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Trial registered on ANZCTR


Registration number
ACTRN12619000966190
Ethics application status
Approved
Date submitted
20/06/2019
Date registered
9/07/2019
Date last updated
16/04/2024
Date data sharing statement initially provided
9/07/2019
Type of registration
Prospectively registered

Titles & IDs
Public title
Scale-up of a primary care intervention for cardiovascular risk management in Malang, Indonesia
Scientific title
Scale-up of a primary care intervention for cardiovascular risk management in Malang, Indonesia
Secondary ID [1] 298556 0
APP1169763
Universal Trial Number (UTN)
U1111-1235-1332
Trial acronym
SMARThealth-SU
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Cardiovascular disease 313382 0
Condition category
Condition code
Public Health 311818 311818 0 0
Health service research
Cardiovascular 311817 311817 0 0
Hypertension

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
SMART (Systematic Medical Appraisal, Referral and Treatment) health is a technology-enabled ecosystem that aims to improve the delivery of consistent high-quality essential primary healthcare to communities. SMARThealth cardiovascular disease (CVD) supports the provision of preventive CVD care at the community and household level by strengthening existing health systems. In Indonesia, the SMARThealth CVD program is a complex intervention with multiple components, each of which includes a component of digital support using mobile devices:

• Raising community awareness through strengthening existing programs:
Through a government-funded chronic disease management program at village and neighbourhood-level primacy health care centres, community health workers (kaders) and nurses raise community awareness of CVD and its risk factors. Activities occur on an approximately monthly basis for 24 months and include role play, traditional theatre and doctor-led group education sessions of 20 minutes duration. A guided 1 hour physical activity demonstration, led by Kaders and nurses, occurs once a week for 24 months in all villages. These activities will be strengthened by the training of health care providers to improve knowledge about CVD and associated risk factors (see ‘Training and performance management’ below), and by individualised patient counselling provided by kaders during monthly household visits using a risk communication tool with pre-recorded animations on the SMARThealth application (see below).

• Training and performance management of health care providers:
Kaders participate in one intensive 5-day face-to-face training programme led by study researchers together with the Malang District Health Authority’s (DHA) SMARThealth implementation team, with subsequent ongoing remote or in-person support from district-level field supervisors. The training session consists of modules to improve knowledge about CVD and associated risk factors, as well as the technical use of the SMARThealth platform (mobile tablet, SMARThealth application and basic medical equipment) for the identification, referral and follow-up of patients at high predicted CVD risk. In one 3-day face-to-face workshop (again led by study researchers together with Malang DHA SMARThealth implementation team), primary care doctors and nurses are provided guidance in the use of the electronic data transmitted by the kader, interpretation of the decision support output from the SMARThealth application for disease and risk management, and use of audit and feedback capabilities. Regular monthly meetings of kaders at the village level are used for problem resolution dring the 24 months intervention period. Kaders, nurses and doctors all receive two automated pre-recorded voice messages by mobile phone each month for 24 months reinforcing SMARThealth procedures.

• CVD risk assessment with clinical decision support:
As part of their routine duties, kaders perform household visits and invite all household members aged 40 years and above to participate. Those who agree undergo CVD risk assessment through a clinical decision support system on a 7-inch Android tablet device using an Android 4.1 operating system. This application prompts the kader to collect basic sociodemographic information, as well as a relevant personal and family health history including medication use. The kaders also use standardized equipment to measure height and weight, and an automated sphygmomanometer to record blood pressure (BP; Omron HEM7130). Three BP measurements are recorded, with the average of the last two considered by the clinical decision support system. Random capillary blood glucose levels are also measured using a Freestyle Optium Neo blood glucose monitoring system, with a value of greater than or equal to 200 mg/dL (11.1 mmol/L) considered by the clinical decision support system to be consistent with diabetes in those without a prior diagnosis. The clinical decision support system then identifies individuals considered at high predicted CVD risk, defined by the presence of any of the following: (1) a past history of CVD confirmed by a doctor diagnosis; or (2) an extreme BP elevation (SBP greater than 160 mmHg or DBP greater than 100 mmHg); or (3) a 10-year predicted CVD risk greater than or equal to 30%; or (4) a 10-year predicted CVD risk of 20-29% and a SBP greater than 140 mmHg. In the absence of Indonesian guidelines, the 10-year risk of fatal or major non-fatal major CVD event (myocardial infarction or stroke) is automatically estimated using algorithms based on the World Health Organization/International Society of Hypertension “low information” risk charts tailored to the South-East Asian Region-B, which recommends screening individuals aged 40 years and above and uses age, sex, blood pressure, smoking and diabetes status.

