High cost burden and health consequences of antibiotic resistance: The price to pay

Abstract

Introduction:
Rising antibiotic resistance may negatively affect the health and cost of care for patients. This study aimed to determine the impact of antibiotic resistance on costs and health consequences for patients.

Methodology:
A one-year observational study was conducted at Christian Medical College, Vellore, a tertiary care hospital, on patients admitted into medical wards with a preliminary diagnosis of suspected sepsis. Patients with confirmed bacteremia were analysed in two groups - resistant and susceptible - based on susceptibility of causative bacteria to the empiric antibiotics administered. Clinical data and details about costs incurred were collected from hospital records. Costs and health consequences were compared using Mann-Whitney U test and Fisher's exact test. For median difference in costs, 95% bootstrap confidence interval was determined.

Results:
Overall, 220 patients were included. The median difference between resistant and susceptible groups in overall costs, antibiotic costs, and pharmacy costs was rupees (INR)/USD 41,993/700 (p = 0.001), 8,315/139 (p < 0.001) and 21,492/358 (p < 0.001), respectively. Health consequences such as intensive care admissions, complications, mortality, and length of stay were significantly higher in the resistant group as compared to susceptible group: 44% vs. 21% (p < 0.001), 56% vs. 37% (p = 0.006), 12% vs. 2% (p = 0.011), and 14 vs. 11 days (p = 0·027), respectively.

Conclusions:
Antibiotic resistance has a significant impact on cost and health consequences. These findings provide a key message for policymakers and other stakeholders to initiate feasible strategies to tackle resistance and reduce the burden.

Full text

Original Article
High cost burden and health consequences of antibiotic resistance: the
price to pay
Sujith J Chandy1,2, Girish S Naik2, Veeraraghavan Balaji3, Visalakshi Jeyaseelan4, Kurien Thomas5,
Cecilia Stålsby Lundborg1
1 Global Health (IHCAR), Dept of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
2 Department of Pharmacology & Clinical Pharmacology, Christian Medical College, Vellore, Tamil Nadu, India
3 Department of Clinical Microbiology, Christian Medical College, Vellore, Tamil Nadu, India
4 Department of Biostatistics, Christian Medical College, Vellore, Tamil Nadu, India
5 Department of Medicine, Christian Medical College, Vellore, Tamil Nadu, India
Abstract
Introduction: Rising antibiotic resistance may negatively affect the health and cost of care for patients. This study aimed to determine the
impact of antibiotic resistance on costs and health consequences for patients.
Methodology: A one-year observational study was conducted at Christian Medical College, Vellore, a tertiary care hospital, on patients
admitted into medical wards with a preliminary diagnosis of suspected sepsis. Patients with confirmed bacteremia were analysed in two
groups – resistant and susceptible – based on susceptibility of causative bacteria to the empiric antibiotics administered. Clinical data and
details about costs incurred were collected from hospital records. Costs and health consequences were compared using Mann-Whitney U test
and Fisher’s exact test. For median difference in costs, 95% bootstrap confidence interval was determined.
Results: Overall, 220 patients were included. The median difference between resistant and susceptible groups in overall costs, antibiotic
costs, and pharmacy costs was rupees (INR)/USD 41,993/700 (p = 0.001), 8,315/139 (p < 0.001) and 21,492/358 (p < 0.001), respectively.
Health consequences such as intensive care admissions, complications, mortality, and length of stay were significantly higher in the resistant
group as compared to susceptible group: 44% vs. 21% (p < 0.001), 56% vs. 37% (p = 0.006), 12% vs. 2% (p = 0.011), and 14 vs. 11 days (p
= 0·027), respectively.
Conclusions: Antibiotic resistance has a significant impact on cost and health consequences. These findings provide a key message for
policymakers and other stakeholders to initiate feasible strategies to tackle resistance and reduce the burden.
Key words: antibiotics; antimicrobial resistance; economic burden.
J Infect Dev Ctries 2014; 8(9):1096-1102. doi:10.3855/jidc.4745
(Received 22 January 2014 – Accepted 05 May 2014)
Copyright © 2014 Chandy et al. This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Antibiotic resistance is rising and is a truly global
problem [1,2]. It has reached alarming proportions in
low- and middle-income countries (LMICs) such as
India [3,4]. Resistance is closely associated with
antibiotic pressure at individual and aggregate levels
[5,6]. Inappropriate use of antibiotics in infections has
been well documented [7]. Many older-generation
antibiotics have become less effective against bacteria
[8]. In response, newer antibiotics have been
prescribed that are significantly more costly [9]. The
impact on patient outcomes and on health systems
need to be closely assessed, especially in LMICs.
Government health centers have been the main
facilities for healthcare in India. There has been a
deterioration of services over the years [10,11].
Currently, mainly the poor utilize these facilities.
Budget constraints necessitate stocks of inexpensive,
older-generation antibiotics such as cotrimoxazole.
Other antibiotics are often unavailable [7]. Due to poor
infrastructural facilities, many patients have
increasingly turned to the private health sector [11];
this has increased direct costs. Current estimates
suggest that medicines account for 72% of families’
out-of-pocket (OOP) health expenses [12].
It is therefore imperative to determine the
incremental cost burden to patients if causative
bacteria are resistant to empiric antibiotics. A loss of
valuable time and grave health consequences may
result, especially in severe bacterial infections. The
switch to more effective and newer antibiotics may
increase expenses. Studies on the impact of antibiotic

