Epidemiological Study Proposal: Nursing Hand

Epidemiological Study Proposal: Nursing Hand Hygiene and Noscomial Disease Hand hygiene has increasingly been viewed as an important part of the hospital and nursing procedure based on the connection between nosocomial disease and habits of handwashing amongst healthcare workers. However, research has been inconclusive regarding the effects of hand-washing training as well as the cultural impacts on hand-washing habits. Therefore, the proposed research would measure these conditions as they relate to the epidemiology of nosocomial disease.

It is expected that the findings will demonstrate a positive correlation between the cultural enforcement of proper hand-washing techniques through effective training methods and the reduction of nosocomial disease. Introduction: Most HCAIs are spread by direct contact, mainly via the hands of healthcare professionals when they touch vulnerable patients or objects and equipment in the near-patient environment (Pittet et a1 1999. Hand hygiene is widely regarded as the most effective way of preventing HCAI on the premise that cleansing hands breaks the chain of infection (Beggs et al. 2009).

However, research studies and audits have demonstrated repeatedly that healthcare professionals perform hand hygiene too seldom and not always when hands are most likely to transfer pathogens, and that technique is often poor (Gould et al. 2008). The extent to which good hand hygiene compliance contributes to the prevention of HCAI is hard to establish. Many authors have made encouraging claims for the success of campaigns intended to boost hand hygiene (Pittet et al. 2000, Harbarth et al. 2002, Lam et al.

2004), but equally there have been a number of initiatives in which attempts to increase hand hygiene compliance have not met with success (Gould and Chamberlain 1997, Marra et al. 2008, Rupp et a1 2008). Many factors contribute to the transmission of infection and the susceptibility of the individual patient. Some pathogens responsible for HCAI are transmitted more readily than others because they resist drying and survive for relatively long periods on the skin (Gontijo Filho et a1 1985).

The severity of patients' underlying illness and the number of invasive procedures they have undergone, such as surgical operations, mechanical ventilation and urinary catheterisation, are also important factors contributing to susceptibility to infection. The cleanliness of the clinical environment may also play a part in determining HCAI rates.

Although there is a lack of evidence to demonstrate the effect of environmental cleanliness on HCAI rates (Rampling et a1 2001), it is logical to suppose that hands are likely to become re-contaminated easily after cleansing and that pathogens are transferred more readily in a non-clean environment. It is also worth considering what comprises 'good' hand hygiene.

Most research studies and audits measuring hand hygiene compliance address the frequency with which hands are cleansed, and whether cleansing occurs before and after patient contacts and other 'clean' and 'dirty' activities, but few studies have assessed technique (Gould et al. 2007). Failure to assess technique is a drawback in light of evidence that training health workers to ensure hand surfaces receive contact with alcohol can reduce infection rates (Widmer et al. 2007).

Despite these criticisms, it is logical to suppose that hand hygiene plays an important role in the prevention and control of HCAI because, if performed correctly, it should interrupt the chain of infection (Beggs et al. 2009). Moreover, a recent trial indicates that ensuring hand contact with adequate amounts of antiseptic for long enough for the product to exert its antimicrobial effect is central to reducing the numbers of bacteria present and in turn reduces the risk of cross-infection (Widmer et al. 2007).

The following research proposal proceeds from the understanding that those individuals staying in hospitals or other populated medical facilities are vulnerable to the host of bacteria and infectious diseases contained within their walls. This vulnerability provoked coinage of the term 'nosocomial,' which is used to describe conditions or aliments secondary to the condition for which an individual was admitted to the hospital. The implication is that this condition was caused by something present within the hospital, and not initially within the patient.

As the research conducted hereafter will indicate, this is most often caused by healthcare workers, presumably practicing poor sanitary practices. A discussion provided by the Centers for Disease Control (2007) offer the study the argument that the presence of nosocomial disease may often be attributed to poor hand hygiene compliance.

To the point, the CDC indicates that "rates of central line-associated bloodstream infections were significantly lower in hospitals with higher rates of hand hygiene." (Larson et al., 666) Confirming nosocomial infection as the proper focus for a dependent variable requires a surface understanding of the variable itself. Accordingly, a study by Beggs et al.

