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ORIGINAL ARTICLE
Year : 2014  |  Volume : 1  |  Issue : 2  |  Page : 39-43

Analysis of a multi-centric pooled healthcare associated infection data from India: New insights


1 Medical Superintendent, Department of Medical Administration, Amrita Institute of Medical Sciences, Kochi, Kerala, India
2 Head Critical Care and Anaesthesia, Fortis Hospitals, Bangalore, India
3 Chief, Dept. of Microbiology, Medanta Hospital, New Delhi, India
4 Chief of Lab Services, Ruby Hall Clinic, Pune, India
5 Administrative Assistant, Medical Administration, Amrita Institute of Medical Sciences, Kochi, Kerala, India
6 Sr. Research Fellow, Department of Medical Administration, Amrita Institute of Medical Sciences, Kochi, Kerala, India

Date of Web Publication12-Feb-2015

Correspondence Address:
Dr. Sanjeev Singh
Amrita Institute of Medical Sciences and Research Centre, Kochi, Kerala
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2348-6139.151298

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  Abstract 

Aim: The aim of this study was to analyze the multi-center data of healthcare associated infections (HAIs) to assess the infection control (IC) scenario in India in context with Center for Disease Control (CDC)/National Healthcare Safety Network (NHSN) and International Nosocomial Infection Control Consortium (INICC) database.
Materials and Methods: Four National Accreditation Board for Hospitals accredited hospitals contributed their raw data on HAIs-number of days and number of infections in all intensive care patients were obtained as per the CDC-NHSN definitions and formulae. Three major device related infections were considered for analysis based on the prevalence of HAIs and discussions with subject experts. All nodal champions from each hospital were trained and common data collection sheet for surveillance in accordance to CDC-NHSN was formed. The pooled means for HAI rates and average of the pooled means for all were calculated using data from them and compared with CDC/NHSN and INICC percentiles.
Results: The Indian pooled mean HAI rates for all infections were above CDC/NHSN percentile threshold but below INICC percentile. Ventilator associated pneumonia was considered as a matter of prime concern, crossing P90 line of CDC/NHSN threshold. However, no HAI rate was in the limit of P25.
Conclusion: Indian HAI rates were higher when mapped with CDC threshold. This suggests the requirement for more standardized and evidence based protocols to tackle the HAIs with an aim to achieve the benchmark within CDC/NHSN thresholds. However, the 4 hospitals have better HAI rates as compared to pooled INICC database.

Keywords: Healthcare associated infection, multi-centric, patient data analysis, retrospective


How to cite this article:
Singh S, Chakravarthy M, Sengupta S, Munshi N, Jose T, Chaya V. Analysis of a multi-centric pooled healthcare associated infection data from India: New insights. J Nat Accred Board Hosp Healthcare Providers 2014;1:39-43

How to cite this URL:
Singh S, Chakravarthy M, Sengupta S, Munshi N, Jose T, Chaya V. Analysis of a multi-centric pooled healthcare associated infection data from India: New insights. J Nat Accred Board Hosp Healthcare Providers [serial online] 2014 [cited 2019 May 23];1:39-43. Available from: http://www.nabh.ind.in/text.asp?2014/1/2/39/151298


  Introduction Top


Healthcare associated infections (HAIs) are recognized as a major burden to patients, society and healthcare management. In 2008, European Center for Disease Prevention and Control estimated that more than four million people acquire HAIs annually in European Union alone of which approximately 37,000 die due to them. [1] In the USA, the incidence of HAIs has been estimated to be approximately 2 million cases annually, with an approximate mortality of 99,000 making it as fifth leading cause of death in acute care hospitals. [2] The prevalence of HAIs in developing countries could get as high as 30-50% [3] of infection rate . In developing countries, there have been concerns on very low compliance by healthcare professionals [4] leading to breach in practices.