Based on the clinical decision support system output, kaders are prompted to provide individualised lifestyle advice and refer all high-risk individuals to nurses or doctors at the primary health care centre for consideration of preventive medication prescription. The clinical decision support system for doctors and nurses is similar to that provided to the kaders, but also provides recommendations for medication use. Unless contraindicated, prescription of a BP lowering drug, a statin and aspirin is recommended for patients with a past history of doctor-diagnosed CVD, while a combination of a BP lowering drug and a statin is recommended for all other high-risk individuals. High-risk individuals are automatically referred back to the kader for follow-up in the community to support lifestyle and medication adherence. The decision support algorithm prioritizes individuals for follow-up depending on the estimated absolute risk (monthly for patients with CVD and/or estimated absolute risk greater than or equal to 30%; every year for those with estimated risk 20-30%). The prioritization algorithm also considers other factors, including whether or not the high-risk individual has seen a doctor following referral; has been prescribed medications; has achieved target BP; or is a current smoker. Priority listings for follow-up are provided to kaders. At each encounter with a kader, nurse or doctor, additional decision support is generated for those not achieving target BP or with high random blood glucose levels. Routine cholesterol screening is not available in this context. On average, an initial screening requires approximately 30 minutes, with 10 minutes for kader follow-up visits. All data collected by kaders, nurses and doctors through the SMARThealth application are uploaded into a shared electronic medical record (OpenMRS) via the Sana Mobile Dispatch Server and stored on a central server. This allows doctors and nurses to view data acquired by kaders and for kaders to view the treatment recommendations made by doctors and nurses. All aspects of this intervention component occur for the duration of the 24 month intervention period.

• Patient engagement:
High risk patients who have been prescribed medications are provided with a medication calendar each month for the 24 month intervention period to record and track daily medication use over a one-month period. During their monthly follow-up visit, kaders will assess medication adherence through observation of medication packets and review and replace the medication calendar. In addition to the monthly follow-up visits by the kaders, automated pre-recorded personalized voice messages are sent to the mobile phone of high-risk patients to promote lifestyle changes, medication adherence and medical follow-up. Two messages are sent every week, with one conveying the advantages of healthy lifestyle changes and the other targeting patient-specific issues such as reminders for a doctor visit, or for adherence to a specific medication.
Intervention code [1] 314810 0
Early detection / Screening
Intervention code [2] 314920 0
Treatment: Devices
Intervention code [3] 314895 0
Prevention
Comparator / control treatment
No control group
Control group
Uncontrolled

Outcomes
Primary outcome [1] 320489 0
The proportion of the target population (adults aged 40 years and above) screened by kaders. Assessed by analysis of de-identified data extracts from the District Health Administration electronic health records.
Timepoint [1] 320489 0
On completion of 24 month implementation phase
Primary outcome [2] 320490 0
The proportion of high risk patients referred to and seen by a prescribing nurse or doctor. Assessed by analysis of de-identified data extracts from the District Health Administration electronic health records.
Timepoint [2] 320490 0
On completion of 24 month implementation phase
Primary outcome [3] 320491 0
The proportion of high risk patients with at least 1 follow-up visit by a kader. Assessed by analysis of de-identified data extracts from the District Health Administration electronic health records.
Timepoint [3] 320491 0
On completion of 24 month implementation phase
Secondary outcome [1] 371782 0
Primary outcome: Change in the proportion of high risk patients on blood pressure lowering therapy from time of screening to end of the implementation period. Assessed by analysis of de-identified data extracts from the District Health Administration electronic health records.
Timepoint [1] 371782 0
On completion of 24 month implementation phase
Secondary outcome [2] 371786 0
The cost-effectiveness of implementing SMARThealth relative to usual care. Modelled analyses of costs of delivering the intervention (staffing, equipment), hospital admissions, primary care presentations and pharmaceuticals compared to benefits using estimates of cardiovascular disease events avoided based on before-after differences in drug prescriptions.
Timepoint [2] 371786 0
On completion of 24 month implementation phase
Secondary outcome [3] 371783 0
Primary outcome: Change in mean systolic and diastolic blood pressure levels among high risk patients from time of screening to end of the implementation period. Assessed by analysis of de-identified data extracts from the District Health Administration electronic health records.
Timepoint [3] 371783 0
On completion of 24 month implementation phase
Secondary outcome [4] 371781 0
Primary outcome: Change in the proportion of high risk patients on optimal CVD preventive therapy from time of screening to end of implementation period. Assessed by analysis of de-identified data extracts from the District Health Administration electronic health records.
Timepoint [4] 371781 0
On completion of 24 month implementation phase.
Secondary outcome [5] 372158 0
The extent to which further localisation of the intervention was important. Assessed by transcription and thematic analysis of in-depth interviews and focus group discussions.
Timepoint [5] 372158 0
Assessed on completion of 24 months implementation phase
Secondary outcome [6] 372159 0
The strategies necessary to facilitate continuous adoption and scale-up beyond the study location. Assessed by transcription and thematic analysis of in-depth interviews and focus group discussions.
Timepoint [6] 372159 0
Assessed on completion of 24 months implementation phase
Secondary outcome [7] 371784 0
Primary outcome: Change in proportion of high risk patients achieving a systolic blood pressure target of less than 140 mmHg from time of screening to end of the implementation period. Assessed by analysis of de-identified data extracts from the District Health Administration electronic health records.
Timepoint [7] 371784 0
On completion of 24 month implementation phase
Secondary outcome [8] 371787 0
The contextual factors that influenced scale-up. Assessed by transcription and thematic analysis of in-depth interviews and focus group discussions.
Timepoint [8] 371787 0
Assessed on completion of 24 months implementation phase
Secondary outcome [9] 371785 0
The resources required to undertake scale up based on analyses of financial records of individual facilities.
Timepoint [9] 371785 0
On completion of 24 month implementation phase