(2006) denotes that "direct contact between health care staff and patients is generally considered to be the primary route by which most exogenously-acquired infections spread within and between wards." (Beggs et al., 621) as the research considers the best manner in which to direct focus between cause and effect (i.e. dependent and independent variable), it is useful to recognize that personnel within the hospital are largely to be seen as the primary carrier of nosocomial infection. Beggs et al.

provide the research with a considerable degree of help in this area as well, indicating that "handwashing is therefore perceived to be the single most important infection control measure that can be adopted, with the continuing high infection rates generally attributed to poor hand hygiene compliance." (Beggs et al., 621) There is however a conflict between this declared perception and the finding produced by the study which contends that larger institutional problems such as staffing shortage and high worker turnover may actually contribute highly to the spread of infectious diseases.

For Beggs et al., there was an incapacity to isolate and therefore fully endorse a correlation between hand hygiene patterns and the reduction of infectious disease. This denotes the research problem at the center of the proposed study. Methods: The proposed study design is a qualitative study and would be conducted across two years of observation as modeled in the article by Rosenthal et al. (2005).

Here, a program of education, training and performance feedback would be implemented and the methodology would be centered on a survey of performance conducted every other week in correlation to the rate of nosocomial infection per 1000 patient days. (Rosenthal et al., 392) the sample would be the whole nursing staff for a selected hospital facility divided by different units. The intent would be to use the success of the study by Rosenthal et al. As model for the study here proposed.

This study's findings would produce some compelling evidence of the positive association between hand-hygiene and the prevention of disease. Accordingly, the study would conclude that, in "4347 opportunities for hand hygiene in both ICUs. Compliance improved progressively (handwashing adherence, 23.1% (268/1160) to 64.5% (2056/3187) (RR, 2.79; 95% CI: 2.46-3.17; P < .0001).

During the same period, overall nosocomial infection in both ICUs decreased from 47.55 per 1000 patient-days (104/2187) to 27.93 per 1000 patient days (207/7409)" (Rosenthal et al., 392) This is an outcome which substantially endorses the need for improved hand-hygiene practices in hospitals where compliance is low and for further study to validate the best ways to achieve this. The research proposed here also is supported by the research design used by Roberts et al.

(2009), which intended to address the perspective that some methods of hand-washing are superior to others with respect to the prevention of the spread of infection or disease. This is the case with the study conducted by Roberts et al. (2009), which would be taken up with the intention to examine the effectiveness of alcohol-based hand washing methods which have been so extensively proliferated in recent years within the medical field. The study would employ an experimental design in multiple phases.

The methodology promotes the intention to draw a comparison between the use of alcohol-based hand cleaners and the use of liquid soap and water, as well as to draw a comparison between the use of alcohol-based hand cleaners with training and without training. For our purposes, variables will center not on the difference between sanitation method bout on the training methods applied in different nursing units, the cultural outcomes produced and the health outcomes produced.

Therefore, the method would divide selected nurses into two separate groups, which would begin the first phase of the study using both hand sanitizer and liquid soap and water. After a period of nine weeks, Groups a and B. would be differentiated for the second phase. Here, Group a would continue with the same hand-washing program while Group B. would be considered the intervention group. The intervention would, for Group B, engage the participants in regular training in hand hygiene with both hand sanitizer and soap and water.

This phase would also last for a duration of nine weeks, with the intent to produce two separate measurements to follow each phase. Once the factual questions have been used, questions regarding beliefs, impressions, feelings and emotions can be integrated into the questionnaire. These are questions dealing with attitude and are the most important questions when doing qualitative social science research to gauge relationships among events. In addition to construction questions about attitudes, it is important to have the questions drafted in the correct format (Nachmias, 2008).

The Quantitative methodologies will be the statistical tests designed for the overall model to incorporate the information provided through one, two or all of the Qualitative data analysis methodologies. The tests used to determine the relationship between these "qualitative" factors and increases in Infection rates, will be the Chi-Square, Student's T-Test, ANOVA (to test for variations among the data), the construction of a Linear Regression Model and the calculation of the Pearson Correlation Coefficient, otherwise known as "R-Squared" (Nachmias, 2008).

