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TitleAntimicrobial Resistance - Beyond the Breakpoint - J. Weber (Karger, 2010) WW
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Page 2

Antimicrobial Resistance – Beyond the Breakpoint

Page 92

Appropriate Antimicrobial Drug Use 81

appropriate agent (6 studies addressed both issues). The authors concluded that the

methodologic quality of the included studies was generally fair. Most trials were clas-

sified as RCTs, but often failed to describe the theoretical basis for interventions. The

investigators reported the following conclusions from this analysis:

1 Quality improvement strategies are moderately effective at reducing the inappro-

priate prescribing of antimicrobials and improving the appropriate selection of

antimicrobials. The median absolute reduction in antimicrobial use was only 8.9%

(interquartile range 6.7–12.4%) in the reviewed studies. For studies targeting selec-

tion of the appropriate agent, the median absolute reduction was 10.6% (inter-

quartile range 3.4–18.2%).

2 Although no single quality improvement strategy is clearly superior, active clini-

cian education may be more effective in certain settings. There was no single inter-

vention or group of interventions that was highly effective. Active educational

interventions appeared to be more effective in studies focusing on the decision to

prescribe antimicrobials, but the difference was not significant. Surprisingly, clini-

cian education alone appeared to be less effective than clinician education plus

audit and feedback of prescribing behavior for those studies that focused on selec-

tion of the appropriate drug.

3 Interventions targeting prescribing for all acute respiratory tract infections may

exert a greater effect on overall prescribing than interventions targeting specific

types of acute respiratory infections. The authors extrapolated antimicrobial

prescribing data to the population level when possible for each study, and they

found that interventions focused on all ARIs, rather than specific diagnoses,

had the greatest potential impact on antimicrobial use. Interventions focused on

particular diagnoses (such as sinusitis or pharyngitis) tended to have greater

effect sizes at the individual level, but the population-level effects were more


4 Study design and quality should be improved. Studies that formally evaluate the

cost effectiveness of interventions to improve antimicrobial treatment and selec-

tion are needed, and studies should evaluate the potential harm of such interven-

tions. A substantial number of studies suffered from methodologic limitations,

such as lack of randomization and failure to document whether the educational

interventions were received by the participants. Multifaceted intervention studies

generally require analysis at the level of the clinic, population, or geographic area

(such as a city or county). Randomized studies at the clinic level are feasible and

have proven useful for evaluation of antimicrobial prescribing interventions.

However, randomization is often impossible at the level of communities, counties,

or other large geographic areas due to cost considerations and limited number of

units available for allocation. In addition, delivery of interventions is more com-

plex and difficult to measure in larger populations. As a result, non-randomized

studies have predominated despite the methodologic superiority of group random-

ized trials.

Page 93

82 Belongia · Mangione-Smith · Knobloch

Doctor-Patient Communication and Its Influence on Antimicrobial Prescribing

When physicians perceive that a patient or parent expects an antimicrobial they are

significantly more likely to inappropriately prescribe [12, 16, 52–57]. However, physi-

cian perceptions are poorly correlated with actual patient or parent expectations for

antimicrobials [16, 53–55, 58, 59]. Although 50–70% of patients and parents expect to

receive antimicrobials when they attend visits for ARI, only 1–6% make direct verbal

requests for them [58, 60]. Even when no direct requests for antimicrobials are made,

physicians still perceive an expectation 34% of the time [58]. If miscommunication

about expectations could be avoided, much inappropriate antimicrobial prescribing

could potentially be prevented.

In pediatrics, physician perceptions are largely predicted by various indirect par-

ent communication behaviors that occur during visits for ARI [57]. Through a series

of qualitative studies, Stivers [61–63] identified 3 parent communication practices

that appeared to be related to physician perceptions that parents expected antimi-

crobials. These were the parent suggesting a candidate diagnosis early in the visit,

resisting the physician’s diagnosis in viral cases, and resisting the physician’s non-

antimicrobial treatment plans [61–63]. Presenting a candidate diagnosis involves

the parent suggesting their child has a diagnosis where antimicrobials are commonly

prescribed, for example ‘I think he’s got sinusitis again,’ rather then just listing their

child’s symptoms, ‘She has a cough and a runny nose’. Diagnosis resistance occurs

when the parent questions the physician’s diagnosis. Treatment resistance is when

the parent questions the physician’s treatment plan. Confirming what Stivers hypoth-

esized based on qualitative analyses, a recent quantitative study showed that parents

who use candidate diagnoses are significantly more likely to expect antimicrobials

(27% increase) and be perceived as expecting them (9% increase) [64]. Surprisingly,

parents who expect antimicrobials are no more likely to question their child’s phy-

sician about non-antimicrobial treatment plans than parents without expectations.

Whether parents expect antimicrobials or not, they are significantly more likely to be

perceived as expecting antimicrobials (20% increase) when they question the physi-

cian’s treatment plan [64]. These findings may explain some of the gap between actual

and perceived expectations.

