Antibiotic overuse in the primary health
care setting: a secondary data analysis of
standardised patient studies from India,
China and Kenya
Giorgia Sulis
,1,2 Benjamin Daniels
,3 Ada Kwan
6,7
3,8
, Jishnu Das
, Madhukar Pai
Amrita Daftary
To cite: Sulis G, Daniels B,
Kwan A, et al. Antibiotic
overuse in the primary health
care setting: a secondary data
analysis of standardised patient
studies from India, China and
Kenya. BMJ Global Health
2020;5:e003393. doi:10.1136/
bmjgh-2020-003393
Handling editor Seye Abimbola
JD and MP contributed equally.
JD and MP are joint senior
authors.
Received 8 July 2020
Revised 1 August 2020
Accepted 3 August 2020
© Author(s) (or their
employer(s)) 2020. Re-use
permitted under CC BY.
Published by BMJ.
For numbered affiliations see
end of article.
Correspondence to
Dr Madhukar Pai;
madhukar.pai@mcgill.ca
ABSTRACT
Introduction Determining whether antibiotic prescriptions
are inappropriate requires knowledge of patients’
underlying conditions. In low-income and middle-income
countries (LMICs), where misdiagnoses are frequent, this is
challenging. Additionally, such details are often unavailable
for prescription audits. Recent studies using standardised
patients (SPs) offer a unique opportunity to generate
unbiased prevalence estimates of antibiotic overuse, as
the research design involves patients with predefined
conditions.
Methods Secondary analyses of data from nine SP
studies were performed to estimate the proportion of SP–
provider interactions resulting in inappropriate antibiotic
prescribing across primary care settings in three LMICs
(China, India and Kenya). In all studies, SPs portrayed
conditions for which antibiotics are unnecessary (watery
diarrhoea, presumptive tuberculosis (TB), angina and
asthma). We conducted descriptive analyses reporting
overall prevalence of antibiotic overprescribing by
healthcare sector, location, provider qualification and case.
The WHO Access–Watch–Reserve framework was used to
categorise antibiotics based on their potential for selecting
resistance. As richer data were available from India, we
examined factors associated with antibiotic overuse in that
country through hierarchical Poisson models.
Results Across health facilities, antibiotics were given
inappropriately in 2392/4798 (49.9%, 95% CI 40.8%
to 54.5%) interactions in India, 83/166 (50.0%, 95% CI
42.2% to 57.8%) in Kenya and 259/899 (28.8%, 95% CI
17.8% to 50.8%) in China. Prevalence ratios of antibiotic
overuse in India were significantly lower in urban versus
rural areas (adjusted prevalence ratio (aPR) 0.70, 95%
CI 0.52 to 0.96) and higher for qualified versus nonqualified providers (aPR 1.55, 95% CI 1.42 to 1.70), and
for presumptive TB cases versus other conditions (aPR
1.19, 95% CI 1.07 to 1.33). Access antibiotics were
predominantly used in Kenya (85%), but Watch antibiotics
(mainly quinolones and cephalosporins) were highly
prescribed in India (47.6%) and China (32.9%).
Conclusion Good-quality SP data indicate alarmingly
high levels of antibiotic overprescription for key conditions
across primary care settings in India, China and Kenya,
with broad-spectrum agents being excessively used in
India and China.
,4 Sumanth Gandra,5
1,2,9
INTRODUCTION
Antibiotic stewardship is critical for tackling
antimicrobial resistance (AMR), especially
in the context of the ongoing COVID-19
pandemic.1 In a recent systematic review on
antibiotic prescription practices in primary
care settings across low-income and middleincome countries (LMICs), we showed that
approximately 50% of patients of any age
seeking care for any reason received at least
one antibiotic.2
However,
determining
inappropriate
prescription in LMICs is a challenge, and
a standardised tool for its assessment is
currently unavailable. Inappropriate antibiotic prescribing can derive from a range of
failings: (1) prescription in the absence of
clinical indication (ie, ‘overprescription’),
which not only produces zero benefit to the
patient but can also be harmful (eg, drug
toxicities or costs for patients); (2) failure
to prescribe antibiotics when necessary
(ie, ‘underprescription’); (3) suboptimal
antibiotic choice with respect to aetiology
(confirmed or presumptive), site, severity
of infection and patient characteristics (eg,
age, comorbidities and pregnancy status); (4)
prescription of wrong dosage and/or duration of antibiotic treatment as compared with
national and international guidelines.3 4
Methods used to assess inappropriateness,
such as prescription audits, medical records
and patient exit interviews, have multiple
limitations.3 5 Electronic records are seldom
available in LMICs, particularly in primary
care, thus making accurate prescription audit
tools difficult to implement. Also, the paucity
and variation of clinical details that can be
captured through medical records (paperbased or not), if they even exist, makes it even
harder to determine the appropriateness
Sulis G, et al. BMJ Global Health 2020;5:e003393. doi:10.1136/bmjgh-2020-003393
1
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Original research
Key questions
What is already known?
► A recent systematic review and meta-analysis showed that, across
48 studies from 27 low-income and middle-income countries
including China, India and Kenya, approximately half of all patients evaluated in outpatient primary care received an antibiotic
prescription.
► Methods used to assess inappropriateness of antibiotic prescription, such as prescription audits, medical records and patient exit
interviews, have multiple limitations.
► Standardised patients (SPs) offer a unique opportunity to explore
prescribing practices and accurately estimate overprescription because case presentations are fixed by design, thus allowing comparisons across settings and providers.
What are the new findings?
► In this secondary analysis of data from nine SP studies carried out
in India, Kenya and China, we provide a more unbiased prevalence
estimate of antibiotic overprescription for selected clinical conditions (asthma, angina, watery diarrhoea, presumptive or confirmed
tuberculosis (TB)) across a range of primary healthcare providers.
► About 30% of SP–provider interactions in China and 50% of those
performed in India and Kenya resulted in inappropriate antibiotic
prescription.
