poisoning in in the population: do we have the tools? Eeva-Katri - - PowerPoint PPT Presentation

Taking action on in intentional self lf- poisoning in in the population: do we have the tools?

Eeva-Katri Kumpula, PhD candidate

School of Pharmacy, University of Otago, Dunedin, New Zealand

Dr Shyamala Nada-Raja

Department of Preventive and Social Medicine, University of Otago, Dunedin, New Zealand

Prof Pauline Norris

School of Pharmacy, University of Otago, Dunedin, New Zealand

Dr Paul Quigley, MB ChB FACEM

Department of Emergency Medicine, Wellington Regional Hospital, Wellington, New Zealand

Structure of presentation

• Background: intentional self-poisoning in New Zealand
• Methods: NZ National Minimum Dataset (NMDS) data
• Results: specific drugs in NMDS data
• Conclusion
• Acknowledgements

Background

• Intentional self-poisoning (ISP): a common form of self-harm

▪ Self-harm: 176.7 per 100,000 population (2013 rate, NZ Ministry of Health, 2016)  ~65% ISP  ~115 per 100,000 ▪ Australia 2010/2011: ~100 per 100,000 hospitalisations due to ISP (public + private; AIHW 2014)

• Overall case fatality rate low, but ISP causes significant population

morbidity and health resource usage

• Some ISP injury events covered by the NZ national personal injury cover,

Accident Compensation Corporation (ACC)

• Interventions to reduce ISP need to be informed by reliable data about

population groups at risk and the substances used

 LIMITING INAPPROPRIATE ACCESS

Methods

• MOH mortality (2000-2012) and morbidity (National Minimum

Dataset, NMDS, public hospitals, stays 24h+, 2000-2014) data  International Statistical Classification of Diseases 10th Revision (ICD- 10) codes X60-X69 (intentional poisoning) and Y10-Y19 (poisoning of undetermined intent, UDP)

▪ Some UDP cases may be ISP

• Analysed by demographic groups
• Level of detail about poisoning agents  nine most commonly seen
• verdose agents at Wellington ED used as ‘indicators’

Results

• Men particularly at risk of fatal ISP and UDP
• Women, young people, Māori, and those living in low-decile areas

particularly at risk of non-fatal ISP and UDP

• No details available about the poisoning agents involved in two thirds
• f deaths in the investigated mortality data
• Public hospital presentation data only had ICD-10 groups of the main

toxicants: most frequently non-opioid analgesics (X60+Y10), psychotropics (X11+Y11)

• Public hospital presentations: ‘indicator substances’ mostly not

identifiable due to ICD-10 group structure

Substance group % of cases Indicator identifiable separately?

4-Aminophenol derivatives (Paracetamol; T39.1) 29% Paracetamol  YES Benzodiazepines (T42.4) 19% Clonazepam, diazepam  NO Ethanol (T51.0) 19% Other and unspecified antidepressants (excl. MAOI; T43.2) 18% Citalopram, fluoxetine  NO Other antiepileptic and sedative-hypnotic drugs (T42.6) 18% Zopiclone  NO Other and unspecified antipsychotics and neuroleptics (T43.5) 13% Quetiapine  NO Other opioids (T40.2) 10% Codeine  NO NSAID (excl. salicylates; T39.3) 10% Ibuprofen  NO

Conclusions

• To reduce harm such as ISP by poisoning agents  restricting

inappropriate access: prescribing practices and over-the-counter availability

• Interventions cannot be planned for whole ICD-10 groups for practical

reasons  nationwide, systematic collection of substance-level data is needed to monitor ISP trends and to inform policy planning for ISP prevention

• Data collection at point of care: ED – automatic pop-up windows
• SNOMED CT

Acknowledgements

• PhD supervisors Prof Pauline Norris and Dr Shyamala Nada-Raja
• Collaborator and mentor Dr Paul Quigley
• New Zealand Ministry of Health – Manatū Hauora  data used in this

study

• Financial support:
• PhD stipend from the School of Pharmacy
• consumables support from School of Pharmacy and Department of Preventive and

Social Medicine

• Dean’s Fund Grant from School of Pharmacy
• Conference trip funding from School of Pharmacy, Division of Health Sciences (UO),

Maurice and Phyllis Paykel Conference Travel Funding