In-depth Study of the Cost-effectiveness of the Risk Assessment and Management Programme for Hypertension (RAMP-HT) for Patients With Uncontrolled Hypertension in Primary Care in Hong Kong
Conditions: Hypertension, Cost Effectiveness
Sponsor: The University of Hong Kong

 
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HKW IRB study protocol - In-depth study of the cost-effectiveness of the Risk 
Assessment and Management Programme for Hypertension (RAMP-HT) for 
patients with uncontrolled hypertension in primary care in Hong Kong 
(Research protocol number: 1 (4 Mar 2015)) 
 
PROPOSED RESEARCH PROJECT 
a Title:  
 In-depth study of the cost-effectiveness of the Risk Assessment and Management 
Programme for Hypertension (RAMP-HT) for patients with uncontrolled 
hypertension in primary care in Hong Kong 
  
b Introduction:  
 Hypertension (HT) is an important risk factor for stroke, coronary heart disease 
(CHD), heart failure and renal diseases1, and the leading risk factor of global 
disease burden2. A multitude of interventions have proven efficacy in lowering 
blood pressure and reducing long term HT complications, including pharmacologic 
treatment, DASH diet (Dietary Approaches to Stop Hypertension), exercise, weight 
reduction, smoking cessation, alcohol moderation and self-monitoring of blood 
pressure1 
3 
4. A few studies had examined the effects of coordinated 
multidisciplinary management programmes for HT patients to maximize the 
effectiveness of these interventions, but the long term benefit or cost-effectiveness 
of such programmes are uncertain5. 
 
In Hong Kong, over 200,000 patients with HT are being managed in public primary 
care clinics (i.e. General Out-patient Clinics (GOPCs) and Family Medicine Clinics 
(FMC)) under the Hospital Authority (HA); but more than 45% HT patients have 
still not achieved target blood pressure control (HA Internal communication). In 
order to improve the quality of care for patients with uncontrolled HT at GOPCs, 
the evidence-based 
1 
4 
6, structured protocol-driven multidisciplinary Risk 
Assessment and Management Programme – Hypertension (RAMP-HT) was 
launched since October 2011 by the HA with the support from the Food and 
Health Bureau. Standardized cardiovascular risk factor assessment, hypertensive 
complication screening and assessment on patient adherence to treatment are 
carried out on enrolled patients (Appendix A). Patients are stratified into low, 

 
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medium or high risk groups according to the 10-year cardiovascular disease 
(CVD) risk calculated from their relevant risk factors by the Joint British Society 
2005 Equation4. A multidisciplinary team comprised of doctors, nurses, dieticians, 
physiotherapists and/or occupational therapists would then deliver individualized 
management targeted to the patient’s risk factors according to standardized risk-
stratified guidelines (Appendix B). 
 
In our evaluation of quality of care of RAMP-HT as part of the Extended Study on 
Evaluation of Quality of Care of Chronic Disease Management and Public-Private 
Partnership Programmes of the HA (HMRF Reference no: EPC_HKU-2), we have 
found that, among patients whose baseline blood pressure was not controlled 
(≥140/90mmHg), RAMP-HT patients had significantly greater reduction in mean 
systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C) and 
estimated 10-year CVD risk after 12 months than matched HT patients receiving 
usual care from GOPCs (p<0.001 for all parameters). In addition, a significantly 
greater proportion of patients in the RAMP-HT group achieved target blood 
pressure (i.e. SBP/DBP<140/90mmHg) after 12 months compared to usual care 
group; the Number Need to Treat (NNT) for achieving one more target blood 
pressure was 14. These preliminary findings on improvement in blood pressure 
and CVD risks are very encouraging, suggesting that RAMP-HT may be effective 
in preventing cardiovascular and renal complications in primary care HT patients. 
  
c Aim, objectives and Hypotheses: 
 Aim and objectives: 
The aim of this study is to evaluate the cost-effectiveness of the RAMP-HT of the 
HA in primary care patients with uncontrolled hypertension. 
 