► Watch antibiotics (ie, broad-spectrum agents with higher potential
for selecting resistance) were very commonly prescribed in India
(about 50%) and China (over 32%), and some patients (0.8%) even
received last-resort antibiotics belonging to the ‘Reserve’ group.
► In India, the average prevalence of antibiotic prescribing was 30%
lower in urban versus rural areas, 55% higher among qualified
providers compared with non-qualified ones and 19% higher for
patients presenting with presumptive TB versus other conditions.
What do the new findings imply?
► Our findings indicate alarming levels of antibiotic overprescription
for conditions that are frequently encountered in primary care, potentially leading to toxic effects and diagnostic delays.
► The choice of antibiotics given to patients is concerning, as several
agents with high potential for resistance selection are often inappropriately prescribed.
► The SP methodology could prove useful to further investigate antibiotic prescribing practices and its underlying determinants, using
other case presentations across a range of different contexts.
of prescription.3 Patient exit interviews are commonly
used alternatives but come with several major drawbacks
that can result in poor and inaccurate estimates that are
incomparable. Data collected in this manner are subject
to recall bias, poor recall and limited clinical expertise
among patients. Further, not only are clinical presentations highly heterogeneous but also the difficulty in actually determining what patients have makes comparisons
very challenging for research.
A less biased method is the use of standardised patients
(SPs), also known as ‘simulated’ or ‘mystery’ patients, that
is, healthy individuals recruited from local communities
and extensively trained to portray a standardised clinical
condition to a healthcare provider.5 Since their clinical
presentations are fixed by design, SPs offer an important
2
opportunity to overcome the methodological limitations
typical of other studies, thus making the assessment of
inappropriateness of antibiotic use less biased and more
accurate.5 Because the underlying illness is prespecified,
the SP methodology allows accurate assessment if an antibiotic is inappropriately prescribed. The SP approach is
not affected by poor recall, recall bias or the Hawthorne
effect, which is commonly observed in patient exit
interviews and direct observations of patient–provider
encounters.5
Considering the aforementioned advantages, we
performed a secondary analysis of prescription data
from previously conducted SP studies in three LMICs
(India, China and Kenya) with two objectives: (1) to estimate the overall proportion of SP–provider interactions
(separately for pharmacy-based and health facility-based
studies) that resulted in prescription or dispensing of at
least one antibiotic in the absence of clinical indication
(ie, overprescription) and (2) to identify factors associated with antibiotic overprescribing in health facilities.
METHODS
Study design and data sources
Data on SP–provider interactions (ie, completed SP visits
with a provider at a health facility or a pharmacy) from
studies conducted by members of our team (India and
Kenya) or had used SP cases developed by our team or
obtained from publicly accessible sources (China) were
gathered to compile a pooled dataset for secondary analyses.6–15 The methods used are described in our published
manual and toolkit on how to conduct SP studies.5
Among studies carried out in India, four involved
primary health facilities across five sites (Delhi, Mumbai,
Patna, three districts in the State of Madhya Pradesh,
and Birbhum district in the State of West Bengal),6–9
while two were performed in pharmacies located in four
different areas (Mumbai, Patna, Delhi and Udupi district
of Karnataka).10 11 We also examined data from a pilot
study carried out in Nairobi (Kenya) and two studies
completed in rural areas of China (Shaanxi, Sichuan and
Anhui provinces), all involving only primary healthcare
providers.12–15
Information regarding medications prescribed by
healthcare providers were collected in these published
SP studies but were not analysed in depth, especially with
regard to inappropriate use. This is because, in most
instances, the primary publications focused on overall
quality of care, rather than the specific components of
care.
Provider selection in original studies
Sampling approaches adopted in each primary study from
which our data were drawn are summarised in table 1.
For the two pharmacy-based studies, a random sample of
pharmacies was selected from a comprehensive list of all
those eligible obtained from relevant authorities.10 11 In
six of the other eight studies, healthcare providers were
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Sulis G, et al. BMJ Global Health 2020;5:e003393. doi:10.1136/bmjgh-2020-003393
Table 1
Main features of SP studies included in our analyses
SP–provider
interactions Tracer conditions
Healthcare
sector
Facility
location
China (2013)
600
Angina, child
diarrhoea
Public
Rural
Census of all clinics designated under the New Cooperative
Yes
Medical Scheme (ie, the major public health insurance programme
in rural areas), followed by random selection of providers
100%
China (2015)
299
Presumptive TB
Public
Rural
Census of all public providers followed by random sampling
from one prefecture in each of 3 provinces out of a total of 47
prefectures, chosen to be representative of rural health systems
Yes
274/274 (100%)
Kenya (2014)
166
Angina, asthma,
child diarrhoea,
presumptive TB
Public and
private
Urban
Non-random convenience sample designed to include lowincome, middle-income and high-income neighbourhoods in
various Nairobi areas
Yes
46/49 (93.9%)
Madhya Pradesh,
India (2010–2011)
1123
Angina, asthma,
child diarrhoea
Public and
private
Rural
No
Census of all medical care providers working in 60 villages
randomly sampled in three districts in Madhya Pradesh; all public
providers and qualified private providers were automatically
sampled; for each public provider, the closest private practitioner
was also sampled
Not applicable
Delhi, India (2014)
250
Presumptive and
confirmed TB,
presumptive MDRTB
Private
Urban
Convenience sample (pilot study)
Yes
Not available
Mumbai and Patna,
India (2014–2015)
2602
Presumptive and
confirmed TB,
presumptive MDRTB
Private
Urban
Street-by-street mapping of private providers who were known
to see adult outpatients with respiratory symptoms, followed by
random sampling stratified by provider qualification and private
provider interface agency registration status
No
Not applicable
Birbhum district,
West Bengal, India
(2012–2014)
823
Angina, respiratory
distress, child
diarrhoea
Private
Rural
Census of private health providers who had been practising for at Yes
least 3 years in 203 villages across Birbhum district
304/360 (84.4%)
Mumbai, Patna and
Delhi, India
(2014–2015)
1200
Presumptive TB,
confirmed TB
Pharmacies
Urban
No
Convenience sample of 54 pharmacies from 28 low-income
localities in Delhi (pilot phase), random sampling of pharmacies in
Mumbai and Patna from a list of all pharmacies registered in the
two cities
Not applicable
Udupi district,
Karnataka, India
(2018)
1522
For both adults
Pharmacies
and children: upper
respiratory tract
infection, diarrhoea,
presumptive malaria
No
Not applicable
Provider selection approach
Urban
Of the 350 pharmacies registered in the district as per the local
and rural pharmacy association, 279 were considered eligible for the
study after excluding those operating inside hospitals (47), those
permanently closed or under renovations (10), those that could
not be identified by the field team (4), those for veterinarian
purposes only (1) and those used for SP training (10).