Chandy et al. –Antibiotic resistance burden J Infect Dev Ctries 2014; 8(9):1096-1102.
1097
resistance in individual patients are lacking, especially
from LMICs. With this purpose, a study was
conducted on hospital in-patients with a preliminary
diagnosis of suspected sepsis and confirmed
bacteremia to assess the direct cost burden and health
consequences of resistance to the empiric antibiotic
administered.
Methodology
Design and setting
An observational study describing costs and health
consequences was conducted at Christian Medical
College (CMC), a tertiary care, not-for-profit
university teaching hospital situated in Vellore, south
India. This hospital, having 2,140 beds and more than
6,000 outpatients per day, caters to patients from
various economic backgrounds and geographical
locations in India [13]. The hospital has basic and
higher speciality departments with more than ten
intensive care units. This study was done in the
medical wards and medical intensive care unit. The
study is reported in accordance with the strengthening
and reporting of observational studies in epidemiology
(STROBE) guidelines [14].
Participants, variables and data collection
Participants were included based on the following
criteria: (i) adult in-patients admitted into medical
wards between 1 January and 31 December 2010 with
a preliminary diagnosis of suspected sepsis; (ii)
patients prescribed empiric antibiotic therapy; and (iii)
blood culture report identifying causative bacteria with
antibiotic susceptibility profile. The main outcome
parameter was overall direct cost in rupees (one US
dollar = 60 rupees) [15]. Various categories of costs
incurred by the patients were documented. These were
costs of antibiotics, the total cost of pharmacy items
(medicines and consumable items), laboratory costs
(investigations), and ward costs (all other costs
incurred while in the ward). Overall costs included
pharmacy (including antibiotics), ward, and
investigation costs. Hospital electronic accounting
records and the pharmacy database were used to
determine these costs. The secondary outcome
parameters were length of stay in hospital, intensive
care admissions, complications, and mortality. This
information was collected from patients’ charts and
electronic records. Data access and availability was
good due to the comprehensive nature of data filing.
Triangulation through these sources was done to
maintain accuracy.
Resistance assessment
As part of the normal diagnostic work-up, patients
admitted with a preliminary diagnosis of suspected
sepsis had 5 to 8 mL of blood collected aseptically.
Bedside inoculation was done in Bact-Alert
bottles(bioMerieux, Marcy l’Etoile, France). Only
aerobic bottles containing Tryticase soy broth were
used. Bact-Alert bottles were loaded in
BacT/ALERT3D system until a positive signal was
identified, and characterized further using the Vitek2
system [16]. Samples were ruled negative if no signal
was identified after five days of incubation.Bacterial
resistance was assessed by antibiotic susceptibility
testing performed on isolates by the Kirby-Bauer disk
diffusion method at the microbiology department. This
department operates the quality assessment program
for microbiological laboratories in India under the
umbrella of Indian Association of Medical
Microbiologists. The susceptibility breakpoints for
each drug are defined according to Clinical Laboratory
Standards Institute (CLSI) guidelines [17]. The
clinical pharmacology unit of the hospital randomly
conducts content testing of antibiotics using high
performance liquid chromatography.
Procedure and analysis
Patients with a preliminary diagnosis of suspected
sepsis and receiving empiric antibiotic therapy were
categorized into two groups: (i) the resistant group –
all patients in whom the susceptibility report
documented resistance of causative bacteria to the
empiric antibiotic, and (ii) the susceptible group – all
patients in whom the report documented susceptibility
of causative bacteria to the empiric antibiotic. Empiric
choices in the guidelines for suspected infections were
based on local antibiograms. Empiric antibiotics were
retained or changed based on the susceptibility report
and clinical response. Antibiotics were coded based on
the ATC (Anatomical, Therapeutic and Chemical)
Index [18].
Overall and categorized costs incurred by patients
in each group were compared. Costs were compared in
rupees using Mann-Whitney U test and presented as
median costs and their respective inter-quartile ranges
(IQR). The median differences between the groups and
their 95% bootstrap confidence intervals (CIs) were
calculated using R version 2.15.1 [19]. Besides costs,
health consequences in the two groups were compared.
Length of stay was analysed using the Mann-Whitney
U test. The proportion of patients having
complications, patients with intensive care admissions,
and mortality in each group were compared using

