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Quality Improvement Strategies for Antibiotic Prescribing

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Majority of prescriptions in US are for acute respiratory infections (ARI's) ... Prescriptions for recommended/nonrecommended ABX per person ... – PowerPoint PPT presentation

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Title: Quality Improvement Strategies for Antibiotic Prescribing


1
Quality Improvement Strategies for Antibiotic
Prescribing
  • Sumant Ranji, M.D.
  • February 16, 2005

2
Closing the Quality Gap
  • Based on subject areas identified in 2003 IOM
    report, Transforming Health Care Quality
  • Identifying activities that increase the rate of
    use of practices that are known to be effective
  • Synthesis of QI strategies across diseases and
    topic areas

3
Definitions
  • Quality Gap difference between observed
    processes/outcomes and those achievable based on
    current knowledge
  • Due to deficiency that could be addressed by
    health care system
  • Quality improvement strategy any intervention
    aimed at reducing the quality gap for
    representative patients
  • Should attempt to improve care for broad group of
    patients
  • May involve patient-, provider-, or system-level
    changes
  • Quality improvement target outcome/process/struc
    ture the strategy is intended to influence

4
Antibiotic prescribing background
  • Majority of prescriptions in US are for acute
    respiratory infections (ARIs)
  • 41 million prescriptions in 1998
  • 55 of prescriptions for ARIs are unnecessary
  • Successes and failures during 1990s
  • Significant decline in prescribing overall
  • Especially among children
  • Inappropriate script rate still 40 for common
    conditions
  • Marked increase in use of broad-spectrum agents
  • Quinolones, macrolides, 2nd/3rd gen
    cephalosporins, others

5
Quality of prescribing
  • Quality Gap
  • Unnecessary prescribing of antibiotics for
    non-bacterial illnesses
  • Unnecessary use of broad-spectrum antibiotics
    where narrow-spectrum agents are effective
  • Quality improvement target
  • Rate of antibiotic prescribing for non-bacterial
    diseases
  • Frequency of use of broad-spectrum agents

6
Quality Improvement Strategies
  • Provider Education
  • Audit and Feedback
  • Provider Reminders
  • Facilitated relay of clinical data to providers
  • Patient Education
  • Patient self-management
  • Patient reminders
  • Organizational change
  • Financial and regulatory incentives

7
Theoretical Framework for ABX prescribing
Clinician Factors -sociodemographics -training/spe
cialty -knowledge -judgment and
heuristics -perceived patient expectations
Patient Factors -sociodemographics -past
experiences -expressed expectations -reported
symptoms -illness severity
System Factors -practice setting -health plan
features -visit and pharmacy copay -patient
enabling systems -formularies/restrictions -pharma
ceutical detailing
Clinician's Decision to Prescribe Antibiotics
8
Quality Improvement StrategiesPrescribing
specific
  • Patient-directed
  • Education
  • Self-management (delayed prescriptions)
  • Financial and regulatory incentives
  • Copayments
  • Providers
  • Education by different modalities
  • Audit and feedback of prescribing practices
  • Financial and regulatory incentives
  • Capitation, restricted formularies
  • Combinations of above strategies

9
Research questions
  • Which QI strategies reduce antibiotic prescribing
    for acute non-bacterial illnesses?
  • Are particular QI strategies more effective for
    certain target conditions?
  • Are these strategies safe for patients?
  • Effects on health services utilization, clinical
    outcomes, satisfaction
  • What are the consequences of these strategies for
    public health and the health care system?
  • Effects on antimicrobial resistance, costs of
    prescribing
  • Which QI strategies are most effective in
    improving the selection of recommended
    antibiotics?
  • Subtopics as above

10
Inclusion/Exclusion Criteria
  • Topic
  • Acute outpatient illnesses
  • Major contributor to problem
  • Different theoretical model for inpatient
    prescribing
  • Study design
  • Evaluate a QI strategy
  • RCT, quasi-RCT, CBA, or ITS
  • Outcomes
  • Measurement of antibiotic prescribing (overall or
    selection)
  • Antimicrobial resistance, disease outcomes, costs
    of prescribing, health services utilization,
    satisfaction with care only abstracted if study
    also measured prescribing

11
A priori hypotheses
  • Publication bias
  • Smaller, non-randomized trials will have greater
    effects
  • Effect of baseline prescribing rate
  • Studies done in populations where
    over-prescribing/poor selection is common will
    have greater effects
  • Targeting of specific diseases
  • Studies targeting prescribing for specific
    diseases will be more effective than those
    targeting a variety of conditions or general ABX
    prescribing
  • Multifaceted strategies
  • As with prior research, studies using multiple QI
    strategies will be more effective than those
    using a single strategy
  • Intensity of intervention
  • Studies using interventions repeated over time
    will be more effective