*For studies in which provider consent was required.
MDR-TB, multidrug resistant tuberculosis; SP, standardised patient; TB, tuberculosis.
Provider Provider
consent participation*
3
BMJ Global Health
Study site (year)
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randomly sampled after performing a census or street-bystreet mapping in the study areas.7–9 13–15 A convenience
sample of practitioners was selected in two pilot studies
respectively performed in Delhi and Nairobi.6 12 A waiver
of provider consent was obtained in four out of nine
studies, all carried out in India, two of which involved
pharmacies.7 9–11 In all the others, verbal or written
informed consent was sought at least 6 weeks prior to the
commencement of SP–provider interactions in order to
reduce the risk of SP detection. Yet, participation rates
were very high (85%–100%) among eligible health
practitioners, and non-participation was usually due to
logistical issues on the day of the visits rather than active
refusal to be involved in the project. Hence, it is reasonable to expect negligible differences between participants
and non-participants, making non-response bias a minor
concern. In all studies, SPs were randomly assigned to
providers, and completion rates of SP–provider interactions were always very high.
Tracer conditions
Tracer conditions (ie, SP case presentations) were
defined similarly across SP studies, thus allowing comparisons across settings. Cases ranged from presumptive or
confirmed tuberculosis (TB) (which requires specific
anti-TB treatment as per WHO recommendations) to
self-limiting infections, such as watery diarrhoea or upper
respiratory tract illness (which only need support treatment, eg, rehydration therapy for diarrhoea), to noncommunicable diseases like asthma or chest pain indicative of angina (these should be referred to a higher level
of care). Importantly, none of such conditions requires
antibiotics, which means that any antibiotic prescribed to
SPs is deemed inappropriate by indication (ie, overprescription).
Outcome assessment
Raw data from original studies were harmonised and
recoded as needed. We used the available information
on medications that were prescribed or dispensed during
each SP–provider interaction to categorise individual
drugs. Antibacterial agents were further classified using
both the ATC (Anatomical–Therapeutic–Chemical)
Index and the WHO Access–Watch–Reserve (AWaRe)
framework.16 17 Fixed-dose combinations (FDCs) of antibiotics (eg, ciprofloxacin/ornidazole) were classified as
‘discouraged’ antibiotics as per WHO recommendations.
The primary outcome measure was expressed as the
proportion of SP–provider interactions that resulted
in antibiotic prescription or dispensing. Secondary
outcomes were proportions of specific groups of antibiotics that were prescribed or dispensed both overall and
across strata of key variables of interest. These proportions provide a direct measure of antibiotic overuse.
Statistical analyses
For studies carried out in health facilities, we conducted
country-level descriptive analyses and reported the crude
4
proportion of SP–provider interactions that resulted
in antibiotic prescription or dispensing. The overall
proportion of prescribed or dispensed antibiotics, along
with ATC-class and AWaRe group-specific proportions,
was calculated across strata defined by key variables of
interest, such as healthcare sector (public/private),
facility location (urban/rural), provider qualification
(qualified/non-qualified, defined based on whether they
had at least a bachelor’s degree in medicine) and tracer
conditions. For all prevalence proportions, we computed
95% CIs using bootstrapping in order to account for clustering at the study level.18
In order to examine the factors associated with antibiotic prescribing in health facilities in India, we fit a
hierarchical Poisson regression model that allows direct
estimation of adjusted prevalence ratios (aPRs) even if the
outcome is common as in this case. Our model included
a random intercept for studies and dummy variables for
facility location, healthcare sector, provider qualification
and tracer conditions as predictors.19 As we anticipated a
fair amount of between-study heterogeneity, we decided
to opt for a mixed model that could better account for
it as compared with including the study or study site as a
covariate. Among tracer conditions, only angina, asthma
and presumptive TB could be included in order to avoid
sparse data problems (ie, violations of the positivity
assumption). The effect of all predictors was expected to
be similar across studies, and therefore only fixed slopes
were considered. These analyses were restricted to India
because we had diverse and more data. We also considered alternative models and examined the pros and cons
of each. A full description of our analyses is provided in
online supplementary file 1.
Data from pharmacies were not pooled because contexts
and tracer conditions were highly heterogeneous in the
two available studies. Therefore, we only calculated prevalence proportions and 95% CIs of dispensed antibiotics,
both overall and in stratified analyses.
All analyses were performed using Stata 16.
Patient and public involvement
It was not possible to involve patients or the public in the
design, conduct, reporting or dissemination plans of our
research because this is a secondary analysis of previously
conducted studies.
RESULTS
The main features of SP studies that were included in
our analyses are summarised in table 1. A total of 4798
SP–provider interactions were completed in health
facilities across urban and rural India, predominantly
in the private sector. Both private and public healthcare providers were involved in the pilot study carried
out in Nairobi (166 interactions), whereas studies from
rural China only targeted the public sector (899 interactions). For these health facility-based studies, we first
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BMJ Global Health
present summary statistics and then report results from
our models.
