1. ANTI-MICROBIAL USAGE IN THE COMMUNITY AND RESISTANCE IN E.COLI
FROM URINE OF PREGNANT WOMEN IN INDIA
Thomas K1, Mathai E1, Mathai M1, Chandy SJ1, Joseph I1, Oommen R1, Mathew J1, Antonisamy B1, Banerjee M1, Cherian S1, Rajaratnam A1 Holloway K2, Sorensen T2
1Rational Antimicrobial Use Study Team,
Christian Medical College, Vellore, India
2WHO, Geneva

2. ABSTRACT
ANTIMICROBIAL USE IN THE COMMUNITY AND RESISTANCE IN E.COLI –
- FROM URINE OF PREGNANT WOMEN IN INDIA
Thomas K1, Mathai E1, Mathai M1, Chandy SJ1, Joseph I1, Oommen R1, Mathew J1, Antonisamy B1, Banerjee M1, Cherian S1, Rajaratnam A1 Holloway K2, Sorensen T2
1Rational Antimicrobial Use Study Team, Christian Medical College, Vellore, India,
2WHO, Geneva
Objective: To document the pattern of antimicrobial use in the community
and relate this to antimicrobial resistance (AMR) patterns in Escherichia coli
Design: An ecological surveillance study.
Setting: Health facilities and pharmacy shops in rural and urban areas
Method:.
Data on antimicrobial use - from a random selection of private practitioners,
hospitals and pharmacy shops using structured questionnaires.
Bulk drug data - from pharmacy shops, and hospitals.
Resistance data - from commensal E.coli isolated from urine of
asymptomatic pregnant women.
Main Outcome Measures:
1. Percentage of patients given specific antimicrobials.
2. Percentage of resistance of E.coli to specific antimicrobials.
Results:
In patient encounters, one antibiotic was prescribed in 5400 (47.3%).
Among the 5400, 2091 (38.7 %) contained one injectable medication.
The most commonly prescribed antibiotics were:
Fluoroquinolones (14.0%), Amoxicillin/Ampicillin (11.4 %), Cotrimoxazole (7.1%)
and Cephalosporins (5.6%).
1068 pregnant women were recruited (546 urban, 522 rural) .
E.coli growth was noted in 535 contaminated urine specimens
E.coli resistance :
Norfloxacin (3.8%), Ampicillin (20.1%), Cotrimoxazole (23.8%), Cefalexin (2.4%)
Conclusion:
Initial results show high levels of antimicrobial use in the community.
Time series analysis may establish association of drug use & resistance.
This information will help to plan interventions for rational drug use.

3. Background
Bacterial resistance to anti-microbial agents is on the increase.
Monitoring relationship between anti-microbial usage and resistance pattern in bacterial pathogens:
- an important tool to demonstrate need for rational drug use. 
A surveillance of anti-microbial resistance pattern of E.coli
in urine specimens from pregnant women was initiated.
This resistance pattern was related to the prescription pattern of antimicrobials in the community.
The documentation of a temporal relationship between drug resistance and usage can be used to plan an intervention.
This intervention once implemented may facilitate rational antimicrobial use

4. Aims & Objectives
Aim:
Phase 1: To establish an integrated surveillance model of
anti-microbial resistance and use
Phase 2: To develop an intervention to improve rational antimicrobial use and contain anti-microbial resistance.
Specific Objectives of Phase 1:
1.    To determine anti-microbial resistance of E.coli isolated from urine of pregnant women in rural and urban setting.
To describe the antimicrobial prescribing patterns
in the same areas, on a monthly basis.
To determine the ecological association between
resistance pattern and antimicrobial usage.              

5. Methods Design: An ecological surveillance Setting:
Public and private health facilities and pharmacy shops
1. Urban community -Vellore Town area, South India
2. Rural community -KV Kuppam block, South India
Data Collection:
A. ANTIMICROBIAL USAGE -
A list of private practitioners, hospitals and pharmacy shops in urban and rural areas was made.
Drug usage was studied using two methodologies:
1. Individual Drug Use Patterns 2. Bulk Drug Use