The objectives are to 
1. Evaluate the long term (5-year) effectiveness of RAMP-HT compared to usual 
care in reducing cardiovascular complications, end-stage renal disease and 
all-cause mortalities in a cohort of primary care patients with uncontrolled 
hypertension at baseline 
2. Estimate the direct medical cost of RAMP-HT and other health services 

 
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among primary care HT patients with or without complications  
3. Evaluate the cost-effectiveness of RAMP-HT, compared to usual care, in 
gaining one Quality-Adjusted Life Year (QALY) in primary care patients with 
uncontrolled hypertension  
 
Hypotheses: 
1. 
RAMP-HT is more effective in reducing 5-year cardiovascular complications, 
end stage renal disease and all-cause mortality among primary care patients 
with uncontrolled hypertension compared to usual care  
2. 
The direct medical cost of RAMP-HT patients, for the same disease 
complication status, is not higher than that of usual care except for the 
RAMP-HT cost 
3. 
The direct medical cost of HT patients with one or more complications is 
higher than that of HT patients without any complication 
4. 
RAMP-HT is cost-effective compared to usual care, i.e. the incremental cost-
effectiveness ratio (ICER) per QALY gained is below the threshold value of 1 
annual GDP (Gross Domestic Product) per capita of Hong Kong 
(HK$295,303, 20137), which is the benchmark recommended by the World 
Health Organization8  
  
d Plan of Investigation: 
 (i)  
Study design  
  
A retrospective study on public primary care population-based matched 
cohorts of patients with uncontrolled hypertension in Hong Kong  
 
 (ii) 
Subjects 
All patients who have enrolled into the RAMP-HT between 1 Oct 2011 and 
31 March 2012, fulfilled the following inclusion criteria and without any 
exclusion criterion will be included in the RAMP-HT cohort. The same 
number of matched patients receiving usual care in GOPCs who have never 
enrolled into RAMP-HT on or before 31 March 2017, fulfilled the following 
inclusion criteria and without any exclusion criterion will be randomly 
selected from the HA clinical management system (CMS) to form the usual 

 
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care cohort.  
 
Inclusion criteria 
1. Age ≥18 years old and <80 years old 
2. Coded with ICPC-2 of K86 on or before baseline* (*date of RAMP-HT 
enrolment for RAMP-HT cohort, and 31 March 2012 for usual care 
cohort)  
3. Had uncontrolled blood pressure (i.e. average Systolic Blood Pressure 
(SBP)≥140mmHg 
OR 
Diastolic 
Blood 
Pressure 
(DBP)≥90mmHg 
between 6 months before and 3 months after baseline*)  
 
Exclusion criteria 
1. Patients who had a diagnosis of any HT complications defined by 
relevant ICPC-2 and/or ICD-9-CM diagnosis codes (defined in the 
“Methods” section) on or before baseline* 
2. Patients diagnosed to have Diabetes Mellitus (DM) on or before 31 
March 2017, defined by ICPC-2 codes of T89 or T90 
3. Patients exclusively managed by Specialist Out-Patient Clinic (SOPC) on 
or before baseline* 
 
To reduce selection bias, the RAMP-HT and usual care subjects will be 
matched in baseline covariates using the propensity score matching 
method: 
 
Baseline Covariates 
The covariates are categorized into demographics, co-morbidities, clinical 
parameters and treatment modalities. Demographics include sex, age, 
smoking status and whether the patient is a recipient of Comprehensive 
Social Security Assistance (CSSA). Co-morbidities include history of chronic 
lung disease, cancer or psychological condition indicated by the presence of 
the relevant ICPC-2 or ICD-9-CM codes in the CMS. These factors reflect 
the patient’s baseline health status. Clinical parameters data including blood 
pressure (systolic and diastolic), lipid profile (including triglyceride level) and 

 
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body mass index (BMI) and treatment modalities including hypertensive 
drug(s) and lipid-lowering agent(s) used account for HT disease severity 
and individual CVD risk. 
 
Propensity Score Matching 
Propensity score matching was first introduced in 19839. The technique 
summarises relevant baseline characteristics of each eligible HT patient into 
a single-index variable (the propensity score); HT patients in usual care will 
be matched to patients in the RAMP-HT group based on the value of the 
propensity score9-11. The propensity score will be generated for each patient 
by logistic regression, modelling the RAMP-HT as dependent variable and 
baseline covariates of patients as independent variables. The propensity 
score mapping will be performed by using the “psmatch2” command12 with 
one-to-one matching without replacement and a callipers of 0.001 approach 
in the STATA.  
 
  
Sample size calculation 
a. For the long-term effectiveness evaluation, the difference in 5-year 
incidence of CVD between the RAMP-HT and usual care group is the 
primary outcome. From the result of our RAMP-HT QOC study, the 
observed 1-year incidence of CVD in the RAMP-HT and usual care 
group was 1.33% and 1.76% respectively. A conservative estimate of 
the 5-year incidence of CVD in RAMP-HT and usual care group would 
be 7% and 8%, respectively. A minimum sample size of 7,116 subjects 
from each group will be needed to detect a difference of 1% in incidence 
rates of CVD between RAMP-HT and usual care group with 90% power 
and at 5% level of significance18. 
 