There is an updated report of data on device associated (DA) HAIs within Intensive Care Units (ICUs) collected by hospitals participating in the International Nosocomial Infection Control Consortium (INICC), [5],[6] which demonstrate huge efforts needs to be put in by healthcare workers toward infection prevention and control (IPC). In US, Centre for Disease Control (CDC) runs a multi-centric, HAI control program with a surveillance system, which is known as US National Healthcare Safety Network (NHSN) [7],[8] where each healthcare organization compares themselves with the benchmark figures and develop their integrative practices to achieve better results.

Quality and patient safety are integral components for the effective healthcare delivery system. HAIs are a major issue jeopardizing patient safety with substantial impact on morbidity, mortality and use of additional resources. At hospitals in low and middle income countries, it makes logistic sense to understand the primary needs and obstacles for prevention and control of HAIs. The main issues in resource limited settings are lack of specific priorities, absence of data, healthcare safety both for the cared, and the care-giver are low on priority and failure to implement the standardized practices. Analysis of surveillance data in accordance with NHSN format and comparing them with benchmarked INICC or NHSN data will help us comprehend the gaps, thereby strategizing and operationalizing good prevention IPC practices.

In this study, our aim was to analyze the Indian data with NHSN and INICC in order to benchmark. This might provide information on HAIs in India and help us strategize the HAI control policy at institutional and national levels."


  Materials and Methods Top


Institutional permission and study settings

This study was conducted with permission from institutional review board. As no direct patient data were utilized in the study, ethical clearance was waived.

Confidentiality considerations

Being data of national importance, the participant institutions had requested to maintain anonymity for their names. Thus, in this study, the institutes were coded as "institute A", "institute B", "institute C" and so on throughout the project.

Study design

This was prospective, multi-center, observational analytical study. Our primary objectives included calculation of proportion rates for HAIs (device related) from January 2010 to December 2012 and compared with that of CDC-NHSN [7],[8] and INICC [5],[6] database.

Sampling method and data collection

The participating hospitals mutually agreed to collect prospective HAI data and analyze them. Nodal officers from each healthcare organization were trained in accordance with CDC-NHSN definitions and formula (numerator and denominator), in turn, they trained their IC team. Each participating hospital submitted their intensive care DA-HAI data prospectively. Data of three device related HAIs viz. ventilator associated pneumonia (VAP), [9] central line associated blood stream infections (CLABSI) [10] and catheter associated urinary tract infections (CAUTI) [11] collected and analyzed. Nondisclosure agreement was signed by the institutional champions to maintain the confidentiality. To protect the privacy, the hospitals coded as A, B, C and D. Data were collected for a period of 2 years up to December 2012.

Data analysis

  • Calculation of pooled means:

    Calculation of pooled means for each of three types of HAI rates - VAP, CLABSI and CAUTI were performed using the following formulas as mentioned by CDC-NHSN.
  • Percentile calculation:

    To explore the threshold value for HAIs to understand and improve hospital IC measures' quality, 25%, 50%, 75% and 90% percentile ranges were calculated for all three types of device related infections based on the hospital infections data using "percentile" built-in function in MS-EXCEL (Microsoft) software.
  • Comparative analysis in the context with CDC/NHSN and INICC threshold
  • Tabular method:

    Similar table was prepared to that reflected in CDC/NHSN guidelines to investigate and understand the difference between pooled means and percentiles of Indian HAI rates and CDC/NHSN and INICC based HAI rates.
  • Graphical method:

    Hospital-wise pooled means of HAI rates were plotted against CDC/NHSN and INICC thresholds based on percentiles for each type of HAI to investigate whether IC rates in study hospitals are within CDC/NHSN and INICC recommended limits. For ease of interpretation, hospital wise means were further averaged and plotted together.