Antibiotic overuse across settings
In India, 2392 of 4798 (49.9%, 95% CI 40.8% to 54.5%)
SP–provider interactions resulted in at least one antibiotic prescription (table 2). Similar proportions were
observed in Nairobi (83 of 166; 50.0%, 95% CI 42.2% to
57.8%), while a lower percentage was found in the China
studies (259 of 899; 28.8%, 95% CI 17.8% to 50.8%).
However, in the latter case, the CI was substantially wide,
reflecting the considerable between-study variance due
to differences in tracer conditions evaluated.
In most instances, only one antibiotic was given
during an individual SP–provider interaction; less than
5% of interactions across all settings resulted in two or
more antibiotics prescriptions. Crude analyses of data
from India indicate that antibiotic overprescription was
more common among healthcare providers in urban
areas, among those working in the private sector and
among qualified professionals. Furthermore, antibiotics
were largely overprescribed to patients presenting with
a diverse range of clinical conditions in all countries
(figure 1). In India, the percentage of subjects receiving
antibiotics was close to 50% for most case types, with a
peak of 59.4% (95% CI 50.5% to 75.0%) among child
diarrhoea cases. However, for angina cases, it was 19.2%
(95% CI 16.8% to 21.1%). About half of the visits for
presumptive TB in China received antibiotics inappropriately, as opposed to 9.2% (95% CI 5.9% to 12.4%) of
visits for suspicious angina and 27.4% (95% CI 21.8% to
32.5%) for child diarrhoea. Case-specific estimates from
Nairobi are highly imprecise due to the small sample size.
Type of antibiotics used
Across studies performed in India, 2768 antibiotics were
given to 2392 patients. The top 10 most prescribed antibiotics across SP–provider interactions in India were azithromycin (381, 13.8%), amoxicillin+beta-lactamase inhibitor (344, 12.4%), amoxicillin (264, 9.5%), levofloxacin
(202, 7.3%), cefixime (198, 7.2%), ofloxacin (165, 6.0%),
ofloxacin+ornidazole (150, 5.4%), norfloxacin+tinidazole (136, 4.9%), ciprofloxacin (102, 3.7%) and cefpodoxime (88, 3.2%). Broad-spectrum agents with higher
potential for selecting resistance (Watch antibiotics) were
disproportionately represented (47.6%, 95% CI 26.8% to
54.0%), and even more so in urban areas (54.9%, 95% CI
54.9% to 55.4%) (table 3). This reflects the heavy use of
quinolones, cephalosporins and macrolides that respectively accounted for 18.8% (95% CI 16.6% to 24.2%),
13.0% (95% CI 8.2% to 14.6%) and 15.4% (95% CI 4.1%
to 19.3%) of all antibiotics prescribed in India. Nearly
80% of Watch antibiotics were given to SPs portraying
a TB case (1086/1362). Three different last-resort or
‘Reserve’ antibiotics (colistin, linezolid and faropenem)
were prescribed in a total of 23 SP–provider interactions
in India, mainly for child diarrhoea (14/23).
Sulis G, et al. BMJ Global Health 2020;5:e003393. doi:10.1136/bmjgh-2020-003393
Discouraged antibiotics, that is, FDCs other than antimycobacterial drugs (such as norfloxacin+tinidazole or
ofloxacin+ornidazole) accounted for 12.1%, of which all
but one were given for child diarrhoea. Anti-TB medications represented 8.3% of antibiotics in India; almost
all of them were given by healthcare providers in urban
areas; and none could be considered appropriate based
on the expected correct management of such cases.
About one-quarter of drugs prescribed in studies from
China could not be categorised based on the AWaRe
framework because only the drug class was reported.
These were mainly cephalosporins, most likely second
or higher generation, and therefore the overall proportion of Watch-group antibiotics is expected to be greater
than 32.9% (table 3). Undefined cephalosporins were
by far the most prescribed antibiotics in China (76/301,
25.2%), followed by gentamicin (45/301, 15.0%), amoxicillin (37/301, 12.3%), erythromycin (26/301, 8.6%)
and levofloxacin (18/301, 6.0%).
Subgroup analyses of antibiotic prescription patterns
among SP–provider interactions that took place in
Nairobi were limited by the small sample size. However,
85.4% (76/89) of all antibiotics prescribed were first-line
and narrow-spectrum agents from the ‘Access’ group,
while the remaining belonged to the ‘Watch’ group.
Factors associated with antibiotic overuse in India
Prevalence ratios of antibiotic overuse and their 95%
CIs estimated through mixed-effects Poisson regression
analysis are reported in figure 2. The adjusted prevalence of antibiotic prescribing was lower in urban versus
rural areas (aPR=0.70; 95% CI: 0.52 to 0.96), for subjects
presenting with suspicious angina (aPR=0.33; 95% CI:
0.27 to 0.40), and asthma (aPR=0.77; 95% CI: 0.66 to
0.89). Patients with presumptive TB were more likely to
receive inappropriate antibiotics (aPR=1.19; 95% CI: 1.07
to 1.33) as compared with individuals with other clinical
conditions. Qualified practitioners were more likely to
prescribe antibiotics than non-qualified ones (aPR 1.55;
95% CI: 1.42 to 1.70).
The hierarchical Poisson model did not show any significant difference between public and private providers,
but this is in contrast with what emerged from alternative
models as described in online supplementary file 1.
Antibiotic dispensing in pharmacies
Our secondary analysis of data from two pharmacybased SP studies showed that over-the-counter antibiotic
dispensing is also a common problem in various parts of
India (table 4).