We have identified from the HA CMS data set of the RAMP-HT QOC 
study 8,681 eligible RAMP-HT patients and 8,681 usual care patients 
who fulfilled the inclusion criteria and without any exclusion criterion 
matched by propensity score matching. These two matched cohorts will 
be the subjects of this long-term effectiveness and cost-effectiveness 

 
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study. The flow chart of subject selection is attached in Appendix C. 
 
b. For the costing analysis, 140 subjects from each of the RAMP-HT and 
usual care groups will be required to show that the direct medical costs 
of RAMP-HT patients (excluding RAMP cost) is not higher than that of 
usual care HT patients by independent one side t-test with 0.3 effect size 
and 80% power at 5% significance level.  
 
307 RAMP-HT and 291 usual care patients had been recruited from May 
2013 to March 2014 in GOPCs across Hong Kong for the longitudinal 
evaluation of patient reported outcomes (PRO) in the RAMP-HT QOC 
study. These patients will be follow-up from May 2015 to March 2016 as 
part of our RAMP-HT QOC study; telephone survey on private health 
service utilization will be conducted at the same time. Taking 25% 
attrition rate into account, 230 RAMP-HT and 218 usual care patients 
will complete the questionnaire. This sample will be adequate for the 
comparison of private medical cost between RAMP-HT and usual care 
group. 
  
 (iii) 
Outcome Measures 
 
The primary outcomes of this study are: 
1. The 5-year incidence of CVD among RAMP-HT and usual care HT 
patients  
2. The direct medical costs of RAMP-HT and usual care HT patients with 
and without complications  
3. The ICER of cost per QALY gained by RAMP-HT compared to usual 
care group 
 
The secondary outcomes of this study are: 
4. The 5-year incidence of end stage renal disease (ESRD) and all-cause 
mortality among RAMP-HT and usual care HT patients  
5. The hazard ratio of CVD, ESRD and all-cause mortalities between 

 
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RAMP-HT and usual care group 
6. The Number-Need-to-Treat (NNT) to reduce one CVD, ESRD and 
mortality in 5 years by RAMP-HT 
 
 (iv)  
Data Collection  
 
HA CMS Data Extraction of Incidence of HT Complications and All-
cause Mortality  
 
Our RAMP-HT QOC study will have collected anonymous 3-year follow up 
HA CMS data of the cohorts available by November 2015. This study plans 
to extend the follow up period for an additional 2 years to measure the 5-
year incidence of CVD, ESRD and mortality outcomes of these HT patient 
cohorts from 1 Oct 2011 to 31 March 2017. The data extraction will be 
carried out by the HA statistics team. Each patient in the cohorts (RAMP-HT 
and usual care groups) will be observed from baseline to the first 
occurrence of any of the outcome events until 31 March 2017, with CMS 
documented diagnoses defined by the relevant ICPC-2 and ICD-9-CM 
codes below: 
 
1. Cardiovascular disease (CVD) is defined as the presence of any of 
coronary heart disease (CHD), heart failure or stroke. CHD includes all 
ischaemic heart disease, myocardial infarction (MI), coronary death or 
sudden death as indicated by the ICPC-2 K74 toK76 or ICD-9-CM 410.x, 
411.x to 414.x, 798.x codes. Heart failure is defined as the ICPC-2 K77 
or ICD-9-CM 428.x codes. Stroke (fatal and non-fatal stroke) is defined 
by the ICPC-2 K89 toK91 or ICD-9-CM 430.x to 438.x codes.  
2. End Stage Renal Disease (ESRD) is defined by any of ICD-9-CM 
250.3x, 585.x, 586.x, or an estimated Glomerular Filtration Rate (eGFR) 
<15mL/min/1.73m2, according to the definition of the National Kidney 
Foundation13.  
3. Mortality is defined by a documented death in the Hong Kong Death 
Registry. 

 
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The definitions of each HT complication are determined by the clinician 
co-investigators and endorsed by clinicians of the HA. 
 
Direct Medical Costing Studies  
 
The costing studies will be evaluated from the healthcare provider’s 
perspective. 
 
(i) In-depth costing of RAMP-HT 
The cost of RAMP-HT comprises of three components: (a) set-up costs; (b) 
ongoing intervention costs; and (c) central administrative costs. Set-up 
costs refer to one-off expenses incurred in the course of setting up the 
programme, which include costs related to staff training, additional 
equipment, information technology and infrastructure. Ongoing intervention 
costs refer to the recurrent costs for the programme operation and 
maintenance, which include costs of staff, printing and consumables in the 
clinics. The evaluation of the set-up costs will be primarily based on the data 
reported by the clusters, who are responsible for maintaining detailed 
inventory of supplies purchased and distributed to the clinics. Central 
administrative costs refer to recurrent costs incurred by the GOPCs for 
running RAMP-HT (i.e. project team cost in Head Office). 
 
