10-year Risk Prediction Models of Complications and Mortality of Diabetes Mellitus in Chinese Patients in Primary Care in Hong Kong
Conditions: Diabetes Mellitus, Cardiovascular Diseases, Mortality, Primary Health Care
Sponsor: The University of Hong Kong

Section 13: Proposed Research Project – 2015 Mar 4 
1 
PROPOSED RESEARCH PROJECT 
a) 
Title: 
 
10-year Risk Prediction Models of Complications and Mortality of Diabetes Mellitus 
in Chinese Patients in Primary Care in Hong Kong 
 
 
b) 
Introduction:  
 
 
Diabetes Mellitus (DM) is a well-recognized public health issue, affecting 415 million 
people and costing HK$5.2 trillion in global health expenditures worldwide (1). It is 
estimated 568,000 people are living with DM in Hong Kong, and the projected 
number will increase to 781,000 in 2040 (1). In Hong Kong, the health care costs for 
diabetic patients are estimated to be HK$2 billion accounting for 4.5% of the gross 
domestic product (GDP) per capita, and a major health service burden accounting 
for 200,000 (17% of total) hospital admissions with 1.2 million hospitalization days 
per year (1, 2). 
 
DM can lead to many complications resulting in morbidity and mortality. According to 
the International Diabetes Federation (IDF), in 2015, diabetes led to 5.0 million 
(14.5% of all deaths) deaths worldwide which translated to one death every six 
seconds and approximately 70% of DM related deaths were attributed to 
cardiovascular diseases (CVD) (1). The development of diabetes-related 
complications significantly increases medical costs (1). A previous local study also 
found that the direct medical cost for diabetic patients with CVD were 1.1 times more 
than patients without CVD in Hong Kong (3).  
 
To prevent DM complications, the American Heart Association (AHA) guidelines 
recommend primary care providers to provide regular assessment and management 
of risk factors for patients especially those who are at high risk of developing DM 
complications. Although the National Cholesterol Education Programme (NCEP) in 
the United States has suggested that all diabetic patients be treated as if they had 
CHD, however the observed rate of cardiovascular diseases (CVD) vary vastly 
among different diabetic patients (4). The American Diabetes Association (ADA) and 
the Canadian Diabetes Association guidelines both include 10-year overall CVD risk 
stratification into account to identify high-risk patients for more intensive medical and 
psychosocial interventions (5). The guidance of stain prescription from the American 
College of Cardiology and the American Heart Association, which is consistent with 

Section 13: Proposed Research Project – 2015 Mar 4 
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the ADA, also takes predicted 10-year overall CVD risk into account (6). The ADA 
recommends aspirin treatment for diabetic patients with a 10-year predicted over 
CVD risk higher than 10% (5). Studies in the United States, the United Kingdom, 
Australia, New Zealand and Hong Kong showed that systematic risk assessment 
and risk-stratified management initiatives in primary care settings could improve 
clinical outcomes such as Haemoglobin A1c (HbA1c), blood pressure (BP) and low-
density lipoprotein cholesterol (LDL-C), as well as reduce utilization of health 
services including Accident and Emergency (A&E) attendance, and hospital 
admissions (7). In 2009, the Hong Kong Hospital Authority (HA) made an initiation to 
enhance the quality of DM care in all HA primary care clinics by the introduction of 
the Multi-disciplinary Risk Assessment and Management Programme – Diabetes 
Mellitus (RAMP-DM) to systematically assess the CVD risk of DM patients and then 
managed according to risk-stratified protocols (8).  
 