In Udupi district (Karnataka state) the proportion of
SP—pharmacist interactions that resulted in antibiotic
dispensing was 3.6% (95% CI: 2.6 to 4.6), with a similar
pattern in both urban and rural areas. In contrast, at
least one antibiotic was dispensed in 319/1,200 interactions performed across Delhi, Mumbai and Patna,
corresponding to 26.6% (95% CI: 24.2 to 29.2) of the
total. However, a direct comparison between these two
5
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BMJ Global Health
Country
All
Variable
At least one antibiotic
Antibiotics, n
0
1
2
3
India
Proportion
(95% CI)
2734/5863
3129/5863
n/N
2465/5863
260/5863
9/5863
China
n/N
Proportion
(95% CI)
46.6 (33.4 to 53.9)
2392/4798
53.4 (46.1 to 66.6)
Kenya
n/N
Proportion
(95% CI)
n/N
Proportion
(95% CI)
49.9 (40.8 to 54.5)
259/899
28.8 (17.8 to 50.8)
83/166
50.0 (42.2 to 57.8)
2406/4798
50.1 (45.4 to 57.9)
640/899
71.2 (49.2 to 71.2)
83/166
50.0 (42.2 to 57.8)
42.0 (31.4 to 47.4)
2159/4798
45.0 (39.8 to 48.2)
229/899
25.5 (25.5 to 42.8)
77/166
46.4 (39.2 to 54.2)
4.4 (1.6 to 6.5)
225/4798
4.7 (1.4 to 6.6)
29/899
3.2 (3.2 to 7.7)
6/166
3.6 (1.2 to 6.6)
0.2 (0.03 to 0.3)
1/899
0.1 (0.1 to 0.3)
0/166
0
0.2 (0.02 to 0.3)
8/4798
BMJ Global Health
6
Table 2 Number, proportion and bootstrapped 95% CIs (based on study-level clusters) of standardised patient–provider interactions in health facilities that resulted in
prescription or dispensing of antibiotics across strata of key variables
Health facility location
Urban
1653/3018
54.8 (50.0 to 55.2)
1570/2852
55.0 (53.0 to 55.2)
–
–
83/166
50.0 (42.8 to 57.8)
Rural
1081/2845
38.0 (26.6 to 48.1)
822/1946
42.2 (39.0 to 46.7)
259/899
28.8 (17.8 to 50.8)
–
–
Public
443/1321
33.5 (20.6 to 50.8)
156/367
42.5 (37.6 to 47.7)
259/899
28.8 (17.8 to 50.8)
28/55
50.9 (38.2 to 63.6)
Private
2291/4542
50.4 (40.8 to 54.5)
2236/4431
50.5 (50.2 to 54.5)
–
–
55/111
49.5 (40.1 to 51.6)
Healthcare sector
Qualified
1186/1906
62.2 (45.4 to 71.3)
1115/1768
63.1 (44.6 to 71.8)
71/138
51.4 (42.8 to 59.4)
NA
NA
Non-qualified
1358/3191
42.6 (38.7 to 48.6)
1277/3030
42.1 (37.8 to 47.9)
81/161
50.3 (42.9 to 57.8)
NA
NA
Clinical presentation
Angina
169/955
17.7 (12.2 to 28.3)
115/598
19.2 (16.8 to 21.1)
Asthma
330/718
46.0 (44.0 to 50.2)
308/676
45.6 (43.5 to 49.0)
29/315
–
9.2 (5.9 to 12.4)
–
25/42
59.5 (45.2 to 73.8)
22/42
52.4 (38.1 to 66.7)
Child diarrhoea
490/997
49.1 (33.4 to 67.9)
399/672
59.4 (50.5 to 75.0)
78/285
27.4 (21.8 to 32.5)
13/40
32.5 (17.5 to 45.5)
Presumptive TB
1293/2253
57.4 (51.3 to 58.6)
1118/1912
58.5 (58.4 to 59.3)
152/299
50.8 (44.8 to 56.2)
23/42
54.8 (39.3 to 69.0)
Confirmed TB
194/404
48.0 (47.7 to 50.0)
194/404
48.0 (47.7 to 50.0)
–
–
–
–
Presumptive MDR-TB
258/536
48.1 (48.0 to 48.1)
258/536
48.1 (48.0 to 48.1)
–
–
–
–
13.2 (9.4 to 20.4)
50.7 (35.6 to 57.5)
65/498
1928/3628
13.1 (9.7 to 17.4)
53.1 (38.4 to 58.0)
33/263
226/636
12.5 (7.3 to 31.6)
35.5 (23.3 to 55.4)
3/6
67/120
50.0 (16.7 to 83.3)
55.8 (47.5 to 64.2)
Patient referred for further evaluation*
Yes
No
101/767
2163/4384
*All child diarrhoea cases from India and Kenya (n=712) were excluded from this analysis because children were not directly assessed by the provider.
MDR-TB, multidrug resistant tuberculosis; NA, not available; TB, tuberculosis.
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Sulis G, et al. BMJ Global Health 2020;5:e003393. doi:10.1136/bmjgh-2020-003393
Provider qualification
Figure 1 Crude percentage of SP—provider interactions resulting in antibiotic prescription/dispensing, by country and
selected conditions (pharmacy-based studies are not included). SP, standardised patient; TB, tuberculosis.
studies is not possible owing to the very different contexts
involved and particularly to the different types of cases
that were examined. As observed in studies from healthcare facilities, subjects presenting to pharmacies with
symptoms suggestive of TB were generally more likely to
receive an antibiotic as compared with other conditions.
The average proportion of Watch-antibiotics (predominantly quinolones and cephalosporins) dispensed across
the three cities was 49.4% (95% CI: 43.9 to 54.4), ranging
from 24.0% (95% CI: 15.0 to 32.0) in Mumbai to 60.9%
(95% CI: 55.1 to 67.1) in Patna. A deeper evaluation of
antibiotic dispensing in Udupi district is limited by the
small sample size. Only 55 antibiotics were dispensed
across 1522 interactions, thus making subgroup analyses less meaningful. Yet, it is worth highlighting that
nearly half of these antibiotics were discouraged FDCs
of two antibiotics, whereas the remaining were almost
equally distributed among Access- and Watch-groups.
More details regarding the types of antibiotics dispensed
across pharmacies in both studies are presented in online
supplementary file 2.