12
Search Strategy
EPOC 537 citations
Hand Search 12 citations
549 citations
382 title exclusions
167 articles
93 full text exclusions
54 articles (74 comparisons)
ABX selection 25 articles (33 comparisons)
ABX prescription 34 articles (41 comparisons)
13
Analysis
  • Measured outcomes
  • ABX prescribing
  • visits at which patient received ABX
    prescription
  • Prescriptions per person-year
  • Prescriptions per provider
  • ABX selection
  • of total prescriptions written for recommended
    agent
  • compliance to clinical guideline for
    prescribing
  • Prescriptions for recommended/nonrecommended ABX
    per person
  • Prescriptions for recommended/nonrecommended ABX
    per provider

14
Analysis
  • Quantitative
  • N31 for ABX prescribing, N19 for ABX selection
  • Meta-regression planned but failed
  • Random effects meta-analysis
  • However, extreme heterogeneity (I2 90)
  • Median effects semi-quantitative analysis
  • Limitations no weighting by sample
    size/variance
  • Necessitates stratified analyses
  • Does allow preservation of natural study units
  • Qualitative
  • N10 for ABX prescribing, N14 for ABX selection
  • Systematic review format, complementary to above

15
Key Findings(a work in progress)
  • Overall effectiveness of QI strategies
  • Possible benefit of self-management
  • Variable methodologic quality of studies
  • No benefit from more intense interventions
  • Possible benefit of multifaceted strategies

16
Results studies suitable for quantitative
analysis
  • ABX prescription (N31)
  • 8 US, 23 non-US
  • 26 target prescribing for ARIs
  • 18 RCT, 13 CBA
  • ABX selection (N19)
  • 3 US, 16 non-US
  • 12 target choice for ARI, 7 for UTI
  • 7 RCT, 12 CBA

17
Study quality
  • Failure to properly document intervention
  • Rationale for study methodology not explained
  • Key study components described inadequately
  • Duration and intensity of intervention
  • Short follow-up
  • Minimal reporting of outcomes beyond prescribing
  • Failure to report key data
  • e.g. number of patients in study
  • Inappropriate statistical analyses
  • Unit of analysis errors
  • Lack of accounting for temporal trends in
    prescribing

18
Overall results
  • QI strategies overall beneficial
  • Prescribing Median reduction of 9.0 (IQR
    -16.6 to -3.4) in prescribing of ABX when not
    indicated
  • Selection Median increase in prescribing of
    recommended ABX of 13.8 (IQR 4.6 - 19.7)

19
Comparative effects on ABX prescribing
20
Comparative effects on ABX selection
21
Median effect on Prescribing Stratified by study
size and design
22
Publication Bias
  • Larger effects among smaller trials
  • Less effect of study design type

23
Baseline prescribing rate
  • Prescribing studies
  • Would expect that studies with high baseline
    prescribing rate may show larger effects
  • Not found in our sample, but skew issues
  • Selection studies
  • Expect baseline low compliance to correlate with
    higher effects
  • Also not demonstrated

24
Targeting of specific diseases
  • Hypothesize that studies targeting prescribing
    for specific conditions may be more likely to
    show effects
  • Not found in our analysis for either prescribing
    or selection studies
  • Confounding by sample size?

25
Multifaceted strategies
  • Previous work (DM) did not reveal benefit for
    multifaceted strategies
  • Possible benefit in prescribing studies
  • Median effect -12.0 (IQR -16.0 to -1.7) for
    multifaceted studies (N15)
  • Median effect -8.8 (IQR -16.9 to -5.9) for
    single-faceted studies (N16)
  • Selection studies single-faceted studies all
    provider education only no difference seen

26
Repeated Interventions
  • Complicated by poor description of study details
  • No difference found for either prescribing or
    selection studies

27
Other outcomes
  • Antimicrobial resistance
  • Only assessed in 2 studies both showed no
    effect, but short duration of followup
  • Health services utilization
  • Assessed in 6 studies in prescribing group
  • No increase in return visits, hospitalizations
    seen
  • ? Effect on duration of illness
  • Patient satisfaction
  • Assessed only in delayed prescribing studies
    (N6)
  • No significant effect seen
  • Costs of prescribing
  • Assessed in 7 studies 15-30 reductions seen,
    but in short-term (
  • Mostly non-US

28
Conclusions and Hypotheses
  • No clear benefit for any single QI strategy
  • Possible exception of patient self-management
    (delayed prescribing)
  • Poor quality of studies limits interpretation of
    results
  • However, overall trials are effective at reducing
    prescribing and improving selection
  • Future analyses
  • Stratified analyses effects of QI strategies in
    relation to sample size, baseline prescribing,
    study design, disease targeting, country
  • Preliminarily no major effects
  • Nonparametric (rank-sum) tests for differences
    between groups
  • Further attempts at meta-regression
  • Common outcome measure for dichotomous and
    continuous studies

29
Thanks
  • Stanford
  • Vandana Sundaram
  • Robyn Lewis
  • Kathy McDonald
  • Doug Owens
  • UCSF
  • Ralph Gonzales
  • Mike Steinman
  • Ottawa
  • Kaveh Shojania
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