A key to cost-effective management of DM is an accurate risk assessment and 
stratification system that identifies high-risk patients for more intensive medical and 
psychosocial interventions. At the same time, an accurate estimation of risk 
distribution can inform policy-makers to allocate appropriate resources and plan 
services that can maximum population health benefit for DM patients. Existing 
prediction functions commonly used include the Framingham [10-year] (9, 10), the 
United Kingdom Prospective Diabetes Study (UKPDS) [5-year] (11, 12) and the local 
(Joint Asia Diabetes Evaluation (JADE) [5-year] (13, 14) models. However, our 
earlier study on 10,969 diabetic patients managed in the HA primary care clinics 
showed none of these models was accurate in predicting the observed events over 
a 4-year follow up for this patient population (15). The Framingham models 
overestimated the risks of CHD and stroke by 2.0 times and 23%, respectively(15). 
Several other studies have also concluded that the Framingham models were not 
applicable to Chinese populations (16). The UKPDS risk prediction models also 
showed a tendency to overestimate the risk of CHD and stroke by 5.1 times and 
51%, respectively (15). The JADE prediction models, although developed from the 
local Chinese population, overestimated the 4-year CHD, stroke rates by 2.9 times 
and 41%, respectively, when compared to the actual 4-year observed incidences 
(15). The JADE prediction models were developed from data of Chinese diabetic 
patients under specialist care who are more likely have more severe disease and 
many had complications such as stroke or CHD at baseline (13, 14), which may 
explain why they tend to over predict complications in primary care DM patients. The 

Section 13: Proposed Research Project – 2015 Mar 4 
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cohort from JADE was collected almost 20 years ago (1995) and the number of 
subjects (around 7000) was relatively small, which may not be generalizable to the 
400,000 DM patients under HA care. Furthermore, JADE only provides 5-year risk 
prediction although 10-year risk is the international standard (17). 
 
We have also identified some deficiencies in existing risk prediction models that 
might have affected their accuracy. Firstly, gender is a factor that is of concern in the 
analyses of risk factors and CVD/mortality because males are typically associated 
with a higher risk of CVD/mortality (18), but statistical adjustment for gender is often 
insufficient to control for varying risk-factor profiles and CVD/mortality incidence (19). 
Secondly, there are possible interaction effects between age and risk factors on the 
CVD/mortality as the magnitude of the effect of specific risk factors such as LDL-C 
on the CVD/mortality may decrease with age (20). Thus, the interaction term 
between age and risk factors should be considered when developing the risk 
prediction models. Thirdly, many studies including our study and post-hoc analysis of 
JADE illustrated that there were curvilinear association (J or U shape) between 
HbA1c/ SBP/ DBP/ LDL-C/ BMI and the risk of CVD/mortality (21, 22), and thus the 
quadratic term of such clinical parameters should be evaluated when developing the 
risk prediction models. Finally, current risk prediction models were based on a single 
measurement (i.e. baseline) of clinical parameters such as HbA1c, SBP, DBP and 
BMI, but the use of repeated measures, e.g. average of all available readings or last 
available before event, may be more better predictors. The studies from JADE also 
suggest the inclusion of factors such as drug treatments and subsequent HbA1c and 
BP may improve the predictive power of the models (13, 14). 
 
Inaccurate risk stratification may lead to inappropriate risk-stratified interventions. 
There is a need for new robust risk prediction models for the prediction of 10-year 
CVD risk and mortality for primary care Chinese patients to enable accurate risk 
stratification of DM patients in the HA on-going RAMP-DM or other primary care 
systematic risk-stratified multi-disciplinary management programmes. Furthermore, 

Section 13: Proposed Research Project – 2015 Mar 4 
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robust risk prediction models for the overall prediction of first CVD and all-cause 
mortality can inform policy makers in service planning and resource allocation.  
 
 
c) 
Aims, Objectives and Hypotheses 
 
 
This study aims to develop 10-year risk prediction models for total CVD and all-
cause mortality among Chinese diabetic patients in primary care. Risk prediction 
models for individual DM complications including CHD, heart failure, stroke and 
ESRD will also be developed.  
 