Parent questioning of non-antimicrobial treatment plans is largely determined by

how physicians present these plans to them. Stivers identified 2 main ways that physi-

cians present non-antimicrobial treatment plans during visits for ARI: positively for-

matted treatment plans (e.g. ‘You can try running a humidifier in her room at night

to settle the cough down’) and negatively formatted or ‘rule-out’ treatment plans (e.g.

‘An antibiotic isn’t going to touch this thing’) [63]. When physicians use negatively

formatted treatment plans and rule-out the need for antimicrobials, parents are sig-

nificantly more likely to question the plan (24% increase) [64]. Thus, focusing treat-

ment plans on what parents can do to make their child feel better rather then on why

antimicrobials are unnecessary decreases parent questioning of treatment plans and

Page 184

Subject Index 173

drivers 43–46

implications 41–43

mechanisms 37

prevalence by pathogen 38–41

Fungal infection, see Antifungal resistance

Gowns, infection control 93, 94

Hand hygiene, infection control 92, 93

Health care facilities

antibiotic control

duration 91, 92

rotation 92

starting 90, 91

economic impact of resistance, see Costs,

antimicrobial resistance

infection control


disinfectants 95, 96

environment 94, 95

gowns 93, 94

hand hygiene 92, 93

patient cleansing 94

patient surveillance and isolation


potable water 96

rapid detection 98

infection prevention

device-related infection 98, 99

host defense 99

prospects for antimicrobial resistance

control 99, 100

Helminths, see Parasitic disease

Hookworm, anthelmintic resistance 133

Human immunodeficiency virus

antiretroviral treatment

drug classes and treatment strategies in

developing countries 157, 158

scale-up in developing countries 154–


drug resistance

development of strains 156, 157

factors in developing countries 158–160

mother-to-child transmission prevention

studies 161, 162

transmission 162–164

Influenza, see Community-acquired


Klebsiella, fluoroquinolone resistance 40, 41


extended-spectrum β-lactamases

detection 27, 28

healthcare-associated producer

colonization and infection risk

factors 24, 25

hospital-acquired producer colonization

and infection risk factors 24

host range and prevalence 22, 23

infection control of producers 25–27

overview 22

treatment for producers 28, 29

third-generation cephalosporins as

substrates 21, 22

types 22

Linezolid, methicillin-resistant Staphylococcus

aureus management 12, 13

Methicillin-resistant Staphylococcus aureus

community-associated infection

clinical manifestations 5

definition 2

diagnosis 9, 10

healthcare-associated infection

comparison 2, 5

incidence trends 3

molecular epidemiology 5, 6

pathogenesis 6–9

prevention 14, 15

risk factors 3, 4, 7

SCCmec element 6

transmission 4

treatment 10–14

virulence factors 9

economic impact 103–109

healthcare-associated infection 2, 5

Minimum inhibitory concentration,

interpretation in antifungal resistance 145,


Parasitic disease

anthelmintic resistance

definition 121

detection 121, 122

factors affecting spread 128–130

filarial parasite resistance

development 127

historical perspective 122

hookworm 133

ligand-gated ion channel-targeting

drugs 126, 127

Page 185

174 Subject Index

Parasitic disease (continued)

mathematical modeling 130–132

prevention prospects 134, 135

tubulin-targeting drugs 127

anthelmintic targets

ligand-gated ion channels 125, 126

metabolism targets 126

tubulin 126

mass treatment

elimination programs 124, 125

morbidity control 122–124

Pneumonia, see Community-acquired


Prescribing inpatient antimicrobials

antibiotic control

duration 91, 9

rotation 92

starting 90, 91

resistance economic impact 89, 102–117

Prescribing outpatient antimicrobials

acute respiratory tract infections 71

Agency for Healthcare Research and

Quality improvement strategies 80, 81

clinical decision support for

improvement 78, 79

doctor-patient communication 82–85

health plan performance measures for

improvement 79, 80

intervention studies 73–78

market forces 72

prospects for study 85

resistance threat 72

trends 72, 73

Pseudomonas aeruginosa, economic impact of

multidrug-resistant pathogens 112

Quinupristin, methicillin-resistant

Staphylococcus aureus management 13

Rifampin, methicillin-resistant Staphylococcus

aureus management 13

Salmonella enterica, fluoroquinolone

resistance 38–40

SCCmec element, methicillin-resistant

Staphylococcus aureus 6

Staphylococcus aureus, see also Community-

acquired pneumonia, Methicillin-resistant

Staphylococcus aureus

overview of infection 1, 2

Streptocococcus pneumoniae, see Community-

acquired pneumonia

Tetracycline, methicillin-resistant

Staphylococcus aureus management 11, 12

Trimethoprim-sulfamethoxazole, methicillin-

resistant Staphylococcus aureus

management 10, 11

Vancomycin, methicillin-resistant

Staphylococcus aureus management 10, 11

Vancomycin-resistant enterococci, economic

impact 109–111

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