Eligibility
Key inclusion criteria
Community-members aged 40 years and above
Minimum age
40 Years
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
No exclusion criteria

Study design
Purpose of the study
Prevention
Allocation to intervention
Non-randomised trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
N/A
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
N/A
Masking / blinding
Open (masking not used)
Who is / are masked / blinded?



Intervention assignment
Single group
Other design features
Phase
Not Applicable
Type of endpoint/s
Efficacy
Statistical methods / analysis
A before-after design will be adopted as reliable data for interrupted time series analyses (especially in the pre-implementation phase) are not available. Outcomes will be evaluated overall and in subgroups defined by age, sex, socioeconomic status and location. Data will be summarised using descriptive statistics. Before-after differences will be assessed using paired t-test or the Wilcoxon signed-rank test, as appropriate, for continuous variables. Binary outcomes will be compared with McNemar’s test. Since the data will be hierarchically structured with participants clustered within villages, multilevel models will be fitted with participants at level-1 and communities at level-2 to correct for non-independence of observations due to geographic clustering. This multilevel approach also allows for examining variation at the village-level using existing administrative data.

A modelled cost-effectiveness analysis will be conducted to identify the resource use implications of implementing SMARThealth relative to usual care. Data on the costs of delivering SMARThealth (staffing and equipment), as well as those associated with service utilisation including hospital admissions, primary care presentations and pharmaceuticals will be collected. Benefits of the intervention will be modelled using estimates of CVD events avoided based on before-after differences in drug prescriptions, given that regression to the mean will affect estimates of blood pressure differences due to the intervention. The analysis will include a cost-modelling exercise in which we track unit costs associated with the expansion of the program and potential economies of scale. It is hypothesised that due to the rationalisation of upfront training and development costs and increased staff productivity through learning by doing, cost effectiveness will improve as scale is expanded. DALYs averted will be estimated using published Indonesian Burden of Disease estimates. Costs and health outcomes will be modelled over five years. Using the costs of business-as-usual care obtained from our previous work and those associated with SMARThealth in this project, the incremental cost per DALY gained will be estimated. Bootstrapping will be used to estimate a distribution around costs and health outcomes, and to calculate the confidence intervals around the incremental cost effectiveness ratio. One-way sensitivity analysis will be conducted around key cost variables, and a probabilistic sensitivity analysis will be conducted to estimate the joint uncertainty in all parameters. A cost-effectiveness acceptability curve will be plotted to determine the likelihood of the intervention being considered cost-effective by Indonesian policy-makers given prevailing cost-effectiveness thresholds for the country and the uncertainty of results.