Chandy et al. –Antibiotic resistance burden J Infect Dev Ctries 2014; 8(9):1096-1102.
1098
Fisher’s exact test. P < 0.05 was considered
significant.
Ethical approval
Permission to conduct this study was granted by
the Institutional Review Board of Christian Medical
College, Vellore (IRB(EC)-ER-5-10-03-2010).
Results
Over a period of one year, from January to
December 2010, a total of 33,897 blood cultures from
the entire hospital were received by the microbiology
laboratory, of which 2,264 had positive blood cultures
with confirmed bacteremia. Among this, 409 blood
cultures were from medical wards. Duplicate cultures,
cultures without susceptibility profiles, and cultures
belonging to patients who did not have a preliminary
diagnosis of suspected sepsis were eliminated from the
list. Finally, 220 patients who had a preliminary
diagnosis of suspected sepsis with confirmed
bacteremia and who were administered an empiric
antibiotic were included in the study. These patients
were divided into two groups –resistant and
susceptible – based on the susceptibility of the
causative bacteria to the empiric antibiotics
administered.
The resistant and susceptible groups (Table 1)
were comparable. The main co-morbidity was
diabetes. Other co-morbidities were kidney disease,
liver disease, and involvement of other systems.
Escherichia coli with resistance to empirically
used piperacillin-tazobactam was the most common
Gram-negative bacteria (Table 2). Staphylococcus
aureus with resistance to empirically used
cloxacillinwas the most common Gram-positive
bacteria.
There was a significant difference in cost (Table 3)
between resistant and susceptible groups in the three
main categories – overall cost, antibiotic cost, and total
pharmacy cost.
Intensive care admissions, complications, and
mortality were significantly higher in the resistant
group (Table 4). The median length of hospital stay
was also higher.
Discussion
In India, infections still contribute significantly to
morbidity and mortality [20]. Antibiotics are
frequently used in the community for many infections
[7,21]. There are many factors that promote their use
[22]. Whatever the factor, increased use contributes to
increased resistance [5,6]. Effective antibiotics have
thus become a precious resource, especially in severe
bacterial infections.
Baseline data (Table 1) shows 220 patients
categorized into two groups based on resistance or
susceptibility to empiric antibiotics. The baseline
demographic parameters and bacteria cultured
compared well between the groups. Potential
confounders such as diabetes (the major co-morbidity)
and the number of co-morbidities compared well.
The most common Gram-negative culture isolate
in the resistant group was Escherichia coli. Many of
these patients were empirically given piperacillin-
tazobactum (Table 2). Resistance to third-generation
cephalosporins, fluoroquinolones, and carbapenems
were also noted. Non-fermenting Gram-negative
bacteria (NFGNB) were the next most common Gram-
negative culture isolates. These isolates were resistant
to piperacillin-tazobactam, carbapenems
(meropenem), ciprofloxacin, and aminoglycosides
used empirically. This widespread resistance is of
great concern and reflects the dire situation regionally
and globally [1-3].
Cost burden
The median overall cost was significantly higher in
the resistant group compared to the susceptible group
(Table 3). The average daily wage of a rural male
casual worker in India is approximately INR 95 (USD
1.6) [23,15]. The median difference amount of INR
41,993 (USD 700) incurred by patients in the resistant
group equates to 442 days of wages spent. This
financial loss of more than one year’s wages
contributes significantly to the cost burden. Very few
studies have looked at direct costs of resistant
infections to patients, and none were conducted in
LMICs. In a study in United States on cost attributable
to acute resistant infections, the extra cost burden was
calculated at 21,018 dollars [24]. The burden in India
is compounded due to lack of health insurance and
rising OOP expenditure. The national poverty line is
INR 816 (USD 13.6) per capita per month in rural
areas and INR 1000 (USD 16.7) per capita per month
in urban areas [25]. In India, 21.9% of the population
is below the poverty line (BPL) [25]. For BPL
patients, the extra cost burden due to a single episode
of a severe bacterial infection could be
insurmountable. It may substantially raise the 5% rate
of Indian households that currently suffer catastrophic
health expenditures [12].