  Results Top


The pooled DA HAIs data from the ICUs of the participating hospitals during the 2-year period were: 57,807 ventilator days, 155,614 central line days and 376,585 urinary catheter days [Table 1]. Pooled mean HAI rates were highest for VAP; (6.74/1000 ventilator days) the next was CLABSI (2.42/1000 central line days), followed by CAUTI (1.63/1000 urinary catheter days) [Table 1].
Table 1: Pooled means of device associated infections (pooled data from all 4 hospitals)

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Pooled Indian ICUs data revealed VAP rate of 6.74/1000 ventilator days, in contrast to CDC-NHSN of 1.43 and INICC of 19.5. Pooled Indian CLABSI rate was 2.40/1000 central line days in contrast to CDC-NHSN 1.02 and INICC 6.12, while pooled Indian CAUTI data was significantly better than the benchmark figure of CDC-NHSN at 2.09 and INICC at 6.5 [Figure 1].
Figure 1: Comparison of Pooled means of HAI rates for India, CDC/NHSN and INICC

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Ventilator associated pneumonia rate appeared to be close to75 th percentile (P75) for CDC-NHSN data and 50 th percentile (P50) for INICC data [Figure 2] and [Figure 3]. CLABSI rates at 2.40 appeared to be close to 90 th percentile (P90) of CDC-NHSN and 25 th percentile (P25) of INICC data [Figure 4] and [Figure 5]. Pooled Indian CAUTI rate at 1.63 appeared to compare well with P50 and P25 of CDC-NHSN and P75 of INICC data [Figure 6] and [Figure 7].
Figure 2: Mapping of pooled VAP incidence rates of study hospitals with CDC/NHSN thresholds

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Figure 3: Mapping of pooled VAP incidence rates of study hospitals with INICC thresholds

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Figure 4: Mapping of pooled CLABSI incidence rates of study hospitals with CDC/NHSN thresholds

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Figure 5: Mapping of pooled CLABSI incidence rates of study hospitals with INICC thresholds

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Figure 6: Mapping of pooled CAUTI incidence rates of study hospitals with CDC/NHSN thresholds

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Figure 7: Mapping of pooled CAUTI incidence rates of study hospitals with INICC thresholds

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  Discussion Top


Centre for Disease Control/National Healthcare Safety Network [8] HAI rates are globally considered bench mark of hospital infection, that is used in developing HAI prevention policies by other countries. Therefore, the bench mark figure of CDC/NHSN was considered as baseline for the comparative analysis in the study also. INICC [5] data for HAI rates are also used for comparison by developing and underdeveloped countries because the it represents the data from such countries. Primary data were collected using vetted data extraction proforma from the hospitals. There is no standard national or state guidelines/policy on HAI prevention and control available though some of the good institutions have developed standardized policies and protocols. Although it is fact that INICC guidelines are evidence-based and could be utilized by developing countries for better care delivery, the contextualized implementation still remains the biggest challenge.

As evident from our study, pooled incidence rates for VAP in India are beyond 90 th percentile (P90) threshold of CDC/NHSN VAP rates and was, therefore, a matter of prime concern. The lower threshold limit for CAUTI was probably because of lower rates compared with CLABSI and VAP. Nevertheless, holistically, there appears to be an urgent need for implementation of evidence based HAI control policy similar to CDC/NHSN and strategies to effectively implement them in India. However, in comparison to INICC data, study hospitals showed remarkably better performance with pooled means of HAI rates lying below threshold lines of 25 th percentile (P25). Surprisingly, VAP control was significantly better in our study findings in the context with INICC [6] thresholds with even 25 th percentile limit (P25) unlike the CDC threshold with pooled rate crossing set limit. There could be a possibility that demography and prevalent hospital care delivery system in developed countries are different than those of developing countries, which results in better IC policies and implementation measures in US as compared to the developing countries. Therefore, it is recommended to cautiously interpret the evidence, especially when mapping national quality indicators with established threshold of developed nations likewise in the present study, before any priority-setting and policy decisions.

The main limitation of our study was a small number of study hospitals with not a true representation of hospitals in Indian context were enrolled. Therefore, the average data could not be considered robust enough for informed decision making. The causative factors tend to change according to regions in the country. Since the collected data does not represent comprehensive representation, the future scope of such projects should ne envisioned with larger sample size and more realistic population representation. The data represented in CDC/NHSN guidelines is categorized by specialty care area and their infection rates, and we should aim of specialty wise data collection and analysis.