DISCUSSION
Our analysis of past SP studies involving 4798 SP–provider
interactions in India showed that healthcare providers
in primary care settings prescribed antibiotics to about
half (49.9%) of patients presenting with clinical conditions that do not require antibiotics. Antibiotic overprescribing was found to be similar (50% of SP–provider
interactions) in a small SP study carried out in Nairobi,
Kenya. Pooled data from two studies conducted in China
showed lower levels of antibiotic overuse (28.8%), but it
should be noted that percentages differed substantially
across individual studies, likely reflecting the different
type of cases being involved. In fact, SP–provider interactions involving presumptive TB cases were more likely to
Sulis G, et al. BMJ Global Health 2020;5:e003393. doi:10.1136/bmjgh-2020-003393
result in antibiotic prescription as compared with other
clinical conditions. Among the two pharmacy-based SP
studies done in India,10 11 the proportion of antibiotic
dispensing was 26.6% and 3.6%, respectively.
Although our focus was on LMICs, the overuse of antibiotics is not confined to LMICs. Large population-based
cohort data have shown that antibiotic overuse in ambulatory settings across the United States was 30% among
children and 17% among adults with certain respiratory
tract illnesses for which antibiotics are not indicated
(eg, asthma, allergies, acute bronchitis or bronchiolitis).20 An analysis of antibiotic prescription practices
based on administrative data from Ontario, Canada,
recently reported an overall rate of unnecessary antibiotic prescribing in primary care of 15.4%, though much
higher percentages were observed for some respiratory
conditions such as acute bronchitis (52.6%).21 However,
a direct comparison with higher income countries cannot
be done due to differences in study methodologies and
local epidemiology.
Nearly 50% of all antibiotics prescribed in the context
of India SP studies belonged to the ‘Watch’ list, with a
peak of 80% among patients presenting with symptoms
suggestive of TB, which is consistent with national antibiotic sales.22 Watch-antibiotics accounted for almost 33%
of all antibiotics across China SP studies, but this is likely
underestimated because nearly one quarter of all antibiotics could not be classified due to insufficient information. Of note, we observed a large use of cephalosporins
(presumably second or third generation ones), which is
in line with previous findings from drug sales analyses
and prescription audits conducted in various parts of
China.2 23 24 In contrast, the small SP study conducted
in Nairobi revealed that over 85% of prescribed antibiotics were from the ‘Access’ group, and half of these were
either trimethoprim/sulfamethoxazole or amoxicillin.
7
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BMJ Global Health
Frequency of antibiotics prescribed/dispensed in health facilities across study countries, overall and according to both the AWaRe and ATC classifications
India
Urban India
Rural India
Drug type
All settings
N
Proportion (95% CI)
N
Proportion (95% CI)
N
Proportion (95% CI)
China
N
Proportion (95% CI)
Any antibiotic
AWaRe classification
2768
–
1896
–
872
–
301
–
Access
876
31.6 (30.0 to 38.9)
584
30.8 (29.8 to 30.8)
292
33.5 (29.9 to 37.1)
126
41.9 (36.2 to 47.2)
Watch
1317
47.6 (26.8 to 54.0)
1041
54.9 (54.9 to 55.4)
276
31.7 (21.2 to 40.3)
99
32.9 (27.6 to 37.9)
Reserve
23
0.8 (0.5 to 1.8)
8
0.4 (0.4 to 0.5)
15
1.7 (1.0 to 2.1)
1
0.3 (0.3 to 1.3)
Discouraged
334
12.1 (4.3 to 36.3)
50
2.6 (2.6 to 2.8)
284
32.6 (25.1 to 44.8)
1
0.3 (0.3 to 1.3)
Not available*
218
7.9 (5.4 to 10.8)
213
11.2 (11.2 to 11.5)
5
0.57 (0.3 to 1.0)
74
24.6 (19.9 to 29.2)
Penicillin
711
25.7 (18.8 to 27.0)
535
28.2 (27.7 to 28.2)
176
20.2 (17.6 to 21.7)
68
22.6 (17.6 to 27.2)
Cephalosporin
361
13.0 (8.2 to 14.6)
294
15.0 (14.9 to 15.0)
76
8.7 (7.8 to 10.7)
75
24.9 (20.9 to 29.2)
21
0.8 (0.6 to 1.8)
9
0.5 (0.47 to 0.51)
12
1.4 (1.1 to 2.1)
0
0
BMJ Global Health
8
Table 3
ATC classification
First generation
22
0.8 (0.2 to 1.1)
Third generation
318
11.5 (7.1 to 12.9)
Not available*
0
0
20
1.1 (1.1 to 1.2)
2
0.2 (0.2 to 0.4)
7
2.3 (0.7 to 4.0)
256
13.5 (13.3 to 13.5)
62
7.1 (6.4 to 8.1)
1
0.3 (0.3 to 1.0)
67
22.3 (18.3 to 26.6)
0
0
0
0
Macrolide
425
15.4 (4.1 to 19.3)
389
20.5 (20.4 to 21.3)
36
4.1 (4.1 to 4.3)
60
19.9 (15.6 to 24.3)
Quinolone
520
18.8 (16.6 to 24.2)
354
18.7 (18.5 to 18.7)
166
19.0 (18.5 to 26.8)
37
12.3 (9.0 to 15.9)
Tetracycline
67
2.4 (1.7 to 4.6)
34
1.8 (1.4 to 1.8)
33
3.8 (3.0 to 4.1)
0
0
Imidazole†
61
2.2 (0.8 to 7.1)
1
0.05 (0.05 to 0.06)
60
6.9 (6.3 to 7.5)
1
0.3 (0.3 to 1.3)
Sulfonamide‡
18
0.7 (0.2 to 1.9)
3
0.16 (0.16 to 0.17)
15
1.7 (0.9 to 2.1)
9
3.0 (1.3 to 5.0)
Aminoglycoside
6
0.2 (0.1 to 1.0)
0
0
0.7 (0.7 to 1.3)
45
15.0 (11.3 to 18.6)
Combinations§
289
12.1 (5.1 to 34.2)
50
2.6 (2.6 to 2.8)
284
32.6 (25.1 to 34.2)
1
0.3 (0.3 to 1.3)
Antimycobacterial
Other antibiotics
229
36
8.3 (0.3 to 10.9)
1.3 (1.0 to 2.4)
226
19
11.9 (11.9 to 12.2)
1.0 (0.1 to 1.0)
3
17
0.3 (0.2 to 0.5)
1.9 (1.8 to 2.6)
1
4
0.3 (0.3 to 1.3)
1.3 (0.3 to 2.7)
6
The unit of analysis is the individual drug, not the standardised patient–provider interaction.