The set-up and intervention costs will be collected by two sets of 
questionnaires to be completed by RAMP-HT programme co-ordinators of 
the HA clusters and GOPCs that provide the RAMP-HT service (Appendix 
D); The central administration costs will be collected from the Finance Office 
in HA Head Office by a structured questionnaire. These questionnaires are 
adapted from the costing questionnaires that were used in our in-depth 
study on the cost-effectiveness analysis of RAMP-DM that had proven 
feasibility and validity (HHSRF # EPC_HKU-1A). 
 
(ii) Direct Medical Costs of HT patients 

 
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The medical cost analysis will focus on the direct medical costs involved in 
the care of HT patients without or with specific HT complications (i.e. CHD, 
heart failure, stroke and ESRD) in both public and private healthcare 
sectors; indirect cost (e.g. opportunity cost) and non-medical costs (e.g. 
transportation cost, carer cost) will not be measured.  
 
Public medical costs will be estimated from products of the unit costs 
(published in the HKSAR Government Gazette and Hospital Authority 
ordinance (Chapter 113) of charges for non-entitled persons) and the 
utilization rates of 1) dispensed drugs (note: self-financed drugs will be 
considered as private medical cost, see below), 2) laboratory tests and 
investigations, 3) healthcare services including general outpatient clinics 
(GOPC), specialist outpatient clinics (SOPC), allied health services (e.g. 
dietician, 
physiotherapist 
or 
occupational 
therapist), 
accident 
and 
emergency (A&E) department and 4) admissions to hospital in the 12 
months before baseline and at 12, 24, 36, 48 and 60 months after study 
enrolment for each patient in the study cohorts. The data extraction will be 
carried out by the HA statistics team. 
 
Private direct medical costs include the cost of all private Western doctor 
and Chinese medicine practitioner consultations, private hospitalisation and 
self-medications inclusive of self-financed medications prescribed by the 
HA. 598 HT patients (307 RAMP-HT patients and 291 usual care patients) 
who were recruited from HA GOPCs for the longitudinal evaluation of 
patient reported outcomes (PRO) in the evaluation of QOC RAMP-HT study 
from May 2013 to March 2014 will be invited to complete a “private medical 
cost of HT questionnaire” (Appendix E) by telephone.  The questionnaire is 
adapted from the costing questionnaire that was used in our in-depth study 
on the cost-effectiveness analysis of RAMP-DM that had proven feasibility 
and validity (HHSRF # EPC_HKU-1A).  
 
Health Preference of HT Patients 
 

 
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Health-related quality of life (HRQOL) measured by the SF-12v2 collected in 
our previous PRO surveys of the evaluation of QOC of RAMP-HT study 
from May 2013 to March 2014 will be converted to SF-6D health preference 
values by the HK algorithm14 15 for HT patients with or without complications 
(CVD, end stage renal disease). 
 
#Our team has successful collaboration with the HA statistics Department to 
extract the relevant data as part of our evaluation of QOC and short-term 
effectiveness 
of 
RAMP-HT 
study 
(HHSRF 
commissioned 
project 
EPC_HKU-2). The detailed data schema is attached in the Appendix F. 
 
 
(v) Data processing and analysis 
 
Long Term Effectiveness of RAMP-HT 
 
Descriptive statistics will be performed on the clinical parameters (e.g. 
blood pressure (BP), lipid profile, BMI) and incidence of CHD, heart 
failure, stroke, ESRD, HT-related mortality and all-cause mortalities of the 
RAMP-HT and usual care cohorts. Differences in each outcome between 
groups will be tested using independent t-tests for continuous variables 
such as mean BP, or chi-square tests for categorical variables such as 
proportion of patients with BP control or CVD incidence. The number 
needed to treat to reduce one CVD, each specific HT complication and 
death by RAMP-HT will be calculated from the unadjusted 5-year rates 
between RAMP-HT and usual care HT cohorts. 
 
Multivariable Cox proportional hazards regression will be performed to 
estimate the adjusted effect of RAMP-HT on the dependent variable of 
each first HT complication event, adjusting for all baseline covariates of 
patients. Cox model has been widely used to evaluate the effectiveness 
of primary care intervention in previous study.16 For each model, survival 
curves will be estimated by Kaplan-Meier method and the differences 
between RAMP-HT and usual care groups will be compared using the 

 
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log-rank test. The effectiveness of RAMP-HT will be measured by Hazard 
ratio with 95% confidence intervals in the regression models. Predictive 
accuracy of Cox models will be assessed and compared using Harrell’s 
discrimination C-index, ranging from zero to one. A value of 0.5 indicates 
no predictive discrimination, and values of 0 or 1.0 indicate perfect 
separation of patients17. Goodness-of-fit for Cox regression model will be 
assessed using Akaike information criterion and Bayesian information 
criterion.  
 
