NORMALIZATION OF USE DATA
4. NORMALIZATION OF USE DATA
It is common practice to normalize aggregate antimicrobial use data to account for differences in census within an institution or geographic area. This is usually accomplished by correcting the use data so that it reflects a rate of use per unit of time, for example, 1,000 days. This day term is often referred to as patient-days since one calculates this denominator by multiplying the num- ber of patients within the institution in a given period of time by the length of hospitalization (in days) and correcting it to a value such as 1,000. Patient-days are also referred to as occupied bed-days. Correction for changes in the popu- lation in this manner allows one to see “true” changes in rates of antimicrobial use rather than fluctuations that may simply reflect changes in the population over time or differences in two or more populations. Although it is always use- ful to normalize the data to detect the true rate of use, normalization is of little value when small changes in census occur. For example, census changes in smaller patient-care areas may be very important whereas institution-wide fluctuations in census may be minimal; thus, normalization may have only a minimal impact (Figure 3). Obviously, if there are no changes in the number of patient-days over time, non-normalized measures (e.g., grams) and the nor- malized measures (e.g., grams/patient-day) would perfectly correlate. These calculations mathematically spread the use over the entire population rather than the population that actually received the antimicrobial of interest. Thus normalized values such as grams per patient-day will not reflect average grams per day doses of an antimicrobial.
The most common denominator for normalizing antimicrobial use within an institution in the United States is 1,000 patient-days. In Europe, the European Surveillance of Antimicrobial Consumption (www.ua.ac.be/main.asp?c⫽*ESAC) recommends use of bed-days, which are calculated by multiplying the number of beds by the occupancy by the length of time of the study. The denominator for use in primary healthcare settings and thus, within a geographic region, is usually inhabitant-days per unit of time, which is calculated by multiplying the number of inhabitants in an area by the number of days studied. For exam- ple, 7 DDD/inhabitant/year is equivalent to each inhabitant of an area receiv- ing a 7-day course of that antimicrobial during a 1-year period. If certain
Relation of Antibiotic Consumption to Resistance
MUSC Ceftazidime Use (Annual Data 1992–2000)
0.05 y = 1E-05x - 0.0002 R 2 = 0.9722 0.04 0.04
Grams/patient-day 0.03 0.03
Figure 3. Example of the impact of small changes in patient-days when normalizing grams of use data (White, 2002).
antimicrobials are used only in patients of specific ages, one can utilize the number of inhabitants in that age range when those data are available. The number of admissions or discharges can also be used to estimate the percent- age of patients exposed to antimicrobials. Other factors that have been used to calculate rates of use are use per number of beds or occupied beds. However, factors that fail to take length of stay into account are probably less useful; thus patient-days, bed-days, and inhabitant-days are preferred. When patient- specific antimicrobial use data are available, there is no need to normalize the data since the antimicrobial exposure reflects only the patients who received antimicrobials.
Parts
» Antibiotic Policies: Theory and Practice
» THEORIES OF FACILITATING CHANGE
» OTHER APPROACHES TO GUIDELINE DEVELOPMENT
» QUALITY ASSURANCE AND DEVELOPMENT OF STANDARDS
» DEVELOPMENT OF CLINICAL STANDARDS IN SCOTLAND
» WHICH QI INTERVENTIONS HAVE BEEN STUDIED IN CAP?
» LINKING PROCESS OF CARE TO OUTCOMES IN QI
» WHAT IS THE EVIDENCE THAT QI INITIATIVES IMPROVE PROCESS OF CARE IN CAP?
» WHAT IS THE EVIDENCE THAT QI INITIATIVES IMPROVE OUTCOMES IN CAP?