*For these drugs, only the antibiotic class (eg, cephalosporin) was available.
†Only metronidazole was prescribed/dispensed.
‡Only trimethoprim–sulfamethoxazole was prescribed/dispensed.
§This category does not include combinations of antimycobacterial drugs.
ATC, Anatomical–Therapeutic–Chemical; AWaRe, Access–Watch–Reserve.
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Sulis G, et al. BMJ Global Health 2020;5:e003393. doi:10.1136/bmjgh-2020-003393
Second generation
Figure 2 Factors associated with antibiotic prescribing/dispensing in health facilities in India. Covariate-adjusted prevalence
ratios and their 95% CIs estimated from a hierarchical Poisson model are reported. SP, standardised patient; TB, tuberculosis.
This is in line with that observed in another SP study
carried out in urban public primary healthcare facilities
in South Africa, where 10/119 (8.4%) interactions for
presumptive TB resulted in antibiotic prescriptions, all of
which belonged to the access group.25 As with the Nairobi
study, however, the small sample size does not allow to
draw meaningful conclusions on antibiotic prescribing
patterns in the area.
Discouraged FDCs of antibiotics were commonly given
in India but not in other settings, accounting for 10.4% of
the total. FDCs were finally banned in India in September
2018, thus leaving hope for a change in the near future.
Table 4 Antibiotic dispensing in Indian pharmacies
Study setting
Udupi district, Karnataka
(n=1522)
Mumbai, Delhi and Patna
(n=1200)
Variable
n/N
Proportion (95% CI)
n/N
Proportion (95% CI)
Number of antibiotics
1
55/1522
3.6 (2.6 to 4.6)
294/1,00
24.5 (22.2 to 27.0)
2
0
0
25/1200
2.1 (1.3 to 2.9)
Pharmacy location
Urban
25/744
3.3 (2.2 to 4.7)
319/1200
26.6 (24.2 to 29.2)
Rural
30/778
3.9 (2.7 to 5.2)
–
–
Adult with URI
11/250
4.4 (2.0 to 7.2)
–
–
Adult with diarrhoea
12/259
4.6 (2.3 to 7.1)
–
–
Adult with fever (malaria suspect)
10/252
4.0 (1.6 to 6.3)
–
–
0
–
–
Clinical presentation
Child with URI
Child with diarrhoea
0/252
20/250
8.0 (4.8 to 11.2)
–
–
2/259
0.8 (0.4 to 1.9)
–
–
Child with fever (malaria suspect)
Adult with presumptive TB
–
–
221/599
36.9 (33.1 to 40.7)
Adult with confirmed TB
–
–
98/601
16.3 (13.5 to 19.3)
15/710
40/812
2.1 (1.1; 3.1)
4.9 (3.6; 6.4)
Patient referred to health provider
Yes
No
41/497
278/703
8.2 (5.8; 10.9)
39.5 (36.1; 43.2)
TB, tuberculosis; URI, upper respiratory illness.
Sulis G, et al. BMJ Global Health 2020;5:e003393. doi:10.1136/bmjgh-2020-003393
9
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BMJ Global Health
Alarmingly, we observed the use of some ‘Reserve’ antibiotics in primary care settings. In India, oral colistin was
prescribed for paediatric diarrhoea, and faropenem was
given to one patient with presumptive TB. This is very
concerning as parenteral colistin is the last resort drug
for treatment of extremely drug-resistant Gram-negative
infections,26 and using the oral formulation could drive
resistance in the community. Similarly, faropenem is an
oral penem antibiotic which has been shown to cause
cross-resistance to intravenous carbapenems.27 In China
SP studies, one presumptive TB case received aztreonam,
indicated for treatment of serious infections due to drugresistant Gram-negative bacteria.
According to our findings from India, antibiotic overuse
was particularly common in rural areas, among qualified
providers and for patients presenting with presumptive
TB. Besides leading to potentially dangerous diagnostic
delays,28 29 the unnecessary use of antibiotics causes
harms to the patient in terms of drug-associated adverse
events and increased out-of-pocket costs.
While normative boundaries may partly explain why
qualified providers prescribed more antibiotics than
non-qualified ones as observed in our analyses for
India, the widespread overuse of antibiotics suggests
that important training gaps likely exist. However,
prescribing behaviours among healthcare providers
also depend on a number of other factors, including
financial incentives from pharmaceutical companies,
patient expectations and requests, or just old habits that
are hard to die.8 30 31
The biggest strength of our study lies in the nature
and quality of the data used to investigate the extent and
patterns of antibiotic overprescribing. Although previous
research had already highlighted that Watch group antibiotics are highly prescribed across India and China,
such studies could not provide a clear picture of inappropriate antibiotic use owing to the limited amount of
clinical information available from prescription audits
and evaluations of drug sales data.32–34 Among the main
advantages of using SPs to evaluate prescription practices
is the fact that tracer conditions are standardised.5 In all
studies included in our analyses, such conditions were
very common illnesses that are frequently encountered
in primary care and that require a well-defined diagnostic
and therapeutic management that does not involve antibiotic use.