Cost Analysis of RAMP-HT 
 
Descriptive statistics will be used to summarize the costs of RAMP-HT and 
annual direct medical costs for RAMP-HT and usual care patients at 
baseline and over each of 5 subsequent years, overall, and by the presence 
of complications. The cost of each healthcare service will be derived by 
multiplication of the unit cost from Gazette and Hospital Authority ordinance 
and the number of respective services used by the patients. To avoid double 
counting the cost of drugs and laboratory tests, the Government Gazette 
cost of each GOPC and SOPC attendance will be adjusted by a factor of 
0.7 to include only the manpower cost18. The total public medical costs will 
be calculated by summing up all costs of drugs, laboratory tests and each 
healthcare service utilisation. The private medical costs will be estimated by 
summarizing the self-reported costs of private consultations, self-medication 
and private hospitalisation. The average annual public and private direct 
medical costs per patient will be analysed by HT complication status (CHD, 
heart failure, stroke and ESRD), and by RAMP-HT and usual care HT 
groups. To determine whether the direct medical costs of the RAMP-HT 
cohort is higher than the usual care HT cohort, one sided independent t-test 
will be performed. 
 
Health Preference by HT Complication Status 
 
The SF-6D preference will be calculated by the HK population specific 

 
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algorithm15 by HT complication status. Independent t-test will be used to 
compare the SF-6D values of HT patients with each complication to those 
without any complication, and between RAMP-HT and usual care HT 
patients. 
 
All above statistical analyses will be performed using STATA Version 13.0 
(StataCorp LP. College Station, Texas, U.S.). All significance tests will be 
two-tailed and those with a p-value less than 0.05 will be considered 
statistically significant. 
 
Cost Effectiveness Analysis of RAMP-HT by Markov Modeling 
 
To evaluate the long term cost-effectiveness of RAMP-HT, a Markov model 
will be developed based on the natural history of a patient population, with 
similar distribution of age and gender as the RAMP-HT cohort, to simulate 
disease development over a lifetime. The structure of the model will be 
established as shown in Figure 1. At the end of each year, each HT subject 
may die, develop one HT complication or stay alive without complications. 
For subjects who develop complications, they may die immediately from the 
complication, die from other cause of death or stay alive in the complication 
health state.  
 
The transition probabilities of developing complications and death from year 
to year will be estimated from the observed complication rates from the 5-
year cohort study for RAMP-HT and usual care HT patients as described 
above. HT complication related mortality rates will be estimated by a review 
of published and unpublished local and overseas literature. Hong Kong 
standard life table19 will be used as a benchmark of survival presenting HT 
related mortality and mortality from other causes in Hong Kong local 
population among different demographic groups (e.g. age and sex). The 
cumulated life-years of the RAMP-HT and usual care cohorts will be 
calculated. The average cost and preference values for each HT disease 
state (with or without specific complication) obtained from the cost and 

 
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preference studies described above will be applied to the model to 
determine the cumulative quality adjusted life years (QALY) and lifetime 
costs for RAMP-HT and usual care cohorts.  
 
The measure of cost-effectiveness of RAMP-HT will be the incremental 
cost-effectiveness ratio (ICER) in term of cost per QALY gained by RAMP-
HT group compared to usual care. By comparing the between-group-
differences in average lifetime costs (∆C) and quality adjusted life years 
(QALYs) gained (∆E), the ICER will be calculated by dividing the 
incremental cost (∆C) by the incremental effectiveness (∆E) in terms of 
QALYs gained by the RAMP-HT intervention compared to usual care. The 
ICER will be compared against the threshold value of 1 GDP (Gross 
Domestic Product) per capita of Hong Kong / QALY (HK$295,303, 20137) to 
determine lifetime cost effectiveness of RAMP-HT. 
 
The Markov models will be analysed by the TreeAge Pro Suite (TreeAge 
Software, Inc, Williamstown, MA) which can run Markov models. 
 