6. 1. Individual Drug Use
15 facilities were selected in both Vellore town (urban area)
and KV Kuppam block (rural area).
In each area, 5 private practitioners, 5 hospitals and 5
pharmacy shops were randomly selected.
Every month, each facility was visited and exit interview
was conducted using a structured questionnaire format.
30 patients being prescribed or dispensed any antimicrobial
agent were included from each facility.
From data collected, the outcomes determined were:
  Total number of drugs prescribed per patient
  Percentage of patients prescribed a specific antimicrobial
  Percentage of patients prescribed multiple antimicrobials
Percentage of patients given an injection        

7. 2. Bulk Drug Use
5 pharmacy shops selected from both urban and rural area
- Bulk use was determined using purchase and sales data
In rural area, bulk use was also estimated in 5 hospitals
- Data was collected using the drug stock register
In urban area, bulk use was also estimated in CMC hospital
(tertiary health care facility)
- Data was collected from pharmacy computer system
The DDD/1000 patients /month was calculated for a specific antimicrobial in a facility using two parameters:
The quantity of specific antimicrobials dispensed every month expressed as number of defined daily dose (DDD) .
2. The total number of patients visiting a facility in a month.

8. B. ANTIMICROBIAL RESISTANCE:
Approximately 100 pregnant women recruited monthly
from the antenatal clinics in :
1. CMC Hospital, Vellore (urban)
2. RUHSA hospital, KV Kuppam Block (rural).
Subjects taking antimicrobials during enrollment or who
received antimicrobials during previous week excluded.
In women who were symptomatic or suspected to have UTI,
midstream urine was collected aseptically and cultured.
Women who were asymptomatic and not suspected to have
UTI were instructed to collect deliberatively contaminated
urine in a sterile container.
The urine specimens were cultured in Maconkey agar.
Susceptibility test was performed in all isolates for
antimicrobials used in treatment of UTI and other common
gram negative infections.
The primary microbiological outcome was the percentage
of isolates resistant to specific antimicrobials.

9. C. DATA MANAGEMENT
Software was developed for data entry.
All data regarding patient recruitment, antimicrobial usage
and antimicrobial resistance was entered on a regular basis.
Data was double entered and verification was done
routinely.
Analysis was done using SPSS version 11.5 software.
Update reports were discussed during the monthly
investigators meeting.

10. Results Antibiotic Usage Pattern
5400 patients (47.3 %) prescribed or dispensed one
antimicrobial from a total of patient encounters.
406 patients (7.5 %) were prescribed more than one
antimicrobial.
2091 (38.7 %) had at least one injection
The commonly prescribed antimicrobials were:
    Fluoroquinolones – 14.0 %
Amoxycillin/ampicillin – 11.4%  
  Cotrimoxazole – 7.1 %
Cephalosporins – 5.6 %
Preliminary Data Analysis of Resistance in E. coli :
535 consecutive E. coli isolated from contaminated urine
E. coli resistance determined for specific antimicrobials:
Norfloxacin - 3.8%
Ampicillin %
Cotrimoxazole %
Cefalexin - 2.4%

11. INDIVIUDAL patient USE of COTRIMOXAZOLE
and RESISTANCE of E. coli - in health facilities 10 20 30 40 50 60
Aug
Sep
Oct
Nov
Dec
Jan
Pharmacy drug use
Hospital drug use
Ecoli Resistance
GP drug use
% Ecoli Resistance
& % Drug Use
RURAL AREA 5 15 25 35
URBAN AREA

12. BULK USE of COTRIMOXAZOLE
and RESISTANCE of E. coli - in health facilities
200
400
600
800
1000
1200
Aug
Sep
Oct
Nov
Dec
Jan 10 20 30 40
Pharmacy drug use
Hospital drug use
Ecoli Resistance
% Ecoli Resistance
RURAL AREA
DDD / 1000 patients 50
100
150
250
CMC drug use
URBAN AREA
DDD/ 1000 patients

13. Conclusions High levels of antimicrobial use in the community (43.7%)
Commonly used antimicrobials are fluoroquinolones (14%),
amoxycillin/ ampicillin (11.4%) and cotrimoxazole (7.1%)
Some antimicrobials with higher resistance in E.coli such
as Cotrimoxazole, are more frequently used in community.
As data is collected , its hoped that definite conclusions
can be drawn regarding the relationship between
antimicrobial use and resistance.
This information will be used in Phase II
- to plan multi-dimensional interventions.
These interventions should be planned based on local
evidence targetting health administrators, health providers
and consumers to improve rational antimicrobial use.