute Care Hospitals, 2011|
|Major Site of Infection||Estimated No.|
|Urinary Tract Infections||93,300|
|Primary Bloodstream Infections||71,900|
|Surgical site infections from any inpatient surgery||157,500|
|Other types of infections||118,500|
|The estimated total number of infections in hospitals||721,800|
Review of the Literature
Healthcare–associated diseases (HAIs), noted as infections a patient acquires while getting health care in a care facility, are vital patient safety challenges (Shang, Stone, & Larson, 2015). In the US, for instance, there were about 722,000 HAIs in acute care facilities, with the greater fraction of HAIs happening outside the intensive care unit (ICU). It was also estimated that on any given day, about 1 in 25 hospital patients at least reported HAI, and per annum there are roughly 75,000 hospital mortality cases attributed to HAI (Shang et al., 2015).
100% original paper
on any topic
done in as little as
HAIs have been linked to increased costs of care, for instance, it costs about $9.8 billion every year. Irrespective of the health burden, most cases of HAIs are actually preventable. Therefore, lessening avoidable HAIs has turned out to be one of the critical elements of the action plan of the DHHS to ensure a safer, reasonable healthcare system. Moreover, it is ranked among the top issues for hospital managers in their endeavors to curtail costs of care and enhance the quality of care (Shang et al., 2015). Pneumonia, for instance, presents a significant challenge to care providers.
Although most causative pathogens of all HAIs are known, according to Noguchi et al. (2015), the causative pathogens of HCAP are still controversial, and the application of normal samples of sputum cultivation is sometimes not suitable because of the possible contamination with oral bacteria. It is also at times hard to decide if methicillin-resistant Staphylococcus aureus (MRSA) is an actual causative pathogen of HCAP (Noguchi et al., 2015).
From the findings of Noguchi et al. (2015), it was determined that HCAP patients had heterogeneous bacteria and high incidence of streptococci relative to that observed using cultivation techniques. Moreover, findings of the study showed a lower rate of MRSA than already anticipated in HCAP patients. In this case, care providers may find it difficult to handle some instances of HAIs.
Throughout the last few years, general ways to deal with the HAI prevention have assumed two conceptually various ways. They have been classified as vertical strategies that strive to eliminate colonization, infection, and transmission of particular pathogens, essentially through utilization of active surveillance testing (AST) to distinguish carriers, then the implementation of measures targeted at controlling transmission from carriers to patients (Septimus, Weinstein, Perl, Goldmann, & Yokoe, 2014).
The second classification involves horizontal strategies that strive to decrease the risk of contamination because of an expansive type of pathogens through adoption of standardized practices that go beyond patients’ unique conditions (Septimus et al., 2014). Cases of horizontal infection control systems incorporate limiting the unwarranted utilization of invasive therapeutic gadgets, improving hand hygiene, environmental cleanliness, and advancing antimicrobial controls (Septimus et al., 2014).
While vertical and horizontal methodologies are not totally unrelated and are frequently intermixed, some practitioners claim that the horizontal strategy under normal endemic circumstances may offer the best general value based on the differing qualities of microorganisms that can bring about HAIs and the limited resources accessible for preventing infections. When local knowledge of microbes, epidemiology, and ecology driven by a solid quality change program is applied, it permits care facilities to concentrate on strategies that focuses on all as opposed to chosen pathogens without a specific pathogen epidemic (Septimus et al., 2014).
Further, surveillance of HAIs is fundamental to healthcare epidemiology and infection management measures and a vital factor for controlling infections (Mitchell & Russo, 2015). Infection prevention reflects the possibilities of reducing HAIs, and the main goal of surveillance is to present quality information that can be utilized as a part of a compelling monitoring and an alert system and to limit the frequency of preventable HAIs (Mitchell & Russo, 2015).
In some instances, HAIs are presented as infections noted in nursing home areas, patients hospitalized for not less than 2 days in the past three months, patients getting home infusion interventions or wound care, and patients visiting a hemodialysis care facility in the past 30 days (Chalmers, Rother, Salih, & Ewig, 2014). The categorization of HAIs may depend on the thinking that patients with successive healthcare contacts would at first need a wide range of anti-microbial treatments since they would be at higher risk for resistant pathogens (and thus higher death rates) relative to patients with no such contacts (Chalmers et al., 2014).
