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Socio-economic & demographic determinants of antibiotic prescribing patterns in NIA Distinct Linkage Project using the N

This study examines the social, economic, and demographic factors that influence antibiotic prescribing patterns in Northern Ireland. The results reveal individual characteristics, area-level characteristics, and key findings and implications for health policy.

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Socio-economic & demographic determinants of antibiotic prescribing patterns in NIA Distinct Linkage Project using the N

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  1. Socio-economic & demographic determinants of antibiotic prescribing patterns in NIA Distinct Linkage Project using the Northern Ireland Longitudinal Study & HSC BSO Enhanced Prescribing DatabaseFiona Johnston, Michael Rosato (NILS RSU) Kim Moylan (HSC BSO)

  2. Presentation Outline • Background to the study • Project design & methods • Results: (1) individual characteristics • demographic • socio-economic & cultural • health status (2) area-level characteristics • area deprivation (NI MDM) • Key findings & implications

  3. Background to the Study Project Approval Criteria: relevant to HSC aims &support public policy longitudinal & unique linking non-routine HSC individual data: rationale & feasibility legal & ethical Context of research: antibiotic (AB) prescribing patterns in NI public health: AB resistance = major global health threat socio-cultural: significant variation in AB prescribing population: demographic / socio-economic differences in AB consumption policy and practice: intervention & stewardship strategies N.B. need targeted information for patients, practitioners & policy-makers

  4. Antibiotic Resistance

  5. Project Design Study Aims • analyse antibiotic prescribing patterns by individual & area attributes • inform health policy & research on management of prescribing Study Design • exemplar project • based on 2 data sources: NILS members linked toantibiotic prescription data held on EPD (12 months ending May 2010) • sample size: • linked AB prescription data to c. 28% of NILS core sample (c. 125,000) • indicator for high (70% and over) and low scanned coverage rates

  6. Methodology Relationship between AB prescriptions AND: individual: demographic, cultural, health & socio-economic characteristics area attributes: deprivation and settlement classifications Categorisation of counts of AB items (BSO advice & ARAC validated): 0: no AB prescription 1 to 2: minimal no. of prescriptions (may need 2nd as 1st didn’t work) 3 to 5: receiving items due to more than one illness 6+: potentially a higher risk group and on high levels/long-term AB use Descriptive & regression analysis to identify patterns and test the relative importance of variables on usage Database linkage & encryption methodology

  7. HSC BSO Enhanced Prescribing Data Pharmacist codes the drug items that were dispensed and these codes are keyed by BSO data prep 2-D barcode captures all information printed on the prescription, including prescribed drug data, plus some data not visible on the prescription Second 2-D barcode pre-printed on prescription gives GP information

  8. Results Individual and Household Characteristics Demographic Cultural Socio-economic Health status Area Characteristics Area deprivation

  9. Descriptive Analysis – Population Distribution Distribution of Prescriptions by Items & Sex Received AB Prescription by Age & Sex

  10. Demographics: Age & Sex Mutually adjusted for Age & Sex (All, high coverage)

  11. Demographics: Marital Status Adjusted for Age & Sex (16-74, high coverage)

  12. Socio-Cultural: Community BackgroundAdjusted for Age, Sex & Marital Status (16-74, high coverage)

  13. Socio-Economic: Tenure/Capital ValueModels adjusted for Age & Sex (All, high coverage)

  14. Demographics: Health StatusAdjusted for Age, Sex & Marital Status (16-74, high coverage)

  15. Results Individual and Household Characteristics Demographic Cultural Socio-economic Health status Area Characteristics Area deprivation

  16. Persons 25-74: prescribing patterns by area deprivation measure. Model adjusted for age, sex and marital status

  17. Persons 25-74: prescribing patterns by indicator of personal deprivation. Model adjusted for age, sex & marital status

  18. Area Factors: Area Deprivation Models adjusted for Age & Sex & Marital Status (16-74, high coverage)

  19. Socio-economic: Person Deprivation ModelAdjusted for Age & Sex & Marital Status (25-74, high coverage)

  20. Ongoing analysis …. however clear patterns emerging: women; those not married; those living in deprived circumstances; and those with a Catholic comm. background all show significantly increased risks when compared with their associated reference groups. Policy Implications: targeted & relevant information for practitioners, campaigns & interventions patient records systematically linked: diagnostic, prescribing & laboratory data dissemination activities … ideas? Future Research: ‘test’ project: not fine-grained information indication / mortality data qualitative analysis: practitioner attitudes & patient expectations more DLPs +++ Key Findings and Implications

  21. Pending & Current NILS DLPs BSO dental activity data: adolescent dental health and use of dental care services (PhD thesis) child dental health and use of dental care services (ongoing) BSO prescribing data: pharmaco-epidemiological studies of characteristics related to; anxiolytic and anti-depressants (pending) diabetes (pending) & anti-obesity medication (pending) antidepressant use among women of reproductive age (submitted for approval) QARC breast screening data: variations in breast screening uptake (PhD thesis & 2 peer-reviewed papers published) SOSCARE: social services admin data: children and families with long term and complex needs (pending) Child Health System data (gestational age, smoking status and birth weight): lone mothers and socio-economic characteristics (pending) Northern Ireland Cancer Registry data: individual, household and area level deprivation and cancer incidence and survival (pending)

  22. The help provided by the staff of the Northern Ireland Longitudinal Study and the NILS Research Support Unit is acknowledged. The NILS is funded by the Health and Social Care Research and Development Division of the Public Health Agency (HSC R&D Division) and NISRA. The NILS-RSU is funded by the ESRC and Northern Ireland Government. The authors alone are responsible for the interpretation of the data. Acknowledgements

  23. NILS Research Support Unit Northern Ireland Statistics and Research Agency McAuley House Tel: 028 90 348138 Email:nils-rsu@qub.ac.uk Website:nils-rsu.census.ac.uk

  24. Frequencies Tables

  25. Feedback

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