Data sources are summarized in the table below: 
Propose 
Item/ unit 
Data collection methods 
Effectiveness Incidence 
of 
CVD, 
ESRD 
and 
all-cause 
mortality 
Anonymous 5-year dataset of 
diagnosis record (ICPC-2 and 
ICD-9-CM) to be extracted by 
HA statistics team from the HA 
CMS database# 
Direct 
Medical 
Costs 
(i)Cost of RAMP-HT 
 
 
 
 
 
 
 
(i) In-depth costing of RAMP-
HT by  
- set-up and operation costs 
questionnaires 
to 
be 
completed by HA clusters, 
GOPCs 
- central administration cost 
questionnaire completed by 

 
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(ii)Public medical costs  
of 
RAMP-HT 
and 
usual 
care 
group; 
with 
complications 
and 
without 
complications 
 
 
 
 
 
(iii)Private direct medical 
costs 
 
Finance Office in HA Head 
Office  
(ii) Public 
healthcare 
service 
cost will be estimated by the 
products of : 
- Public 
healthcare 
service 
utilization 
rates 
will 
be 
extracted from the 5-year HA 
CMS dataset.  
- Unit cost of each healthcare 
service published in the 
HKSAR 
Government 
Gazette  
(iii)Private 
medical 
cost 
questionnaire 
administered 
by telephone survey on 230 
RAMP-HT and 218 usual 
care HT subjects   
Calculation 
of 
quality 
adjusted life 
years (QALY) 
SF-6D 
Health 
preference values of HT 
patients 
without 
and 
with complications 
SF-12v2 HRQOL data from 410 
RAMP-HT and 388 usual care 
HT patients collected in our 
previous PRO surveys from 
May 2013 to March 2014 will be 
converted to SF-6D preference 
values. 
Cohort 
Life-
time Mortality 
estimates 
(i) HT related mortality  
(ii) Mortality from other 
causes 
(i) Annual HT-related mortality 
rates will be estimated from 
the annual incidence of HT 
complications and published 
mortality 
rates 
of 
the 
respective complications. 
(ii) Hong Kong standard life 
table19 will be used as a 

 
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benchmark for the estimation 
of mortality from other causes  
adjusted for age and sex) 
 
The Markov modelling will run on the following assumptions: 
1. Each HT patient will develop only one HT complication in his/her lifetime.
2. HT patients without complications will not die from HT related death, but 
may die from other cause of death. 
3. HT patients with complications may die from either HT-related death or 
other cause of death. 
4. RAMP-HT is effective in reducing HT complications for a period of 5 
year; thus the transition probability adjustment of CVD/ESRD 
development (as determined by the annualized hazard ratio obtained 
from our long-term effectiveness analysis) will be applied to the model of 
the RAMP-HT cohort in the first five years. After 5 years, the RAMP-HT 
will have the same transition probabilities of developing HT 
complications as usual care HT cohort.  
5. RAMP-HT is repeated every 12-24 months and the effectiveness of 
RAMP-HT will not decrease over time; thus the 5-year effect of RAMP-
HT can be annualized. Adjustment will be made on the average duration 
of repeat RAMP-HT based on available empirical data in order to avoid 
over-estimation of the RAMP-HT cost. 
6. The medical cost (public and private) of HT patients in usual care and 
RAMP-HT programme is the same for the same HT health status except 
for the additional RAMP-HT cost.  
7. All costs and health preference will be discounted by an annual rate of 
3.5% as recommended by the guidance of National Institute for Health 
and Clinical Excellence (National Institute for Clinical Excellence 2008).  
 
[Word Court: 3968 ]  
e Timeline 
 Project timeline 
(0-24 month) 
Work Phase  
Milestones 

 
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April – July, 2016 
(0-4 month) 
 
Study planning  
a) Recruitment, training of RA and 
literature review 
b) Data schema and operational 
definition confirmation with the HA 
Statistics Team  
c) Costing data collection 
questionnaires development and 
field testing  
d) Extraction of 3-year outcome and 
public medical cost data from HA 
CMS data for interim analysis  
August – 
November, 2016 
(4-8 month) 
 
Cost Data Collection a) Costing data collection 
b) Feedback on  results of cost of 
RAMP-HT to HA RAMP-HT 
programme team to assure validity 
December, 2016 –  
March, 2017 (9-12 
month) 
 
Preliminary Model 
development based 
on 3-year data 
a) Analysis of annual direct public and 
private medical costs of RAMP-HT 
& usual care subjects 
b) Preliminary analysis of ICER of 
RAMP-HT Vs. usual care  
 
April– November, 
2017 (13-20 
month) 
 
Final Data analysis  
a) Extraction of 5-year outcome and 
public medical cost data from HA 
CMS  
b) To evaluate the 5-year long-term 
effectiveness of RAMP-HT 
c) Final Markov model on cost-
effectiveness of RAMP-HT based 
on 5-year outcome and cost data. 
December, 2017 – 
March, 2018 (21-
24 month) 
 
Preparation of final 
report and 
manuscripts 
a)  Two manuscripts for submission to 
peer-reviewed international 
journals   
b)  Final report ready for submission 

 
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before September, 2018. 
 
  
f) Existing Facilities:  
 The Department of Family Medicine and Primary Care (FMPC) has more than 20 
years of experience in health services research with a focus on quality of primary 
care and patient-reported outcomes. Our research team of clinical academic staff, 
postdoctoral fellows, statisticians and research assistants has a track record of 
close collaboration with the HA in the extraction and analysis of longitudinal data 
from the HA CMS in the past five years in the evaluation of QOC studies of 
various enhanced primary care programmes including the RAMP-HT, RAMP-DM 
and Patient Empowerment Programme (PEP) which involved over 400,000 
patients and have published 10 papers from these studies.  
 