These tests will be utilized in conjunction with a predetermined level of significance, or alpha. Since these tests will all be measuring the means and relationships of one data set, the level of alpha to be used in these tests is set at .05. Therefore, the resultant calculations will be compared to this level of alpha to determine if there is any statistical significance between these factors and increases in infection rates. To ensure the validity of the linear models, the R-squared value will be calculated and included within the analysis.

There are numerous classes of the R-squared value, however for our purposes; it will be the "coefficient of determination" that accompanies linear regression models. Consequently, the main role of R-squared in our analysis will be to provide an explanation for the variances in the data. For example, if the R-squared value of the PDI for a certain subset of the population equates to .88, this would translate into the PDI being responsible for 88% of the data results, with 12% being attributed to chance or randomness.

The formula that will be incorporated into this research for R-squared is as follows: and the traditional standard of "goodness of fit" will be applied to the R-squared values; wherein, a R-squared result of 1.0 indicates the data fits perfectly and conversely, a R-squared value of 0.0 indicates no correlation between the two variables and all the data points are the result of randomness and chance. Furthermore, the various Chi-Square analysis will be broken down across age categories in order to determine the impact of generational status on the various elements.

The standard formula for Chi-Squared will be utilized; the formula is as follows: Error margin The allowable error margin as plus or minus 15 percentage points. Pattern of responses The expected pattern of responses is as follows (see plot). 'Response a' (40%), 'Response B' (60%). In particular, the percentage for 'Response a' is 40%. Margin of error One factor that determines the required sample size is the acceptable margin of error. If we are willing to accept a relatively wide margin of error we'll need a relatively small sample.

By contrast, if we desire a relatively narrow margin of error we'll need a relatively large sample. For an error margin of plus/minus 15.00 points we need 41 subjects. If we were to double the error margin (to 30. The sample size would be reduced to 11 subjects. By contrast, if we were to cut the error margin in half (to 7. The required sample size would increase to 164 subjects. We assumed that the percentage in this category is 40% which led to a sample size of 41.

If the true percentage is actually 30% the required sample size would be 36. If the true percentage is actually 50% the required sample size would be 43. Alpha In computing the sample size we assume that we want to be 95% certain that the observed value falls within the margin of error (rather than 90% certain, for example) and also that we are concerned with errors in either direction. Changing either of these assumptions would also affect the sample size required. Results: According to the data presented in the Figure, the R-squared value is .7987.

As this graph demonstrates there is a distinct correlation between these two variables. The dotted lines represent the Confidence Interval for this analysis. This Confidence Interval represents the relationship between these two variables. The solid line in the figure represents the regression of the means of the data involved in the analysis. As the Confidence Interval is closer to the solid line this indicates that the relationship is based on a statistical relationship and not random chance.

As this figure demonstrates, the relationship between washing hands by nursing staffs and the increase in Nosocomial Infection rates is based on statistical correlation, therefore it can be asserted that this one specific, cultural value can have an impact on attitudes and behaviors and negatively impact these behaviors to the end result of increasing infection rates. Using the standard Chi-Square Test formula we arrive at the following.

The p-value for this Chi-Square Test is .0001 or there is a .01% chance that the variations within this data are due to chance and randomness. Therefore, it can be concluded that Hand Washing Protocols are a very significant factor in developing various levels of Nosocomial Infections. Utilizing the Standard T-Test equation, we arrive at the following. The t-value is .3171 with a corresponding p-value of .7619.

Since the p-value is greater than the predetermined level of significance, .05; it can be concluded there is a 76% chance the variations in this data are predicated on statistical relationships. With the p-value of the T-Test being in doubt it is relevant that another form of analysis be conducted to determine if there is a relationship. Therefore, a linear regression model with R-Squared testing was conducted. Using the same data we arrive at the following result. The R-Squared value is .9026.

This indicates the relationship between Hand-hygiene and Infection Rates has a 90.26% correlation. Therefore, this indicates the strong relationship between the two values. An additional test conducted to ensure validity was an F-Test. Using the data from Table 2.8, the F-Test revealed F = 151737, the P. value is < 0.0001. This test suggests that the difference between the two Standard Deviations is extremely significant. Best-fit Standard 95% confidence interval Parameter Value Error from to Slope 0.02554 0.007777 -0.007922 0.05901 Y intercept 2.427 0.2925 1.168 3.686 X intercept -95.01 As the table demonstrates, the 95% Confidence Interval is between -0.007922 and 0.05901.