It is also sensible to presume that treating all HAI cases in a similar fashion would prompt over treatment in locations with low MDR microbe presence and under treatment in locations with high rates. Chalmers et al., (2014) also claim that in reality, the high rate of mortality in HAIs is more probably caused by old age and co-morbidities than MDR microbes, but there are risks of wide range antibiotic agents (such as Clostridium difficile infection, advancing antibiotic resistance).
The findings by Chalmers et al. (2014) offer some clinical implications for nurses. The results make a solid claim for the need to comprehend the local instances of MDR microbes and to determine the most suitable treatment regimen in areas where such prevalence is high. Accordingly, it is also imperative to determine risk factors for MDR microbes in specific patients. For instance, MDR pathogen risk scores can be applied to assist care providers to classify risks of these microbes objectively to determine the most appropriate antibiotic treatment.
Evidence demonstrates that HIAs require local and specific interventions. Chalmers et al. (2014) argue that interventions for HIAs, such as pneumonia, should be guided by the local cases of MRD pathogens (Livorsi & Eckerle, 2014). Further, Livorsi and Eckerle (2014) also support the need to develop a local syndromic antibiogram specific to a given infection. Hence, local microbiologic data could be useful for providing information that is more relevant than the current global one. Nonetheless, Livorsi and Eckerle (2014) recognized the challenges associated with developing a microbiologic diagnosis for some HAI patient populations, local guidelines can likewise be derived by checking clinical results in patients who meet certain criteria but treated (Livorsi & Eckerle, 2014).
In some cases, it could be difficult to isolate patients based on the types of infections under a given category, for instance, pneumonia (Corrao, Venditti, Argano, Russo, & Falcone, 2014). Corrao et al. (2014) note that they do not know whether healthcare-acquired pneumonia itself is the most proper instrument to use to isolate patients with the multidrug-resistant etiology, but reliable findings show that a fraction of patients with the community-acquired pneumonia has a multidrug-resistant infection, and in most of these cases, patients satisfy the healthcare-associated pneumonia definition.
The varying etiology of infections in patients exposed to care facilities have been plainly shown in different kinds of infections, including bacteremia, endocarditis, unconstrained bacterial peritonitis, and urinary tract diseases. However, pneumonia remains an uncertain issue due to the poor evidence currently available in published studies (Corrao et al., 2014).
Determinants of Healthcare-Associated Infections
Currently, the healthcare system has witnessed tremendous changes, which have transferred multiple aspects of care from hospital settings to community settings. Consequently, the known variations between hospital-acquired infections and community-acquired infections have become less clear. For this, more successive contacts with care facilities, patients, particularly the fragile elderly patients, have increased risks of multidrug-resistant (MDR) colonization and to acquire more serious pneumonia, with an actual risk to get ineffective empiric antibiotic treatments and, in this way, to have a severe outcome (Russo et al., 2014).
Determinants and epidemiology of HAIs may not always be readily known, for instance, in neonatal, resource-limited settings in emerging worlds (Pathak, Singh, Jain, Dhaneria, & Lundborg, 2014). Findings by Pathak et al. (2014) confirm that HAI is extremely common in resource-constrained settings, and HAI surveillance can be improved by combining various aspects associated with HAIs.
100% original paper
written from scratch
specifically for you?
HAIs are infections developed either within or outside care facilities based on the availability of risk factors for multidrug-resistant (MDR) microbes due to earlier contacts with care facilities. Specifically, risk factors have been identified as “hospitalization in the previous 90 days, residence in a nursing home, home infusion therapy, chronic dialysis, and contact with a family member harboring MDR pathogens” (Russo et al., 2014, p. 5387).
The Effectiveness of Clinical Prevention Interventions
According to Rothberg et al. (2014), some HAI patients may present results that are more serious than results obtained for infections, even after controlling for comorbidities and demonstrating the seriousness of instances of pneumonia. In pneumonia, for instance, recent findings have failed to show enhanced results when guideline-concordant (GC) antibiotics are given to patients (Attridge et al., 2016).
The study by Attridge et al. (2016) was intended to assess the relation between patient outcomes and GC treatment in patients admitted to an intensive care unit (ICU) with pneumonia. The study major outcome was a 30-day patient mortality rate, and risk factors for the major result were evaluated and the findings did not show enhanced outcomes in ICU patients with patients who received GC-HCAP treatments (Attridge et al., 2016). This finding could perhaps explain why the rate of pneumonia is higher than other cases of HAIs.