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Table 1. Description of demographics, co-morbidities, and bacteria cultured
n = 220
Resistant group
n = 133
Susceptible group
n = 87
Mean age with SD
52 years (± 17.3)
53 years (± 17.2)
Gender
Male
86 (65%)
58 (67%)
Female
47 (35%)
29 (33%)
Co-morbidities
Patients with co-morbidities
112 (84%)
76 (87%)
Patients with diabetes alone as co-morbidity
42 (32%)
33 (38%)
Mean number of co-morbidities per patient with SD
2.1 (± 1.3)
2.3 (± 1.5)
Bacteria cultured
Gram-negative bacteria (GNB)
102 (77%)
66 (76%)
Escherichia coli
53
37
Klebsiella pneumoniae
4
7
NFGNB*
36
15
Enterobacter
1
3
Other GNB
5
4
Mixed GNB
3
0
Gram-positive bacteria (GPB)
24 (18%)
18 (21%)
Staphylococcus aureus
17
8
Enterococcus
4
4
Streptococcus pneumoniae
3
4
Group A beta haemolytic streptococcus
0
2
Mixed GPB/GNB
7 (5%)
3 (3%)
*Non-fermenting Gram-negative bacteria included Acinetobacter baumanni and Pseudomonas aeruginosa
Table 2. Bacteria isolated in the resistant group and main empiric antibiotics used to which resistance was documented
Piperacillin-
Tazobactam
Cefotaxime
Ceftriaxone
Ciprofloxacin
Gentamicin
Amikacin
Meropenem
Ertapenem
Gram-negative
Bacteria (GNB)
Escherichia coli
43
3
6
7
2
1
1
-
Klebsiella
pneumoniae
3
1
-
-
1
1
1
-
Enterobacter
1
-
-
-
-
-
1
-
NFGNB*
18
5
-
8
9
2
16
3
Benzyl Penicillin
Cloxacillin
Ciprofloxacin
Vancomycin
Gram-positive
bacteria (GPB)
Staphylococcus
aureus
-
17
9
-
Enterococcus
2
-
1
1
Streptococcus
pneumoniae
2
-
1
-
*Non-fermenting Gram-negative bacteria included Acinetobacter baumannii and Pseudomonas aeruginosa
- indicates that particular antibiotic was not used empirically in patients whose blood culture grew respective bacterial isolates subsequently

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1100
A significant proportion of OOP health
expenditure in India is spent on medicines [12]. The
antibiotic costs borne by patients in the resistant group
were significantly higher – by INR 8,315 (USD 139) –
than those of patients in the susceptible group (Table
3). Pharmacy costs (Table 3) were again significantly
higher in the resistant group. This shows that antibiotic
resistance may also lead to use of other medicines and
consumables, thereby further adding to the costs.
Health consequences
Secondary outcomes such as intensive care
admission, complications, and length of stay were also
assessed (Table 4). Patients in the resistant group had
to stay in the hospital an extra three days. A US study
done on patients with hospital-acquired infections
reported a longer stay [26]. Longer bed stay has cost
implications and may increase the risk of hospital-
acquired infections. Bed occupation where availability
is scarce may delay treatment for other patients
waiting to be admitted.
A comparison of intensive care admissions showed
23% more admissions in the resistant group (Table 4).
Another study showed similar results, with a 37%
difference in intensive care admissions [24].Crucial
beds in intensive care maybe occupied, thereby
denying care to other critical patients. In our study, the
proportion of patients developing complications was
19% higher in the resistant group. Renal failure,
respiratory failure, and circulatory shock were some of
the common complications. In the few studies looking
at health consequences, the focus was length of stay
Table 3. Comparison of direct costs between resistant (R) and susceptible (S) groups (n = 220)
Cost in INR/USD
Resistant group
n = 133
Median cost INR/USD
(IQR)
Susceptible group
n = 87
Median cost INR/USD
(IQR)
R&S difference
Median cost INR/USD
(Bootstrap 95% CI)
p value
Overall cost
88,686/1,478
(36,265 – 164,850)
47,380/790
(25,847 – 86,087)
41,993/700
(16,667 – 63,848)
0.001
Antibiotic cost
16,734/279
(6,722 – 27,853)
8,255/138
(3,799 – 13,560)
8,315/139
(4,953 – 10,859)
<0.001
Total pharmacy cost
39,482/658
(20,205 – 64,431)
16,309/272
(9,359 – 36,891)
21492/358
(8,950 – 29,001)
<0.001
Laboratory investigation
cost
12,235/204
(4,452 – 22,309)
8,436/141
(4,035 – 16,278)
3,710/62
(136 – 7,033)
0.055
Ward cost
12,425/207
(7,543 – 20,925)
10,300/172
(7,419 – 16,090)
2,060/34
(-286 – 4,045)
0.108
Table 4. Comparison of health consequences between resistant (R) and susceptible (S) groups
Patients
n = 220
Resistant group
n = 133
Susceptible group
n = 87
Difference between R and S
groups
p value
Median length of stay in days
(IQR)
14
(8.5 – 22.5)
11
(8 - 17)
3
0.027
Hospital stay in days (range)
Intensive care admissions
2-108
59 (44%)
3-60
18 (21%)
23%
<0.001
Mortality
16 (12%)
2 (2%)
10%
0.011
Complications
75 (56%)
32 (37%)
19%
0.006
System wise complications
Renal
29
18
Circulatory
16
5
Metabolic
3
4
CNS
4
3
Respiratory
6
2
Renal & circulatory
9
-
Renal & metabolic
2
-
Renal & respiratory
2
-
Respiratory & circulatory
1
-
Others
3
-