Furthermore, representative samples of healthcare
providers from public and/or private sectors were
selected in all SP studies conducted in India, with the
only exception of one relatively small pilot study in
Delhi. In this pooled dataset, private practitioners were
much more represented than public providers, but we
lacked statistical power to make appropriate comparisons between the two groups. Yet, this distribution well
reflects the fact that about 75% of outpatient visits in
India take place in the private sector, with nearly 70% of
primary care in the country being delivered by informal
providers.35 36
10
Of note, available data originated from a range of
geographical areas with different sociocultural and
economic profiles and could be generalisable to similar
contexts in India. For all these reasons, the representativeness of our findings is very good, and selection bias is
likely negligible due to the robust mapping and sampling
approach used across all SP studies.
There are limitations in our study. First, the SP study data
from China and Kenya were limited and lacked generalisability. Second, our analyses were restricted to overprescription and to a limited number of clinical scenarios.
Third, we could not investigate other important forms
of inappropriate antibiotic use, such as the choice of the
incorrect drug and dosage to treat a given infection. This
is an intrinsic limitation that arises from the type of tracer
conditions used across SP studies so far. Although the SP
methodology was initially implemented to assess overall
quality of care in LMICs and to evaluate educational/
behavioural programmes in high-income countries, this
approach is being increasingly adopted to gain insight
into medication use, and especially drug dispensing practices among pharmacists. Data recording systems in SP
studies are therefore improving in order to facilitate the
collection of key details regarding medications that were
harder to capture from studies whose main objective was
not related to drug use.
In conclusion, the prevalence of antibiotic overprescribing estimated from SP studies ranged from 29%
in China to 50% in India and Kenya, and Watch antibiotics accounted for a large proportion of antibiotics
prescribed in both India and China. Combining the SP
methodology with new tracer conditions would allow
overcoming many of the typical limitations of most
studies aimed at evaluating inappropriate antibiotic use
in greater detail. SPs represent a unique opportunity to
further explore prescription practices among healthcare
providers, including the management of common infectious diseases, such as pneumonia or urinary tract infections, that contribute substantially to the overall antibiotic
use in primary care. Future studies also need to focus on
untangling the channels for antibiotic overprescription
and better understand the determinants of such practice
among public and private healthcare providers in various
contexts.
The extent of antibiotic overuse in primary care across
LMICs is a serious concern and requires targeted antimicrobial stewardship interventions aimed at improving
rational and locally adapted prescribing practices. An
active involvement of private providers in all such interventions would be essential to ensure uptake, particularly
in countries where the private sector plays a major role in
healthcare. Greater efforts are also necessary to develop
and scale up accurate point-of-care tests that could guide
therapeutic choices where resources are scarce. Additional research is also required to evaluate whether antibiotic use (especially use of drugs such as azithromycin
and hydroxychloroquine) will dramatically increase as a
consequence of the COVID-19 pandemic, and concerns
Sulis G, et al. BMJ Global Health 2020;5:e003393. doi:10.1136/bmjgh-2020-003393
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BMJ Global Health
have already been raised about the implications for
AMR.37
Author affiliations
1
Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal,
Québec, Canada
2
McGill International TB Centre, McGill University, Montreal, Québec, Canada
3
McCourt School of Public Policy, Georgetown University, Washington, District of
Columbia, USA
4
School of Public Health, University of California Berkeley, Berkeley, California, USA
5
Division of Infectious Diseases, Department of Medicine, Washington University in
Saint Louis, Saint Louis, Missouri, USA
6
Dahdaleh Institute of Global Health Research, York University, Toronto, Ontario,
Canada
7
Centre for the AIDS Programme of Research in South Africa (CAPRISA), University
of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa
8
Centre for Policy Research, New Delhi, Delhi, India
9
Manipal McGill Program for Infectious Diseases, Manipal Centre for Infectious
Diseases, Manipal Academy of Higher Education, Manipal, Karnataka, India
Twitter Giorgia Sulis @giorgiasulis, Benjamin Daniels @_bbdaniels, Ada Kwan
@kwantada, Amrita Daftary @DaftaryAmrita and Madhukar Pai @paimadhu
Contributors GS, MP, JD and BD designed the study. GS and BD performed the
data cleaning and coding. GS analysed the data and prepared the first draft of the
paper. All authors critically revised the manuscript until final approval.
Funding Most studies included in our analyses were funded by the Bill and
Melinda Gates Foundation (OPP1091843). GS is a recipient of a Richard H.
Tomlinson Doctoral Fellowship (McGill University), and MP holds a Tier 1 Canada
Research Chair from Canadian Institutes of Health Research.
Competing interests MP is on the editorial boards of BMJ Global Health. All other
authors declare that they have no conflicts of interest.
Patient and public involvement Patients and/or the public were not involved in
the design, conduct, reporting or dissemination plans of this research.
Patient consent for publication Not required.
Ethics approval Each study had been conducted after ethics approval. Our study
was approved by the McGill Faculty of Medicine Institutional Review Board (IRB
review number: A04-B19-20B (20-04-053)), and all primary studies we included
had their own independent ethics approvals.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available upon request. All data relevant
to the study are included in the article or uploaded as supplementary information.
Parts of the data used for our analyses are available through original publications
as reported in the reference list. Additional data included in this study are available
upon request.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits
others to copy, redistribute, remix, transform and build upon this work for any
purpose, provided the original work is properly cited, a link to the licence is given,
and indication of whether changes were made. See: https://creativecommons.org/
licenses/by/4.0/.
ORCID iDs
Giorgia Sulis http://orcid.org/0000-0001-6641-0094
Benjamin Daniels http://orcid.org/0000-0001-9652-6653
Ada Kwan http://orcid.org/0000-0003-4889-9433
Amrita Daftary http://orcid.org/0000-0003-2275-3540
Jishnu Das http://orcid.org/0000-0002-5909-3585
Madhukar Pai http://orcid.org/0000-0003-3667-4536
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