The slope o the linear regression is .02554, this is within the Confidence Interval of the model. Furthermore, the R-Squared calculation is 0.84; therefore the data represents an 84% relationship between Hand-Hygiene and Infection Rates. As we can see, the Linear Regression (represented by the solid line) is relatively close to the upper and lower limits of the Confidence Interval (represented by dotted lines).

This indicates there is a likely correlation between the Mean MAS scores and reliance on We project the likely findings of such a study based on the findings produced by similar studies of this nature. Accordingly, the study by Sax et al. is compelling in terms of the preeminent finding the social pressures are likely to have a significant impact on handwashing behaviors.

The consideration that behavioral tendencies in this area will hinge upon normative conditions proceeding from peers, superiors and the industry as a whole does provide the research with an area from which to draw some clear recommendations. Namely, as the discussion here below touches upon the failure of certain training objectives with respect to hand hygiene, it is useful to consider that an entwining of hand washing goals with other cultural priorities can significantly impact the positive adoption of desired behaviors.

This finding would be reinforced by an echoed perspective in the study by Barrett & Randle, which similarly identified the capacity for social pressure and the construction of a positive culture endorsement of hand hygiene as having a likely positive impact on behavioral tendencies in this area.

To this end, they would observe that "respondents emphasized the importance of fitting into the clinical area and role models in shaping hand hygiene compliance." (Barrett & Randle, 1851) Discussion: Infections related to healthcare are among the most important causes of morbidity and mortality in hospitalized patients. A study of prevalence carried out by the World Health Organization (WHO) in 55 hospitals from 14 countries, showed that 8.7% of hospitalized patients contract Nosocomial Infections (NI).

The importance of NI in terms of morbidity, mortality, impact on quality of life in patients and relatives and secondary economic costs, has been emphasized repeatedly in the last years. In the developed countries, around 5-10% of patients admitted to hospitals for acute conditions presented an infection that was not being incubated or present at the time of admission. Healthcare-related infections are the direct cause of 80,000 deaths in the United States and 5,000 deaths in England every year.

According to data from the Survey on Prevalence of Nosocomial Infection in Spain (EPINE study) for 2006, NI affected between 7% and 9% of patients admitted to Spanish hospitals. These data are very similar to those for developed countries in terms of frequency, economic cost and mortality.

NI present many of the characteristics that define a significant problem in patient safety: affect millions of people all over the world, complicate patient care, contribute to the patient death or temporary/permanent disability, increase resistance to antimicrobials and generate substantial additional costs in the treatment of the patient disease. There are many causes of NI, which are related to healthcare systems and processes, as with the behavior of the professionals involved.

The results of the Study of the Efficacy of Nosocomial Infection Control (SENIC study) finding that vigilance is an effective method for the prevention of NI [6, 7]. Indeed, in the hospitals included in the infection prevention program where prevention and control activities were carried out, infection rates was a reduction near to 32%. Other studies have shown the benefits of NI prevention in healthcare and economic terms.

The areas of action against these infections are based on simple and well established precautions which have been seen to be effective and widely accepted - the "ordinary precautions" cover all the basic principles for controlling infections that required in all healthcare centers. They are applied to all patients regardless of their diagnosis, risk factors, and infection status, in order to reduce the risk to the patient and healthcare workers of contracting infections.

Hand hygiene (HH) is an important element in ordinary precautions and is the most effective measure for preventing infections. The hands of health workers (HCWs) are the most common carrier of transmission of microorganisms from one patient to another, from one area of the patient's body to another and from a polluted environment to patients. The HH is considered the most important measure, because of its proven efficiency (it is estimated that the impact on the reduction of NI is 50%), its effectiveness, and its low cost [10].

However, there is poor compliance with HH regulations by healthcare workers all over the world, and all the studies carried out in hospitals suggest that the frequency of compliance is lower than 50% of the opportunities in which the practice is considered a priority [11, 12].