The objectives are to: 
1. Calculate the 10 years incidence of total CVD, all-cause mortality and each major 
DM complication in Chinese DM patients in primary care. 
2. Determine the risk factors that significantly predict total CVD, all-cause mortality 
and each major DM complication for Chinese DM patients in primary care. 
3. Develop and validate risk prediction models for total CVD, all-cause mortality and 
each major DM complication for Chinese DM patients in primary care. 
4. Develop a risk prediction nomogram and chart for the risk of total CVD, all-cause 
mortality for Chinese DM patients in primary care 
 
Hypotheses 
The following hypotheses will be tested: 
1. Patient socio-demographic, clinical parameters, disease characteristics, and 
treatment modalities (these independent variables are described in the Methods 
section) are predictive of 10-year risk of total CVD, all-cause mortality and 
individual DM complication as a dependent variable.  
2. The risk prediction models for total CVD, all-cause mortality and individual DM 
complication developed in this study can have over 70% of discriminating power. 
 
d) 
Plan of Investigation: 
 
 
(i) Study design 
 
 
 A 10-year retrospective study on a population-based cohort of Chinese DM 
patients in primary care.  
 
 
 
 

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(ii) Subjects 
 
The cohort will include all patients with a documented clinical diagnosis of DM 
and were receiving care in the Hospital Authority (HA) primary care General 
Out-Patient Clinics (GOPC) and Family Medicine Clinics (FMC) on or before 1 
July 2006 identified from the HA clinical management system (CMS) database. 
 
Inclusion criteria 
1. Age ≥ 18 years old 
2. At least 1 GOPC/FMC attendance on or within 1 year before 1 July 2006 
3. Had a CMS record of the coding of ICPC-2 of T89 (Diabetes insulin 
dependent) or T90 (Diabetes non-insulin dependent) on or before 1 July 
2006 
 
Exclusion criteria 
1. Patients who had a diagnosis of any DM complications defined by the 
relevant ICPC-2 or ICD-9-CM (shown in the Method section) on or before 1 
July 2006. 
2. Patients exclusively managed by Specialist Out-Patient Clinic (SOPC) on or 
before 1 July 2006. 
 
Sample size calculation 
 
The detailed sample size calculation for each study outcome is attached in 
Appendix B. The required sample size is based on the requirements for the 
development and validation of the least common DM complication of ESRD. 
Specifically, based on our previous study, the 5-year incidence of ESRD was 
1.9%(8), which can be extrapolated to a 10-year incidence of ESRD was 3.8% 
by assuming a constant incidence rate over time. To develop the risk prediction 
model for ESRD by multivariable Cox proportional hazard regressions with 
forward stepwise variables selection on 16 potential risks factors, we need 
21,053 subjects using the 1 in 50 rule that 1 candidate predictor can be studied 
for every 50 events (23). To validate the risk prediction models, the area under 
the receiver operating characteristic curve (AUC) will be used to assess the 
discriminatory power of the predictive model on a separate sample of subjects. 
With 10 years incidence rate of ESRD was 3.8%(8), a total 14,307 subjects 
(544 subjects with ESRD and 13,763 subjects without ESRD) is needed to 

Section 13: Proposed Research Project – 2015 Mar 4 
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ensure a precision of 0.05 for AUC of 0.7 by a 95% confidence interval. 
 
Hence, a total 35,360 male and 35,360 female are needed for the development 
(training dataset) and validation of risk prediction models stratified by gender. 
The HA data set that we have obtained for our 5-year evaluation of quality of 
care and effectiveness of RAMP-DM study(8) has identified 120,857 DM 
patients under the care of public primary care on or before 1 Jan 2009, so we 
should be able to obtain sufficient number of subjects. 
 
 
 
 
(iii) Methods 
 
Each patient will be followed from 1 July 2006 to 31 December 2016 for the 
development of the indicator DM complications and mortality. 
 
