Key Facts
- •Appeal concerning payment to defence solicitors under the Criminal Legal Aid (Remuneration) Regulations 2013.
- •Dispute over the appropriate count of Pages of Prosecution Evidence (PPE) for calculating graduated fees.
- •Appellant represented defendants charged with drug possession; one defendant pleaded not guilty.
- •The Crown's case relied on evidence from seized mobile phones (electronic data).
- •Determining Officer assessed PPE count at 7,086, including 6968 pages of electronic evidence from phones.
- •Appellant argued for inclusion of more electronic data to exceed 10,000 pages, triggering additional fees.
- •Appellant initially argued for inclusion of all phone data; later accepted a percentage-based approach.
- •The core issue was whether the additional electronic evidence was of central importance to the case.
Legal Principles
Calculation of PPE count under Schedule 2 of the 2013 Regulations, particularly paragraphs 1(1)-(5) and 20.
Criminal Legal Aid (Remuneration) Regulations 2013
Inclusion of electronic evidence in PPE count depends on whether it's of 'central importance' to the case, not merely helpful or important.
Lord Chancellor v SVS Solicitors [2017] EWHC 1045 (QB), paragraph 50(viii)
Percentage-based approach to including electronic evidence is appropriate when only part of the data is relevant.
R v Sereika (SCCO 168/13), R v Barrass (SC-2020-CRI-000083), R v. Mucktar Khan (SCCO 2/18), R v Gyamfi [2022] EWHC 2550 (SCCO)
Electronic evidence that has never existed in paper form is not automatically included in the PPE count; the Determining Officer has discretion.
Criminal Legal Aid (Remuneration) Regulations 2013, Schedule 2, paragraph 1(5); Lord Chancellor v SVS Solicitors [2017] EWHC 1045 (QB), paragraph 50(ix)
Outcomes
Appeal dismissed.
The Determining Officer's inclusion of 10% of the phone images was reasonable; most of the data was irrelevant and did not meet the threshold of 'central importance'. The percentage-based approach was deemed inappropriate for the diverse and largely irrelevant data sets.