» Designing and implementing a CAP intervention
» LEVEL OF AGGREGATION OF ANTIMICROBIALS
» ANTIMICROBIAL USAGE MEASURES
» Relationships based on patient-specific data
» Relationships based on aggregate usage
» ANTIBIOTIC CONSUMPTION; ALTERNATIVE UNITS OF MEASUREMENT
» ANTIBIOTIC CONSUMPTION CALCULATOR
» BENCHMARKING FOR REDUCING VANCOMYCIN USE AND VANCOMYCIN- RESISTANT ENTEROCOCCI IN US ICU S
» THE HARVARD EMERGENCY DEPARTMENT QUALITY STUDY
» ANALYSIS BY INDIVIDUAL ANTIMICROBIAL AGENT
» BENCHMARKING WITH OTHER ANTIMICROBIAL UTILISATION DATA
» STATE OF THE ART OF ANTIBIOTIC PROPHYLAXIS IN SURGERY
» AUDITING AND IMPROVING THE QUALITY OF ANTIBIOTIC PROPHYLAXIS IN SURGERY
» TYPES OF STUDIES TO OBTAIN QUALITY DATA ON A PATIENT LEVEL
» MULTIDISCIPLINARY ANTIMICROBIAL MANAGEMENT TEAMS
» THE ROLE OF THE PHARMACIST IN INFECTION MANAGEMENT
» TRAINING AND SUPPORT IN INFECTION MANAGEMENT FOR PHARMACISTS
» ANTIBIOTIC POLICY IN THE TERTIARY CARE CENTRE
» ANTIBIOTIC CONSUMPTION IN ICU S
» ANTIBIOTIC RESISTANCE IN ICU
» The impact of antibiotic policies and antibiotic consumption on antibiotic resistance
» IT and benchmarking to improve antibiotic prescribing
» COST OF HOSPITAL-ACQUIRED INFECTION
» THE COST OF ANTIMICROBIAL RESISTANCE
» Costs of screening/surveillance cultures
» Isolation, cohorting, and contact isolation
» EPIDEMIOLOGY OF INVASIVE FUNGAL INFECTIONS
» Antifungal resistance in Candida species
» Antifungal resistance cannot be transmitted by extrachromosomal DNA
» RATIONAL USE OF ANTIFUNGAL AGENTS
» THE CHANGING FACE OF VIRAL INFECTIONS AND THEIR MANAGEMENT
» PROBLEMS ASSOCIATED WITH ANTIVIRAL THERAPY
» ANTIVIRAL TREATMENT STRATEGIES
» ANTIVIRAL PROPHYLAXIS STRATEGIES
» ANTIBIOTIC CONCENTRATIONS AT TARGET SITES
» An infant with aplastic anaemia
» A long-standing E. coli infection of liver cysts
» BREAKPOINTS: A SHORT HISTORY AND OVERVIEW
» PHARMACODYNAMIC RELATIONSHIPS AND EMERGENCE OF RESISTANCE
» EVALUATION OF THE ANTIMICROBIAL RESISTANCE SURVEILLANCE DATA PUBLISHED IN THE MEDICAL LITERATURE
» PRACTICAL ASPECTS OF THE IMPLEMENTATION OF THE SURVEILLANCE PROGRAM
» Multivariate analysis methods
» Evolutionary genetic approaches
» Study of the relationship between bacterial resistance and antimicrobial consumption
» To predict the short-term evolution of resistance
» To evaluate interventions to control antibiotic resistance
» DISINFECTANTS: TYPES, ACTIONS, AND USAGES
» Evidence of bacterial resistance to biocides
» Mechanisms of bacterial resistance to biocides
» EVIDENCE OF CROSS-RESISTANCE BETWEEN BIOCIDES AND ANTIBIOTICS
» DISINFECTANT USAGE AND ANTIBIOTIC RESISTANCE
» METHODS OF LITERATURE REVIEW
» PROBLEMS WITH INTERPRETATION OF PUBLISHED STUDIES
» Distribution of educational materials
» Audit and feedback with or without other educational materials
» Educational group meetings or seminars
» Educational outreach/academic detailing
» Financial/healthcare system changes
» EFFECT OF INTERVENTIONS ON ANTIBIOTIC RESISTANCE
» DDD/1,000 INHABITANTS AND DAY (DID)
» PRESCRIPTIONS/1,000 INHABITANTS AND YEAR
» INDICATIONS FOR ANTIBIOTIC PRESCRIPTIONS
» POSSIBLE CAUSES FOR OBSERVED VARIATIONS IN ANTIBIOTIC USE
» DETERMINANTS OF ANTIBIOTIC CONSUMPTION
» COLLECTIVE AWAKENING AND PROGRESSIVE MOBILIZATION OF FRENCH PUBLIC HEALTH AUTHORITIES
» ANTIBIOTIC USE AND COST TRENDS
» IMPACT ON HEALTH BUDGETS OF ANTIBIOTIC USE
» ACCESS TO ESSENTIAL ANTIBIOTICS AT ALL LEVELS OF CARE
» EPIDEMIOLOGY OF ANTIMICROBIAL RESISTANCE
» THREAT OF ANTIMICROBIAL RESISTANCE
» ECONOMIC IMPLICATIONS OF ANTIMICROBIAL RESISTANCE
» FACTORS CONTRIBUTING TO DEVELOPMENT AND SPREAD OF RESISTANCE
» STRATEGIES FOR CONTAINMENT OF RESISTANCE IN DEVELOPING COUNTRIES
» Antibacterial resistance and policies
» Policies, guidelines, and education on antibacterial use
» Discovery, development, and commercialization in the face of policies
» Antibacterial development, labelling, and benefits
» WHAT CAN BE DONE NOW ABOUT ANTIBIOTIC RESISTANCE?
» HOW CAN THE DIAGNOSTIC LABORATORY HELP?
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