Definition of indicator DM complications  
The incidence of four major DM complications (CHD, stroke, heart failure and 
ESRD), total CVD and all-cause mortality will be calculated. The incidence is 
counted from the earliest date of documented diagnosis defined by the relevant 
ICPC-2 and/ or ICD-9-CM coding recorded in the HA CMS database from 1 July 
2006 to 31 December 2016. The relevant ICPC-2 and ICD-9-CM codes of each 
DM complication and mortality are determined by the academic and HA clinician 
co-investigators as listed below:-  
 
1. CHD (ischaemic heart disease, myocardial infarction (MI), coronary death or 
sudden death) is defined by any of ICPC-2 K74 to K76 and ICD-9-CM 410.x, 
411.x to 414.x, 798.x 
2. Stoke (fatal and non-fatal stroke) is defined by any of ICPC-2 K89 to K91 or 
ICD-9-CM 430.x to 438.x.  
3. Heart failure is defined by any of ICPC-2 K77 or ICD-9-CM 428.x 
4. CVD is defined as the presence of any of CHD, heart failure and stroke 
ICPC-2 or ICD-9-CM codes listed in 1, 2 and 3 above. 
5. ESRD is defined by any of ICD-9-CM 250.3x, 585.x, 586.x, or an 
eGFR<15mL/min/1.73m2.  
6. Mortality is identified from the Hong Kong Death Registry.  
 
Risk factors to be Included in the Risk Prediction Models  
Risk factors (independent variables) previously found to be associated with DM 

Section 13: Proposed Research Project – 2015 Mar 4 
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complications from the literature (4) and those that are routinely available in 
primary care are selected to strike a balance between comprehensiveness and 
feasibility.  The potential risk factors that will be explored include those related 
to patient’s socio-demographics, clinical parameters, disease characteristics 
and treatment modalities. Patient socio-demographics include sex, age and 
smoking status. Clinical parameters include body mass index (BMI), 
haemoglobin A1c (HbA1c), systolic and diastolic blood pressure, lipid profile 
(total cholesterol, HDL-C, LDL-C, triglyceride), estimated glomerular filtration 
rate (eGFR) and albuminuria. Disease characteristics include the duration of 
DM and co-morbidity. Treatment modalities include the use of specific anti-
hypertensive drugs, insulin, specific oral anti-diabetic drugs and lipid-lowering 
agents. The operational definitions of the risk factors are shown in appendix A. 
These factors, except sex, age, duration of DM and co-morbidity, are 
modifiable, which have implications for practice. 
 
Data collection 
In the middle of 2017, anonymous data from 1 January 2006 to 31 December 
2016 of all DM patients who satisfy the inclusion criteria and without any 
exclusion criteria will be extracted by the HA statistics team from the HA clinical 
management system (CMS) database. We have successful experience in 
working with the HA in the extraction of similar data from 2009 to 2013 for our 
extended evaluation on quality of care and effectiveness of RAMP-DM study 
(7), and we have obtained preliminary agreement from the HA for the data 
extraction in the present study.  
 
Outcome Measures 
 
1. The incidence of total CVD, all-cause mortality and each of 4 major DM 
complications over 10 years  
2. Factors predictive of total CVD, all-cause mortality and each of 4 major DM 
complications over 10 years  
3. 10-year risk prediction models for total CVD, all-cause mortality and each of 
4 major DM complications 
4. Factors that have sufficient power to classify Chinese DM patients in primary 
care into risk group in terms of total CVD and all-cause mortality. 
 
 
 
 
 

Section 13: Proposed Research Project – 2015 Mar 4 
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(iv) Data processing and analysis 
 
The cohort will be stratified by gender. Descriptive statistics will be used to 
calculate the incidence of total CVD, all-cause mortality and each of 4 major DM 
complications will be analysed annually and cumulatively over 10 years with a 
95% confidence interval. The distribution of risk factors will be cross-tabulated 
by complication or mortality events. The 10 year cumulative incidence of various 
DM complications and mortalities will be further analysed by Kaplan-Meier 
method. The Kaplan-Meier survival curve will be used to describe the 
survivorship of total CVD, all-cause mortality and each of 4 major DM 
complications in the study cohort over 10 years. Unadjusted associations 
between the risk factors and odds of events will be assessed by independent t-
test for continuous variables or Chi-square test for categorical variables. 
 