HAIs are usually based on the Infectious Diseases Society of America (IDSA) guidelines, which recommend specific interventions, such as the extended-spectrum antibiotic treatment for patients meeting healthcare-associated pneumonia criteria (Webb, Dascomb, Stenehjem, & Dean, 2015). In any case, the prescient value of some models are constrained, and evidence shows that outcomes are not enhanced following the use of guideline-concordant treatment and, thus, improved techniques to anticipate the risk of infections are required (Webb et al., 2015). Further improvements and validation of prediction scores derived from risk factors that are more thorough for infections are required. Once a precise, satisfactorily certified prediction score is provided, its clinical importance will be assessed.
For effective patient-centered strategies, it is recommended that possibilities of patients from the community to care facilities and have MDR versus non-MDR infections should be determined. Risk factor tools can help care providers to determine the exact challenge and decide on the most effective intervention therapies.
Nonetheless, risk factor tools should be individualized to account for local epidemiology. It ought to be evident that a risk factor assessment, a microbe analysis, and a decision on the correct treatment are only few considerations for averting mortality in HAIs patients. All assessments should be linked to specific therapies centered on stabilization of the patient’s immune response and effective management of comorbidities (Restrepo & Aliberti, 2014).
It is imperative to recognize that guidelines for HAIs are developed based on scientific evidence and, therefore, physicians who administer therapies should account for cultural issues of their patients. Hence, it is imperative to identify patients to account for HAIs sufficiently to ensure that patients with obviously repeated cases are clearly identified (Komiya, Ishii, & Kadota, 2015).
The aim of patient-centered care is to account for the unique needs of patients. In this case, patient education, patient knowledge, engagements of family members to ensure safety, and any concerns associated with the care provision should be sufficiently handled. Patients should also be allowed to bring their own unique perspectives and care agendas. Such engagement is advanced by developing an effective rapport and providing different instructive materials to facilitate patient involvement in care provision.
Clinical Prevention Concepts
By developing guidelines for HAIs, the IDSA recognized that HAI patients are at elevated risk for infection with MDR microbes and the lack of effective antibiotic treatments or protection contributes to increased mortality. Risk factors for the advancement of HAIs and the rise of infections from medication-resistant microbes, basically methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa, are not similar among the category of patients with different cases of HAIs.
For instance, dialysis patients face various risks relative to nursing home patients (Shorr & Zilberberg, 2015). Besides, there is relevance heterogeneity of risk factors for HAIs within various groups and subgroups because of differences in local factors, for example, local microbiology and strategies for providing care and differences in individual risk components, for example, status or earlier antibiotic contacts.
Additionally, it is imperative to evaluate evidence for other causes of HAIs, such as possible risk factors for cases of HAI drug resistance. Thus, the intervention should always focus on patients at greater risks for HAIs. Still, within the scope of infections, care providers should always ensure that they account for different types of infections based on types of risk, including MRSA and possible resistance to drugs (Bo et al., 2017).
Researchers have developed different risk scoring devices to categorize patients based on the probability that their infection has emanated from pathogen, for example, methicillin-resistant S. aureus or P. aeruginosa (Shorr & Zilberberg, 2015). Results from the scoring tools give accurate means to isolate patients on the premise of the possible recuperation from resistant microbes than does other infections. The vast majority of these risk-scoring tools are simple to compute and use, and a few have been independently tested for validity (Shorr & Zilberberg, 2015). Overall, effective care requires care providers to consider using these instruments in their strategies to handle patients with various cases of HAIs.
HAIs are among the major killers in the US, they have been ranked based on the available data, and pneumonia is the most common. For HAIs, the IDSA recognized deaths related with them and, it developed guidelines to ensure quality care and outcomes.
While a major achievement has made in controlling some types of HAIs, there is significantly more work needed to handle serious cases, such as pneumonia and gastrointestinal illness. Major initiatives can be implemented to manage and eliminate HAIs in different settings. Findings demonstrate that when healthcare settings, practitioners, and individual physicians and nurses, know about HAI issues and find a way to prevent them, rates of infections and deaths decline significantly (Centers for Disease Control and Prevention, 2016). Eliminating HAIs is largely possible, but it requires a conscious contribution of all stakeholders in the healthcare system to enhance care, protect patients, and reduce mortality rates.
