Methods for AMR Surveillance in Communities – lessons from the Durban site Gray AL and Essack SY

2. 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

3. 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

4. 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

5. 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

7. 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

8. 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)

9. 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

10. 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

11. 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

12. Data sources - clinics

13. 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)

14. 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)

15. Antimicrobial use - cotrimoxazole

16. 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 ofhealth authorities • viability of the District Health Systems model • routine data vs. periodic (survey) approach

17. 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)?

18. 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)

19. Acknowledgements • WHO/EDM for funding this pilot project • Kathy Holloway (WHO, Geneva) andThomas 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