The cohort will be randomly split on a 2:1 basis, with the two-third sample used 
for developing the risk prediction models, and the other one-third sample used 
for validation of the risk prediction models. The analyses will be carried out 
separately for men and women. 
 
Development of risk prediction models 
Cox proportional hazard regressions with forward stepwise method will be used 
to develop the risk prediction models for total CVD, all-cause mortality and each 
of 4 major DM complications. If the main term of a clinical parameter is selected 
in the models, the quadratic term of such clinical parameter will be evaluated. 
Afterwards, the interaction terms between selected predictors and age will be 
also examined in the risk prediction models. Cox regressions is the most 
commonly used method in risk prediction models in the Framingham Heart 
Study (9, 10) and UKPDS (11, 12). It allows us to estimate the risk of disease or 
death for an individual, given their prognostic variables. A positive hazard ratio 
means a higher likelihood of event associated with that specific variable. 
Conversely, a negative hazard ratio means a lower likelihood of the event 
associated with that specific variable. The key proportional hazards assumption 
will be assessed by examining plots of the scaled Schoenfeld residuals against 
time for the covariates. Any non-random pattern indicates a violation of the 
proportional hazards assumptions in which case transformation of covariates 
may be considered. For example, all continuous variables were naturally 
logarithmically transformed to minimize the influence of extreme values and to 

Section 13: Proposed Research Project – 2015 Mar 4 
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improve discrimination and calibration of the models. A parametric approach 
such as exponential or Weibull distribution for the hazard function can also be 
carried out. A total of 6 risk prediction models will be established for total CVD, 
all-cause mortality and each of 4 major DM complications. The log of the hazard 
ratio of each selected risk factor in the final model will be used as coefficient 
weights in the prediction model of each relevant outcome. The risk equations for 
10 year’s follow-up will be established by combining these weights with the 
survivor function(9). A receiver operating characteristic (ROC) curve of 
predicted risk against observed events will be used to determine the risk 
threshold for the predicted outcomes as the risk threshold with the highest sum 
of sensitivity and specificity of the actual observed events. 
 
Validation of risk prediction models 
To validate the risk prediction models for total CVD, all-cause mortality and each 
of 4 major DM complications, they will be applied to the data of the one third 
validation sample to estimate the risk level of the subjects. A receiver operating 
characteristic (ROC) curve of predicted risk against observed events will be 
used to calculate the area under the curve (AUC) for assessing overall 
prediction accuracy. The corresponding 95% confidence interval will be 
obtained by bootstrapping of size 2000. An AUC of less than 0.7 indicates 
limited discriminating power, 0.7 to 0.8 is acceptable, and higher than 0.8 
suggests strong discrimination of the predictive models. Using the optimal 
prediction risk threshold identified by ROC curve in training dataset, and the 
corresponding sensitivity and specificity of the models against observed events 
will be calculated to further evaluate the performance of the models. 
 
Calibration will be used to measure how closely predicted outcomes agree with 
actual outcomes. Calibration of the model’s ability to correctly estimate the 
absolute risks will be examined by modified Hosmer-Lemeshow test and 
calibration plots. The modified Hosmer-Lemeshow test for time to event data 
measures how well the predicted probability of the expected event rate agrees 
with the observed event rate, where a p-value higher than 0.05 indicates good 
model calibration. In a calibration plot of the observed incidence of events 
against the predicted risk shows the scatter along the 45° line of perfect fit 
between predicted risk and observed incidence of event throughout the entire 
risk spectrum.  

Section 13: Proposed Research Project – 2015 Mar 4 
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The D statistic, R2 statistic and Brier score will be calculated for assessing the 
predictive power of the model. The D statistic is a measure of discrimination 
where higher value indicates better discrimination. The R2 statistic is a measure 
of explained variation with a higher value indicating better performance. The 
Brier score is a measure of goodness of fit in which a lower value mean higher 
accuracy.  
 