Relevance of Past Studies Conducted 
As explained above, we have already identified the required cohort of over 16,000 
HT patients and will have collected their 3-year clinical data by 30 November 
2015, which provides an opportunity for an extended 5-year study. Our research 
team in the Department of Family Medicine and Primary Care in collaboration with 
our School of Public Health and School of Nursing also has a track record of cost-
effectiveness analysis studies including an on-going in-depth study of the cost-
effectiveness of the RAMP-DM and PEP to be completed in 2016.  
g Justification of Requirements: 
 The funding request in this application is to cover the staff and other cost for the 
extended and additional data collection and data analysis from April, 2016 to 
March, 2018, which does not overlap with the previous evaluation of QOC of 
RAMP-HT from 2012 to 2016.  
Proposed Budget (All costs are in HKD) 
Item 
Description/Justification 
Cost 
1.Staff related costs 
 
 
1 Full time Senior 
Research Assistant 
 
 
Senior Research Assistant with 
expertise in cost-effectiveness analysis 
and biostatistics is needed to carry out 
the large amount of complex data 
$1,030,164

 
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0.5 Full time 
Research Assistant 
for two years 
 
analysis of longitudinal data and co-
ordinate the whole project and liaise 
with the HA Statistics Department. 
$33,140 (includes MPF) x 24 months = 
$795,360 
 
Research Assistant is required to liaise 
with the HAHO, RAMP-HT cluster 
coordinators and RAMP-HT clinic in-
charge, assist data cleaning and data 
analysis, literature review and 
preparation of manuscripts and reports. 
$19,567 (includes MPF) x 0.5 x 24 
months = $234,804 
2.General 
Expenditure 
 
 
Service charge by 
the HA 
for CMS data extraction by the 
Statistics Department of the HA 
$100,000 
Computers and 
Statistical software 
Two high speed & memory computers 
for handling a large database and the 
licenses of TreeAge Pro Suite 
(TreeAge Software, Inc, Williamstown, 
MA) and STATA software (STATA Corp, 
College Station, Texas) are needed for 
the analysis of a large data set. 
$34,300 
Overseas conference Conference attendance to disseminate 
results 
$10,000 
Publication fee 
 
$10,000 
Audit fee 
 
$10,000 
Printing and 
consumables 
 
$5,500 
Total: 
 
$1,199,964
 
 

 
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h Purpose and Potential: 
 This study addresses the HMRF thematic priority of enhanced primary care and 
multi-disciplinary treatment of the most common chronic disease. The results will 
provide empirical evidence on the long term effectiveness and cost effectiveness 
of the multidisciplinary RAMP-HT in improving health of patients with 
hypertension. This can inform policy on whether special intervention programmes 
such as the RAMP-HT should be implemented.  
 
i) Key References:  
1. Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint 
National Committee on Prevention, Detection, Evaluation, and Treatment of High 
Blood Pressure: the JNC 7 report. JAMA 2003;289(19):2560-71. 
2. Lim SS, Vos T, Flaxman AD, et al. A comparative risk assessment of burden of 
disease and injury attributable to 67 risk factors and risk factor clusters in 21 
regions, 1990–2010: a systematic analysis for the Global Burden of Disease 
Study 2010. The lancet 2013;380(9859):2224-60. 
3. Glynn LG, Murphy AW, Smith SM, et al. Interventions used to improve control of 
blood pressure in patients with hypertension. The Cochrane database of 
systematic reviews 2010(3):CD005182. 
4. Wood D, Wray R, Poulter N, et al. JBS 2: Joint British Societies' guidelines on 
prevention of cardiovascular disease in clinical practice. Heart 2005;91:1-52. 
5. Mattila R, Malmivaara A, Kastarinen M, et al. Effectiveness of multidisciplinary 
lifestyle intervention for hypertension: a randomised controlled trial. Journal of 
human hypertension 2003;17(3):199-205. 
6. Krause T, Lovibond K, Caulfield M, et al. Management of hypertension: summary 
of NICE guidance. Bmj 2011;343:d4891. 
7. Census and Statistics Department HKSAR. Gross Domestic Product (Yearly) 
(2013 Edition), 2014. 
8. World Health Organization. Cost effectiveness and strategic planning (WHO-
CHOICE): Cost-effectiveness thresholds. Secondary Cost effectiveness and 
strategic planning (WHO-CHOICE): Cost-effectiveness thresholds. 
http://www.who.int/choice/costs/CER_thresholds/en/. 
9. Rosenbaum PR, Rubin DB. The central role of the propensity score in 
observational studies for causal effects. Biometrika 1983;70(1):41-55. 

