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Trial registered on ANZCTR


Registration number
ACTRN12618001486213p
Ethics application status
Not yet submitted
Date submitted
29/08/2018
Date registered
4/09/2018
Date last updated
4/09/2018
Type of registration
Prospectively registered

Titles & IDs
Public title
Does removing prescription charges reduce hospital stays for people with low incomes and high health needs?
Scientific title
Randomised controlled trial of the impact of removing prescription charges for people with low incomes and high health needs, on hospital bed-days
Secondary ID [1] 295912 0
HRC 18/134
Universal Trial Number (UTN)
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Pharmaceutical funding policy 309387 0
Condition category
Condition code
Public Health 308247 308247 0 0
Health service research

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
Exemption from standard $5 prescription charges for 12 months from 1 Feb 2019. Participants will be issued with an ID card identifying them as being in the study, and community pharmacists will be given a list of study participants in their geographical area. When participants visit community pharmacies to pick up prescribed medicines they will not be required to pay the standard $5 per item prescription charge. They will still have to pay any other charges for their medicines, such as blister packing charges. If participants in the intervention group are asked to pay the $5 charge they will be able to contact the study team via a free phone line.
Intervention code [1] 312238 0
Other interventions
Comparator / control treatment
Usual care. Participants in control group will continue to face standard prescription charges. As is usual in New Zealand, they may sometimes be able to access some forms of one-off assistance with prescription costs.
Control group
Active

Outcomes
Primary outcome [1] 307217 0
All-cause hospital bed-days, as assessed by data linkage with National Minimum Dataset (NMDS)
Timepoint [1] 307217 0
Assessed as a single timepoint, for the 12 months from 1 Feb 2019
Secondary outcome [1] 351146 0
All-cause hospitalisations, from NMDS data
Timepoint [1] 351146 0
Assessed as a single timepoint, for the 12 months from 1 Feb 2019
Secondary outcome [2] 351147 0
Hospitalisations for diabetes from NMDS data;
Timepoint [2] 351147 0
Assessed as a single timepoint, for the 12 months from 1 Feb 2019
Secondary outcome [3] 351148 0
All-cause mortality from NMDS data;
Timepoint [3] 351148 0
Assessed as a single timepoint, for the 12 months from 1 Feb 2019
Secondary outcome [4] 351149 0
Prescription medicines dispensed (number and type (Anatomical Therapeutic Chemical (ATC classifications))
Timepoint [4] 351149 0
Assessed as a single timepoint, for the 12 months from 1 Feb 2019
Secondary outcome [5] 351150 0
Emergency department visits from NMDS data
Timepoint [5] 351150 0
Assessed as a single timepoint, for the 12 months from 1 Feb 2019
Secondary outcome [6] 351151 0
Ambulance call-outs from the St John Ambulance national database and from the Wellington Free Ambulance database
Timepoint [6] 351151 0
Assessed as a single timepoint, for the 12 months from 1 Feb 2019
Secondary outcome [7] 351152 0
SF36 administered by phone
Timepoint [7] 351152 0
At baseline, 6 months and 12 months from 1 Feb 2019
Secondary outcome [8] 351153 0
EQ5D administered by phone. Results will be used to calculate QALYs (Quality Adjusted Life Years)
Timepoint [8] 351153 0
At baseline, 6 months and 12 months from 1 Feb 2019
Secondary outcome [9] 351428 0
Hospitalisations for mental health problems from NMDS data;
Timepoint [9] 351428 0
Assessed as a single timepoint, for the 12 months from 1 Feb 2019

Eligibility
Key inclusion criteria
Living in one of the study areas (Tairawhiti District, Porirua, and Dunedin); living in an area of high deprivation (NZDep 8-10) or homeless; and either having diabetes (for which they take medication) or on-going mental health problems (for which they take antipsychotics)
Minimum age
18 Years
Maximum age
No limit
Gender
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
People who do not consent will be excluded,

Study design
Purpose of the study
Treatment
Allocation to intervention
Randomised controlled trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
Central randomisation by computer algorithm
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Randomisation will be carried out using a computerised algorithm using stydyIDs. It will be done without a stratification.
Masking / blinding
Open (masking not used)
Who is / are masked / blinded?



Intervention assignment
Parallel
Other design features
Phase
Not Applicable
Type of endpoint(s)
Statistical methods / analysis
On average people with diabetes in our study regions spend 1.61 days per year in hospital. This is likely to underestimate the number of bed-days for diabetics in our study sample, who live in areas of high deprivation. People taking antipsychotics have high incidence of hospitalisation (in our as-yet unpublished analysis 38% of people taking antipsychotics were hospitalised in a year) and hospitalisations for mental health tend to be long (average of 17.4 days). Therefore the number of bed-days per person for people living in highly deprived areas and taking antipsychotics is unlikely to be lower than the figure for diabetics. Assuming a conservative rate of 1.61 bed-days per person, a sample size of 943 in each group would give us 80% power to detect a 10% reduction in bed-days in the intervention group. Therefore, we aim to recruit 1000 people in each group to provide sufficient power to assess the primary outcome while allowing for people to withdraw from the study. Participant randomisation will be assessed by comparing baseline characteristics of the two groups using descriptive statistical techniques. The primary outcome (ie, hospital bed-days) will be assessed using a suitable version of regression model in the poisson family (eg; negative binomial regression) to compare two groups during the follow-up period (with 95% confidence intervals) whilst allowing for control of any unbalanced baseline characteristics. Each of the other outcomes measured as counts (i.e., all-cause hospitalisation events, diabetics/mental health hospitalisation events, ED visits) will also be analysed in the same way. Similarly, outcomes measured on continuous scales (eg, EQ-5D) will be compared between two groups using a statistical model such as OLS regression with suitable adjustments (e.g. transformations) if found to be necessary. Binary outcomes (death) will also be analysed in the same way using logistic regression.