Development of a risk prediction nomogram and chart 
In order to enable the 10-year risk prediction models for total CVD and all-cause 
mortality to be applied in busy clinical setting, risk prediction nomograms and 
charts will be developed for men and women. For the nomogram, the patient’s 
score for each predictor is plotted on the appropriate scale and vertical lines are 
drawn to the line of points to obtain the corresponding scores. The score of 
each predictor will be transformed based on the estimated standardized beta 
coefficient of each predictor from the risk prediction model. For the continuous 
predictors (e.g. age), the line with interval depends on it units from minimum to 
maximum values among studies subjects (e.g. 20, 40, 60, 80 years old) will be 
displayed on the nomogram plot and the corresponding scores will be obtained 
based on the estimated standardized beta coefficient of the predictor (e.g. 
assign age of 20 to 0 point, age of 40 to 2 points, age of 60 to 4 points, age of 
80 to 6 points). For categorical predictor (e.g. gender), each level of the 
predictor will be ranked a corresponding score based on the estimated 
standardized beta coefficient of the predictor (e.g. assign female to 0 point, 
male to 3 points). We have developed a similar nomogram on the risk of DM in 
a previous study as shown in Appendix C (24). All scores are summed to obtain 
a total score. The total score is plotted on the total line with corresponding 
predicted risk of CVD. Moreover, we will develop risk prediction charts similar to 
those developed by the Joint British Society as shown in Appendix D. The most 
significant predictors, up to a maximum of five, found in the full Cox regression 
models will be selected to classify subjects into 10-year CVD risk groups of 
<10% (low risk), 10%-20% (medium risk) and >20% (high risk). The Kaplan-
Meier survival curves of each risk group will be developed and compared by 
log-rank tests to confirm the hazard ratios are significantly different among all 
risk groups. 
 

Section 13: Proposed Research Project – 2015 Mar 4 
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STATA software version 13 (STATA Corp, College Station, Texas) will be used 
for data analyses. 5% is used as the level of significance in all statistical tests. 
 
Strategies against anticipated problems  
1. Missing data of risk factors 
Multiple imputation will be used to handle missing data. Multiple imputation 
aims at increasing the power of the analysis and producing more reliable 
and applicable models within clinical practice. It also takes subjects with 
incomplete data into account to avoid unnecessary biases. Each missing 
value will be imputed five times by the chained equation method, which was 
equivalent to attain a relative efficiency of 95%. For each of the five imputed 
datasets, the same analysis will be performed with the five sets of results 
combined based on Rubin’s rules. 
2. Misclassification bias from the use of administrative database 
This study relies on doctor entries of clinical diagnosis on DM complications 
and co-morbidity indicated by pre-defined by ICPC-2 and ICD-9-CM codes, 
which may be subject to misclassification bias. We will cross-check the 
diagnosis and prescription relation, and compare diagnosis from hospital 
and primary care records for the same patient in order to assess the data 
quality. 
 
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e) 
Timetable of Work 
June – August, 2017: 
i. Recruitment, training of RA  
ii. Meeting and discussion with HA co-investigators and Statistics team to confirm the 
data extraction schema and operation definitions  
 
September – December, 2017 
i. Extraction of data from HA CMS  
ii. Update literature review 
 
January – March, 2018: 
i. Data cleaning 
ii. Data analysis on the incidence & risk factors of DM complications and mortality  
  
April – December, 2018:  
i. Data analysis to develop 6 prediction models 
ii. Validation of the prediction models 
 
 

Section 13: Proposed Research Project – 2015 Mar 4 
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January -May 2019: 
i. Development of 10-year CVD risk prediction nomograms  
ii. Development of 10-year CVD risk prediction charts 
iii. Preparation of manuscripts for publications  
iv. Preparation of final report. 
 
f) 
Existing Facilities:  
 
 
The Department of Family Medicine and Primary Care (FMPC) has more than 20 
years of experience in health services research. We have a dedicated team led by 
clinical academic staff and supported by postdoctoral fellows, statisticians and 
research assistants. Our research team has been commissioned, in collaboration 
with the HA, to carry out an extended evaluation of quality of care and effectiveness 
of five new chronic disease management programmes of the HA from 2012 to 2016. 
We have completed three evaluation and quality improvement cycles and built the 
preliminary CEA models for the two programmes as scheduled, and published 27 
referenced journal papers. Our team has the expertise and computer facilities to 
handle a very large data base of over 200,000 patient records.  
 