Recruitment
Recruitment status
Recruiting
Date of first participant enrolment
Anticipated
1/02/2021
Actual
20/02/2021
Date of last participant enrolment
Anticipated
31/05/2024
Actual
Date of last data collection
Anticipated
31/05/2024
Actual
Sample size
Target
275000
Accrual to date
148801
Final
Recruitment outside Australia
Country [1] 21625 0
Indonesia
State/province [1] 21625 0
Malang

Funding & Sponsors
Funding source category [1] 303098 0
Government body
Name [1] 303098 0
National Health & Medical Research Council
Country [1] 303098 0
Australia
Primary sponsor type
Other
Name
The George Institute for Global Health
Address
Level 18, International Towers 3, 300 Barangaroo Ave, Barangaroo NSW 2000 Australia
Country
Australia
Secondary sponsor category [1] 303088 0
None
Name [1] 303088 0
Address [1] 303088 0
Country [1] 303088 0
Other collaborator category [1] 280753 0
University
Name [1] 280753 0
University of Brawijaya
Address [1] 280753 0
Jl. Veteran Malang
Ketawanggede
Kec. Lowokwaru
Kota Malang, East Java 65145
Country [1] 280753 0
Indonesia
Other collaborator category [2] 280754 0
Other
Name [2] 280754 0
The George Institute for Global Health, India
Address [2] 280754 0
311-312, Third Floor, Elegance Tower
Plot No. 8, Jasola District Centre
New Delhi 110025
Country [2] 280754 0
India
Other collaborator category [3] 280758 0
Government body
Name [3] 280758 0
BPJS Kesehatan
Address [3] 280758 0
Jalan Letjend Suprapto No. 11
RT. 10 / RW. 7
Cempaka Putih Timur
Jakarta Pusat
DKI Jakarta 1064
Country [3] 280758 0
Indonesia
Other collaborator category [4] 280757 0
Government body
Name [4] 280757 0
Malang District Health Agency
Address [4] 280757 0
Jl. Panji No.120
Kec. Kepanjen
Malang, East Java 65163
Country [4] 280757 0
Indonesia
Other collaborator category [5] 280755 0
University
Name [5] 280755 0
University of Sydney
Address [5] 280755 0
Camperdown
NSW 2006
Country [5] 280755 0
Australia
Other collaborator category [6] 280756 0
University
Name [6] 280756 0
University of Manchester
Address [6] 280756 0
Oxford Rd
Manchester M139PL
Country [6] 280756 0
United Kingdom

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 303643 0
University of Brawijaya Human Research Ethics Committee
Ethics committee address [1] 303643 0
Ethics committee country [1] 303643 0
Indonesia
Date submitted for ethics approval [1] 303643 0
03/07/2019
Approval date [1] 303643 0
05/09/2019
Ethics approval number [1] 303643 0
236/EC/KEPK/09/2019
Ethics committee name [2] 303642 0
University of New South Wales Human Research Ethics Committee
Ethics committee address [2] 303642 0
Ethics committee country [2] 303642 0
Australia
Date submitted for ethics approval [2] 303642 0
08/07/2019
Approval date [2] 303642 0
18/09/2019
Ethics approval number [2] 303642 0
HC190531

Summary
Brief summary
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 94370 0
Prof Anushka Patel
Address 94370 0
The George Institute for Global Health
Level 5, 1 King St, Newtown NSW 2042
PO Box M201, Missenden Rd, NSW 2050
Country 94370 0
Australia
Phone 94370 0
+61 2 8052 4439
Fax 94370 0
Email 94370 0
apatel@georgeinstitute.org
Contact person for public queries
Name 94371 0
Anna Palagyi
Address 94371 0
The George Institute for Global Health
Level 5, 1 King St, Newtown NSW 2042
PO Box M201, Missenden Rd, NSW 2050
Country 94371 0
Australia
Phone 94371 0
+61 2 8052 4334
Fax 94371 0
Email 94371 0
apalagyi@georgeinstitute.org.au
Contact person for scientific queries
Name 94372 0
Anna Palagyi
Address 94372 0
The George Institute for Global Health
Level 5, 1 King St, Newtown NSW 2042
PO Box M201, Missenden Rd, NSW 2050
Country 94372 0
Australia
Phone 94372 0
+61 2 8052 4334
Fax 94372 0
Email 94372 0
apalagyi@georgeinstitute.org.au

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
No
No/undecided IPD sharing reason/comment


What supporting documents are/will be available?

Doc. No.TypeCitationLinkEmailOther DetailsAttachment
2686Ethical approval  apalagyi@georgeinstitute.org.au


Results publications and other study-related documents

Documents added manually
No documents have been uploaded by study researchers.

Documents added automatically
No additional documents have been identified.