These analytical techniques will assess the independent effect of the intervention on each outcome. We will also be able to identify subgroups of the population that are more likely (or unlikely) to benefit from the intervention.

We will also perform a health economic sub-study. Costs will be determined from the perspective of the New Zealand health system (ie expenditure from Vote:Health). This will include expenditure on hospital stays, ED visits and medicines, and ambulance callouts. Unfortunately reliable and complete data are not available for either GP visits or lab tests, but hospital visits are by far the largest expenditure and are the main outcome of interest. Ministry of Health data on publicly funded hospital discharges will be used to calculate the cost of hospital stays.

Incremental cost-effectiveness ratios (ICERs) will be determined; these are the result of dividing the incremental costs by incremental QALYs across the study groups from the payer perspective. The time horizon for the economic analysis will be for the duration of the trial (primary analysis) and modelled out for 10 years (secondary analysis). For the primary analysis, ICERs will be calculated from the collected costs and the QALYs (as determined by the EQ-5D and multiplied by the duration of time of follow-up). The uncertainty in the estimation of the costs and effectiveness will be modelled using nested imputation and bootstrapping. In every economic evaluation, it is important to address how to handle missing data. Any missing data will be assumed to be missing at random and appropriate imputation techniques will be employed. Lastly, the ICER will be determined for each bootstrap sample and each of the 10,000 bootstrapped effect and cost differences (between the treatment and control groups) will be plotted on the cost-effectiveness plane. For the longer time horizon, we will use appropriate decision modeling techniques for health economic evaluation.

Recruitment
Recruitment status
Not yet recruiting
Date of first participant enrolment
Anticipated
Actual
Date of last participant enrolment
Anticipated
Actual
Date of last data collection
Anticipated
Actual
Sample size
Target
Accrual to date
Final
Recruitment outside Australia
Country [1] 20803 0
New Zealand
State/province [1] 20803 0

Funding & Sponsors
Funding source category [1] 300509 0
Government body
Name [1] 300509 0
Health Research Council of New Zealand
Address [1] 300509 0
PO Box 5541, Wellesley Street, Auckland 1141
Country [1] 300509 0
New Zealand
Funding source category [2] 300510 0
Government body
Name [2] 300510 0
Pharmaceutical Management Agency (Pharmac)
Address [2] 300510 0
Level 9, Simpl House, 40 Mercer St, Wellington, 6011
Country [2] 300510 0
New Zealand
Primary sponsor type
Individual
Name
Professor Pauline Norris
Address
Centre for Pacific Health
Va'a o Tautai
Division of Health Sciences
University of Otago
PO Box 56
Dunedin 9054
New Zealand
Country
New Zealand
Secondary sponsor category [1] 299985 0
None
Name [1] 299985 0
Address [1] 299985 0
Country [1] 299985 0

Ethics approval
Ethics application status
Not yet submitted
Ethics committee name [1] 301301 0
Health and Disability Ethics Committee (HDEC)
Ethics committee address [1] 301301 0
Ministry of Health
PO Box 5013
Wellington 6140
Ethics committee country [1] 301301 0
New Zealand
Date submitted for ethics approval [1] 301301 0
24/09/2018
Approval date [1] 301301 0
Ethics approval number [1] 301301 0

Summary
Brief summary
Although prescription charges in New Zealand are low compared with many other countries, many people report that they cannot afford the medicines they need. We plan to conduct a randomised controlled trial of prescription charges to see whether removing charges would improve people’s health. We will recruit a group of people who have diabetes and/or ongoing mental health conditions requiring medication, and live in deprived neighbourhoods. We will divide the group in half and pay prescription charges for one group for twelve months. We will then compare how many days people from each group spend in hospital to see whether free prescriptions make a difference. The differences in use of health services, quality of life, and medicines use between the groups will also be investigated.
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 86554 0
Prof Pauline Norris
Address 86554 0
Centre for Pacific Health
Division of Health Sciences
University of Otago
PO Box 56
Dunedin 9054
New Zealand
Country 86554 0
New Zealand
Phone 86554 0
+64 27 4809595
Fax 86554 0
Email 86554 0
pauline.norris@otago.ac.nz
Contact person for public queries
Name 86555 0
Dr Kim Cousins
Address 86555 0
Centre for Pacific Health
Division of Health Sciences
University of Otago
PO Box 56
Dunedin 9054
New Zealand
Country 86555 0
New Zealand
Phone 86555 0
+64 3 479 8493
Fax 86555 0
Email 86555 0
kim.cousins@otago.ac.nz
Contact person for scientific queries
Name 86556 0
Prof Pauline Norris
Address 86556 0
Centre for Pacific Health
Division of Health Sciences
University of Otago
PO Box 56
Dunedin 9054
New Zealand
Country 86556 0
New Zealand
Phone 86556 0
+64 27 4809595
Fax 86556 0
Email 86556 0
pauline.norris@otago.ac.nz

No data has been provided for results reporting
Summary results
Not applicable