In our evaluation of QOC and effectiveness studies of the RAMP-DM and Patient 
Empowerment Programme (PEP) we have identified a cohort of 90,000 primary care 
DM patients and extracted their anonymized 5-year longitudinal data from 2008 to 
2013, and showed the effectiveness of both RAMP-DM and PEP in improving 
clinical outcomes and reducing the incidence of 1-year and 3-year CVD 
complications in DM patients (7). We are confident that we can identify the required 
number of subjects from our existing cohort and extract the 10-year longitudinal 
data. (Dr Daniel Fong and Mr Eric Wan) are bio-statisticians who will lead the 
statistical analysis of the project, and we have previously managed and analyzed 
large datasets that include over 2 million records from over a hundred thousand 
individuals (7, 8, 22), and conducted risk prediction models leading to international 
journal publications (24, 25). 
 
g) 
Justification of Requirements: 
 
 
 
Proposed Budget (All costs are in HKD) 
Item 
Description/Justification 
Sub-total 
Cost 
1.Staff related 
costs 
 
 
one Full time 
Senior Research 
Senior Research Assistant with expertise in 
medical statistics is needed to carry out the 
$994,029 

Section 13: Proposed Research Project – 2015 Mar 4 
13 
Assistant  
 
 
 
 
 
0.5 Full time 
Research 
Assistant for one 
and half years 
 
large amount of complex data analysis of 
longitudinal data, co-ordinate the whole 
project and liaise with the HA Statistics 
Department. HKD $32,950 (includes MPF) x 
24 months = $790,800 
 
Research Assistant is required to assist data 
cleaning, analysis, literature review and 
preparation of manuscripts and reports. 
HKD $22,581 (includes MPF) x 0.5 x 18 
months = $203,229 
2.General 
Expenditure 
 
 
Service charge 
by the HA 
For CMS data extraction by the Statistics 
Department of the HA 
$150,000 
Computers 
One high speed & memory computer for 
handling a large database 
$7,000 
Statistical 
software 
One license of STATA software (STATA 
Corp, College Station, Texas) are needed for 
the analysis of a large data set. 
$7,000 
Overseas 
conference 
Conference attendance to disseminate 
results 
$10,000 
Publication fee 
 
$20,000 
Audit fee 
 
$10,000 
Printing and 
consumables 
 
$1,971 
Total: 
 
$1,200,000 
 
 
h) 
Purpose and Potential: 
 
 
This study is strongly relevant to the thematic priority of “Enhancing multi-
disciplinary health service models and effectiveness for primary care programmes in 
chronic disease management” of the Health & Health Services Research. There is a 
need for the development of 10-year risk prediction models based on the up-to-date 
population-based cohort for diabetic complications, particularly first total CVD. 
Chinese patients in primary care will enable accurate risk stratification, better 
prioritization of resources and more cost-effective interventions for diabetic patients 
in primary care. They can also better inform and empower patients to prevent 
potential DM complications. At the health policy level, the results can inform 
decisions on service provision in the care of diabetes mellitus in primary care to 
achieve maximum population health benefit. The prediction models can also be 
used as an outcome measure on the potential benefits of complication prevention in 
clinical trials on DM interventions in primary care. 
 

Section 13: Proposed Research Project – 2015 Mar 4 
14 
i) 
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15. Jiao FF, Wong CKH, Fung SCC, McGhee S, Lam CLK. Cardiovascular risk prediction 
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