There are different factors contributing to low levels of HH compliance, both among the professionals: lack of knowledge of the importance of preventing NI, a lack of understanding of the appropriate techniques involved, the occurrence of contact dermatitis; and by the healthcare organization: staff shortaged, work overload, difficult access to points used for conventional hand hygiene, and finally, the absence of an institutional commitment to overall improvement of HH. Pittet et al.

[13], carried out a study in a university hospital, based on direct observation of physicians, and identified behavioral factors associated with beliefs, attitudes and perceptions in non-compliance of HH. There was over 75% believed that not performing HH led to a higher risk of cross-transmission, 72% thought that HH was unnecessary after removing gloves and 72% thought that HH was necessary after each patient.

The positive impact of hand washing is particularly underscored here by such studies as that presented by Rosenthal et al., which found that especially in poor facilities where education, training and resource are lacking, the reduction of infectious disease can be highly associated with formalized improvements in hand washing techniques. How to best go about achieving an optimally effective intervention remains up for discussion though, as denoted by other studies.

This causes us to project that that proposed study would produce findings supporting these epidemiological presumptions, namely that which proposes that proper training and the cultivation of cultural cues will improve a commitment to proper handwashing and will therefore result in the reduction of communicable diseases in the healthcare setting.

It is expected that the intervention group in this case will demonstrate a far greater set of outcomes in terms of both creating a culture where proper hand hygiene is expected and practiced and in terms of producing better treatment outcomes for hospital patients on the whole. From the results it is apparent that the relationship between textile composition and bacterial diversity previously noted in vitro translates to in vivo conditions.

Additionally, the fact that diversity was assayed through visual assessment of colony type means that more diversity may have existed within the samples than was noted due to visual similarities. Initial culturing of samples on nutrient agar also excluded the growth of fastidious organisms.

These two factors do limit the degree to which diversity observed fails to encapsulate total diversity, but as the same treatments were applied to both classes of textile samples it can be reasonably postulated that both classes of textiles would have their diversity reduced in similar degree. We therefore propose that observed differences do reflect real differences in bacterial populations on the two classes of textiles.

Additionally, though the length of the semester did not permit species identification, the higher incidence of alpha-haemolysing bacteria on the artificial textiles inconclusively suggests that these populations may contain more potential pathogens. These results have implications for health care policy regarding composition of hospital-mandated garments, and can be applied to other studies related to hospital safety. For example, the 2000 study by Tammelin et al. found data to support that tightly-woven textiles reduced MRSA transmission in the operating theater (9).

Potentially, use of tightly-woven garments made of natural fibers would further reduce transmission due to their decreased pathogen load. There are factors beyond fiber choice that influence the bacterial load of hospital garments. It has been shown that under the standard temperature conditions used in home washing machines, Gram positive bacteria were easily transferred from contaminated to previously sterile garments (10). Data also suggest that liquid and powdered laundry detergents are ineffective at preventing bacterial cross-contamination between garments or removing Gram positive microorganisms (11).

The above studies demonstrate that protocols involving the appropriate care of hospital-mandated garments may provide an important contribution to the prevention of nosocomial infections. Regarding the development of such protocols, it is known that bleach-based cleaners are effective at removing microorganisms (11). However, cleaners in this category often damage or destroy artificial fibers. This information suggests another reason why natural fibers may be a superior choice when attempting to prevent transmission of potentially pathogenic microorganisms. Direct observation is labor-intensive and therefore time consuming.

Data collectors require training and monitoring to ensure that they are all undertaking observation and documentation in the same way. Their presence may alter usual behavior in ways that are unpredictable. A recognized auditor in a position of authority might promote increased hand hygiene compliance- the 'Hawthorne effect' (Box 4) -- but a new or junior member of the infection prevention and control team might have considerably less impact (Eckmanns et a1 2006). Direct observation is currently the most common method used to audit hand hygiene compliance.

Product uptake Measuring product uptake involves assessing the weight or volume of liquid soap and skin disinfectants used over a given period of time. This approach is much less time consuming than direct observation, does not involve auditors being trained or monitored and is unlikely to alter healthcare professionals' usual behavior. However, the results can be inaccurate either because the products are lost through wastage or because they are used for some unintended purpose such as general cleaning.

Other disadvantages are lack of information about the quality of the hand hygiene episode with which staff are complying, loss of opportunity to correct poor compliance and absence of contextual information that may help to identify and.