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1101
and mortality [24,26]. Complications have a cascading
impact on overall costs and therefore need to be
assessed.
Mortality was five times higher in the resistant
group. The magnitude of difference is larger than was
previously reported. In a study with 1,391 hospitalized
patients, there were 70 deaths (5%), of which only half
had a resistant organism [24]. Another study in
intensive care found that the hazard ratio for discharge
dead or alive when comparing sensitive and resistant
organisms was close to one [27]. Both these studies
were not done in LMICs. The relatively higher
mortality in the resistance group in our study,
therefore, needs to be noted.
The impact of resistance and the required response
Rising resistance and the fear of ineffective
antibiotics may lead to treatment with prolonged
courses of newer, broader, and more expensive
antibiotics. This will raise the cost burden even
further. Rising resistance could also mean delays in
treatment and health consequences. Multi-pronged
strategies are needed to tackle the problem of
resistance. Strategies should include infection control,
improvement of diagnostics, guideline development,
continuing education, and regulation enforcement.
Changing behaviour and empowering the public are
also important. For this to happen, awareness
programs and mass media campaigns would be useful.
The findings of this study could provide a key
message that would catch the attention of all
stakeholders, raise awareness about resistance, and
help improve appropriate antibiotic use.
Methodological considerations
This is one of the first studies in an LMIC looking
at cost and health consequences of antibiotic
resistance. The data generated mainly focused on
direct costs, but gives crucial evidence on the huge
impact of antibiotic resistance. This study provides the
basis for a future economic study, where indirect and
intangible costs could be measured. Unlike high-
income countries, which have data pooled in electronic
records and national registries, the data for this study
had to be sourced through multiple channels including
accounts, pharmacy, clinical, and laboratory
departments. Our study included all organisms,
whereas others – one in China that compared
extended-spectrum beta lactamase-positive and
negative infections in intra-abdominal infections [28]
and one in Europe on bloodstream infections[29] –
focused on a limited number of organisms.
Conclusions
The findings of this study demonstrate
significantly higher costs to patients infected with
resistant bacteria as compared to those infected with
susceptible bacteria. Mortality, the greatest price that
patients have to pay, was significantly higher,
underlining the association of antibiotic resistance to a
fatal outcome. Other health consequences were also
significantly higher.
Overall, the message is clear and alarming. The
economic and health burden of resistance can be
devastating to individual patients and to health
budgets. This burden will be felt more by patients in
LMICs, such as India, with low health insurance
coverage and high OOP expenditure. This key
message needs to be disseminated to all stakeholders,
individuals, health professionals, hospital
administrators, policymakers, and society as a whole.
We hope that this message encourages stakeholders to
refocus their attention on the dangers of resistance and
tackle the problem through feasible strategies for
appropriate antibiotic use and infection control
practices. These measures will hopefully decrease the
cost burden to the individual and improve health in the
society.
Acknowledgements
We wish to thank the pharmacy, accounts, medical units,
and management of Christian Medical College, Vellore for
their cooperation for this study.
No specific funding was received for this study. S.J.C
received a scholarship from Erasmus Mundus External
Cooperation Window Lot 15, India.
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Corresponding author
Sujith J Chandy
Global Health (IHCAR), Dept of Public Health Sciences
Karolinska Institutet, Stockholm, Sweden
Phone: + 91-9443813800
Email: sjchandycmc@gmail.com
Conflict of interests: No conflict of interests is declared.