Methods for AMR Surveillance in Communities – lessons from the Durban site

Gray AL and Essack SY

Department of Pharmacology, Nelson R Mandela School of Medicine and School of Pharmaceutical Sciences, University of KwaZulu-Natal, Durban, South Africa

Summary of the Durban pilot project Objective

To investigate the association between antibiotic use and resistance over time in respiratory tract infections in the Inner West metropolitan area of Durban

Methods Sputum specimens from consenting patients with self-

reported cough, with or without fever, at 4 convenience sampled sites

Retrospective prescription audit (2 weeks’ Rx per month) from 7 randomly selected private pharmacies, 7 convenience sampled private dispensing practitioners and 7 randomly selected primary health care clinics

Results No direct relationship between resistance levels and

antimicrobial usage; feasibility of establishing a system to generate data of this sort demonstrated

Methodological issues - resistance

Grand aim: “to determine the incidence of resistant infections among the total number of infections in a population” Overcome biases of hospital-based and

treatment failure associated data Need to choose a common infection with easily

accessed clinical material – in our case: respiratory tract infections sputum specimens (vs. oropharyngeal swab) -

minimally invasive ? carriage vs. infection

Problems encountered Negotiating access in both the public and

for-profit private sectors had to use convenience sample

Low return small % of positive sputa (521/3556) – 14.7% preponderance of some isolates - M. catarrhalis

resistance could not be characterised over time H. influenzae – 387/570 (67.9%) S. pneumoniae – 137/570 (24.0%) M. catarrhalis – 46/570 (8.1%)

Time consuming and expensive 3 fieldworkers, travelled 9 945km in 12 months

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Quarters (Oct 2002 to January 2004)

Reasons for declining returns …

Fieldworker motivation- repetitive task, dealing with difficult patients

Refusal by some patients to give repeated specimens when no immediate clinical benefit was discerned

Potential solutions Rotating sites – difficult to negotiate Community feedback – easier in public sector? Different target infection/carriage

Methodological issues - usage Grand aim: enable “early action to optimize

prescribing patterns and to reduce inappropriate use” move beyond hospital-level utilisation reviews cover all possible sources of community access:

informal (markets) – assumed not to be a major source in South Africa

formal – on-prescription sales by retail (community) pharmacies on-prescription sales by dispensing medical

practitioners issues by state-operated primary health care clinics

(largely nurse practitioners)

Initial challenges – negotiating access (1)

Negotiating access - pharmacies willing to co-operate – allowed random sampling stratified by socio-economic status of area

Data source – original prescriptions;

computerised accessible, good data

on the prescription – allnecessary details

sparse clinical data

Initial challenges – negotiating access (2)

Negotiating access – dispensing doctors Initially reluctant to co-operate – had to resort to

convenience sampling ongoing policy battles around the “right” to dispense currently sell prescription data – source of income for

the independent practitioner association (IPA) stratified by socio-economic

status of area

Data source – clinical records variable quality of data

Initial challenges – negotiating access (3)

Negotiating access – PHC clinics protracted negotiations with provincial and local

authorities – allowed random sampling stratified by size to include 2 large community

health centres (CHCs) mixed medical practitioner and nurse

prescribers

Data source – daily clinic registers

(“tick registers”) Sparse data

Data sources - clinics

Problems encountered …

Small numbers of antimicrobial prescriptions in smaller pharmacies, practices and clinics

Large number of “tick registers” in larger clinics (CHCs) – inability to access all data accurately

Solutions implemented returned to collect extra week of data per site (2

weeks’ Rx) deleted all AM usage data from one

problematic CHC (left with 20 sites)

Further concerns …

Missing data - clinics usually dispense original packs, so quantities could be

assumed – difficult when practices change e.g. increased prescribing of cotrimoxazole for PCP prophylaxis

Choice of denominator usually as DDD/1000 pop/unit time not possible without a “catchment population” or complete

coverage mobile population no “registration” with a provider using both sectors interchangeably

Used Defined Daily Doses (DDD) per 100 patients seen (doctors/clinics) or prescriptions dispensed (pharmacies)

Antimicrobial use - cotrimoxazole

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Time and expense

2 fieldworkers (full-time M.Pharm student, ½ day nurse) for medicine utilisation review travelled 15 578km (from Mar ’03 to Feb ’04)

3 fieldworkers for sputum collection travelled 9 945km

Feasibility as an ongoing venture? commitment of health authorities viability of the District Health Systems model routine data vs. periodic (survey) approach

Possible alternative sources of medicine use data (problems) Pharmacies

Wholesaler and distributor sales records Wide range of possible sources, locally and across the

country/ direct purchase from manufacturers – impact of new pricing regulations?

Doctors IPA data (currently revenue generating) Impact of dispensing license regulations and data

privacy regulations? Clinics

Depot issue records Clinic (CHC) to clinic supplies – impact of the DHS and

nature of future contracts with local authorities (municipal health services)?

Conclusions Although no direct relationship between resistance

levels and antimicrobial usage could be shown, the feasibility of establishing a system to generate data of this sort was demonstrated

Given the differences in antimicrobial use patterns in different settings, interventions to contain the development of resistance will have to be carefully tailored for each setting

Choose a different target infection or site of carriage; rotate collection between different sites; need to characterise resistance separately for different settings?

Need to measure AM usage in different settings; could perhaps limit to a few selected months of the year (some seasonal variation)

Acknowledgements

WHO/EDM for funding this pilot project Kathy Holloway (WHO, Geneva) and

Thomas Sorenson (Statens Serum Institut, Denmark) for technical advice and support

Our co-investigators (Wim Sturm, Fathima Deedat), the fieldworkers and laboratory staff, for their hard work and insights into the process

The staff at the facilities, for allowing us access to patients and/or data

The patients, for providing us with sputum specimens