 
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22 
 
10. Rosenbaum PR. Observational studies: Springer, 2002. 
11. Rubin DB. Estimating causal effects of treatments in randomized and 
nonrandomized studies. Journal of educational Psychology 1974;66(5):688. 
12. Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and 
propensity score matching, common support graphing, and covariate imbalance 
testing. Statistical Software Components 2012. 
13. Levey AS, Coresh J, Balk E, et al. National Kidney Foundation practice 
guidelines for chronic kidney disease: evaluation, classification, and stratification. 
Annals of internal medicine 2003;139(2):137-47. 
14. Lam CLK, Wong CKH, Lam ETP, et al. Population norm of Chinese (HK) SF-12 
health survey_version 2 of Chinese adults in Hong Kong. Hong Kong Practitioner 
2010;32(2):77-86. 
15. McGhee SM, Brazier J, Lam CLK, et al. Quality-adjusted life years: population-
specific measurement of the quality component. Hong Kong Med J 2011;17(Suppl 
6):17-21. 
16. Wong CKH, Wong WCW, Wan YF, et al. Patient Empowerment Programme in 
primary care reduced all‐cause mortality and cardiovascular diseases in patients 
with type 2 diabetes mellitus: a population‐based propensity‐matched cohort 
study. Diabetes, Obesity and Metabolism 2015;17(2):128 -35  
17. Harrell FE, Lee KL, Mark DB. Tutorial in biostatistics multivariable prognostic 
models: issues in developing models, evaluating assumptions and adequacy, and 
measuring and reducing errors. Statistics in medicine 1996;15:361-87. 
18. Hospital Authority. Hospital Authority Annual Report 2010-2011. 
19. Census and Statistics Department HKSAR. Hong Kong life tables, 2006-2041, 
2012  
List of additional Materials 
I. 
Figure 1. The Structure of Markov Model 
II. 
Table 1. Clinical and costs parameters inserted to Markov model 
III. 
Appendix A Protocol for the RAMP-HT programme 
IV. 
Appendix B Workflow of the RAMP-HT programme 
V. 
Appendix C Flow Chart of the Process of Matching Subjects 
VI. 
Appendix D Cost questionnaires (cluster and clinic) 
VII. 
Appendix E Telephone Survey on the Cost of Private Medical Care 

 
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VIII. 
Appendix F Data schema for Cost-Effectiveness Analysis 
 
 

 
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Figure 1. The Structure of Markov Model 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 1. Clinical and costs parameters inserted to Markov model 
Natural history 
of Hypertension 
Transition probability from without complication to Coronary Heart 
Disease* 
Transition probability from without complication to Stroke* 
Transition probability from without complication to Heart Failure* 
Transition probability from without complication to End Stage Renal 
Failure* 
The rate of other death unrelated to Coronary Heart Disease, Stroke, Heart 
Failure or end stage renal disease 
The rate of HT-related death from Coronary Heart Disease, Stroke, Heart 
Failure or end stage renal disease 
Survival rate of Hong Kong population 
 
*The hazard ratio obtained from the results of long- term effectiveness of 
RAMP-HT will be applied to adjust the transition probabilities of 
Coronary Heart Disease, Stroke, Heart Failure and end stage renal disease 
in the first 5-year after baseline 
Costs 
Cost of RAMP-HT 
Direct medical costs for each health status (i.e. Hypertension without 
complications, HT with Coronary Heart Disease, HT with Stroke, HT with 
Heart failure or HT with End Stage Renal Disease) 
Health utility 
Health utility for each health status (i.e. Hypertension without 
complications, HT with Coronary Heart Disease, HT with Stroke, HT with 
Heart failure or HT with End Stage Renal Disease) 
 
Incidence of End Stage 
Renal Disease (ESRD) 
HT patients under 
usual care/ RAMP-HT 
Incidence of Coronary 
Heart Disease (CHD) 
CHD death
Other 
death 
PCVD 
PESRD
Incidence of Stroke 
Incidence of Heart 
Failure (HF) 
Stroke 
death 
HF death 
ESRD 
death 
PHF 
PStroke