Being an exploratory research design, the Indian percentile values at this point of time cannot be considered as national threshold, for formulating guidelines of HAI prevention and control policies. However, the findings of this study suggest that the existence of evidence based guidelines results in better IC. Thus, they could be essentially utilized to suggest to the decision makers to structure a stronger environment for HAI control in India. The study can be considered as a pilot project for designing larger epidemiological studies encompassing more indicators and participants from a wider range of healthcare setups from across the country. That would not only be more representative, but also help in enhancing regional HAI trends leading to creating stronger and up-to-date database. That may become a surrogate to formulate public health policies for effective prevention and containment of HAIs and rising antimicrobial resistance.


  Acknowledgment Top


Children's Heart Link, US supported the project of multi-centric pooled HAI data collection and analysis.

 
  References Top

1.
European Center for Disease Prevention and Control. Annual Epidemiological Report on Communicable Diseases in Europe 2008. Stockholm: ECDC; 2008.  Back to cited text no. 1
    
2.
Centers for Disease Control. Public health focus: Surveillance, prevention, and control of nosocomial infections. MMWR Morb Mortal Wkly Rep 1992;41:783-7.  Back to cited text no. 2
    
3.
Habibi S, Wig N, Agarwal S, Sharma SK, Lodha R, Pandey RM, et al. Epidemiology of nosocomial infections in medicine intensive care unit at a tertiary care hospital in northern India. Trop Doct 2008;38:233-5.  Back to cited text no. 3
    
4.
Kotwal A, Taneja D. Health care workers and universal precautions: Perceptions and determinants of non-compliance. Indian J Community Med 2010;35:526-8.  Back to cited text no. 4
[PUBMED]  Medknow Journal  
5.
Rosenthal VD, Maki DG, Mehta A, Alvarez-Moreno C, Leblebicioglu H, Higuera F, et al. International nosocomial infection control consortium report, data summary for 2002-2007, issued January 2008. Am J Infect Control 2008;36:627-37.  Back to cited text no. 5
    
6.
Mehta A, Rosenthal VD, Mehta Y, Chakravarthy M, Todi SK, Sen N, et al. Device-associated nosocomial infection rates in intensive care units of seven Indian cities. Findings of the International Nosocomial Infection Control Consortium (INICC). J Hosp Infect 2007;67:168-74.  Back to cited text no. 6
    
7.
Edwards JR, Peterson KD, Andrus ML, Dudeck MA, Pollock DA, Horan TC, et al. National Healthcare Safety Network (NHSN) Report, data summary for 2006 through 2007, issued November 2008. Am J Infect Control 2008;36:609-26.  Back to cited text no. 7
    
8.
Emori TG, Culver DH, Horan TC, Jarvis WR, White JW, Olison DR, et al. National Nosocomial Infection Surveillance System (NNIS): Description of surveillance methods. Am J Infect Control 1991;19:19-35.  Back to cited text no. 8
    
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Rosenthal VD, Guzman S, Crnich C. Impact of an infection control program on rates of ventilator-associated pneumonia in intensive care units in 2 Argentinean hospitals. Am J Infect Control 2006;34:58-63.  Back to cited text no. 9
    
10.
Higuera F, Rosenthal VD, Duarte P, Ruiz J, Franco G, Safdar N. The effect of process control on the incidence of central venous catheter-associated bloodstream infections and mortality in intensive care units in Mexico. Crit Care Med 2005;33:2022-7.  Back to cited text no. 10
    
11.
Rosenthal VD, Guzman S, Safdar N. Effect of education and performance feedback on rates of catheter-associated urinary tract infection in intensive care units in Argentina. Infect Control Hosp Epidemiol 2004;25:47-50.  Back to cited text no. 11
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
 
 
    Tables

  [Table 1]


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[Pubmed] | [DOI]



 

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