Attridge, R. T., Frei, C. R., Pugh, M. J., Lawson, K. A., Ryan, L., Anzueto, A.,… Mortensen, E. M. (2016). Health care–associated pneumonia in the intensive care unit: Guideline-concordant antibiotics and outcomes. Journal of Critical Care, 36, 265–271. Web.
Bo, M., Amprino, V., Dalmasso, P., & Zotti, C. M. (2017). Delivery of written and verbal information on healthcare-associated infections to patients: Opinions and attitudes of a sample of healthcare workers. BMC Health Services Research, 17, 66. Web.
Centers for Disease Control and Prevention. (2016). HAI Data and Statistics. Web.
Chalmers, J. D., Rother, C., Salih, W., & Ewig, S. (2014). Healthcare-associated pneumonia does not accurately identify potentially resistant pathogens: A systematic review and meta-analysis. Clinical Infectious Diseases, 58(3), 330-339. Web.
Corrao, S., Venditti, M., Argano, C., Russo, A., & Falcone, M. (2014). Healthcare-associated pneumonia and multidrug-resistant bacteria: Do we have a convincing answer? Clinical Infectious Diseases, 58(8), 1196-1197. Web.
Komiya, K., Ishii, H., & Kadota, J.-i. (2015). Healthcare-associated pneumonia and aspiration pneumonia. Aging & Disease, 6(1), 27–37. Web.
Livorsi, D., & Eckerle, M. K. (2014). Developing local treatment guidelines for healthcare-associated pneumonia. Clinical Infectious Diseases, 59(4), 609-610. Web.
Magill, S. S., Edwards, J. R., Bamberg, W., Beldavs, Z. G., Dumyati, G., Kainer, M. A.,… Fridkin, S. K. (2014). Multistate point-prevalence survey of health care–associated infections. The New England Journal of Medicine, 370(13), 1198-208. Web.
Mitchell, B. G., & Russo, P. L. (2015). Preventing healthcare-associated infections: The role of surveillance. Nursing Standard, 29(23), 52-58. Web.
Noguchi, S., Mukae, H., Kawanami, T., Yamasaki, K., Fukuda, K., Akata, K.,… Yatera, K. (2015). Bacteriological assessment of healthcare-associated pneumonia using a clone library analysis. PLoS ONE, 10(4), e0124697. Web.
Pathak, A., Singh, P., Jain, S., Dhaneria, M., & Lundborg, C. S. (2014). Incidence and determinants of health care associated blood stream infections at a neonatal intensive care unit in Ujjain, India: Results of a prospective cohort study. International Journal of Infectious Diseases, 21(S1), 48. Web.
Restrepo, M. I., & Aliberti, S. (2014). Healthcare-associated pneumonia: Where do we go next? Clinical Infectious Diseases, 58(3), 340–341. Web.
Rothberg, M. B., Haessler, S., Lagu, T., Lindenauer, P. K., Pekow, P. S., Priya, A.,… Zilberberg, M. D. (2014). Outcomes of patients with healthcare-associated pneumonia: Worse disease or sicker patients? Infection Control and Hospital Epidemiology, 35(S3), S107-S115. Web.
Russo, A., Falcone, M., Giuliano, S., Guastalegname, M., & Venditti, M. (2014). Healthcare-associated pneumonia: A never-ending story. Infectious Disease Reports, 6(2), 5387. Web.
Septimus, E., Weinstein, R. A., Perl, T. M., Goldmann, D. A., & Yokoe, D. S. (2014). Approaches for preventing healthcare-associated infections: Go long or go wide? Infection Control and Hospital Epidemiology, 35(7), 797-801. Web.
Shang, J., Stone, P., & Larson, E. (2015). Studies on nurse staffing and healthcare associated infection: Methodological challenges and potential solutions. American Journal of Infection Control, 43(6), 581–588. Web.
Shorr, A. F., & Zilberberg, M. D. (2015). Role for risk-scoring tools in identifying resistant pathogens in pneumonia: Reassessing the value of healthcare-associated pneumonia as a concept. Current Opinion in Pulmonary Medicine, 21(3), 232–238. Web.
Webb, B. J., Dascomb, K., Stenehjem, E., & Dean, N. (2015). Predicting risk of drug-resistant organisms in pneumonia: Moving beyond the HCAP model. Respiratory Medicine, 109(1), 1-10. Web.