diabetic care performance report draft 08/12/16

COVER PAGE

This report is intended only for the use of the individual entity to which it is addressed and may contain information that is privileged, confidential, and exempt from disclosure.

Disclaimer:

The content of this report is based on information mainly gathered from published secondary sources. Although best efforts have been made to ensure that the information and data contained here is reliable, no representations are made as to its completeness, timeliness or quality. Majority of data originated from the Ministry of Health institutions hence generalizability is limited to the population of which they represent. Anyone may reproduce, publish or otherwise use the content of this report as the concepts and information herein are already in the public domain. However, acknowledgement to Malaysian Healthcare Performance Unit, Ministry of Health Malaysia would be appreciated.

Suggested citation is: Malaysia Diabetes Care Performance Report 2016 (2017) Malaysian Healthcare Performance Unit, Ministry of Health Malaysia, Kuala Lumpur.

Acknowledgement of Publication:

We would like to thank the Director General of Health Malaysia, for his permission to publish this article.

Published by:

Malaysian Healthcare Performance Unit 1st Floor, MMA House, 124 Jalan Pahang, 53000 Kuala Lumpur, Malaysia. Tel : (603) 40443060 Fax : (603) 40443080 Email : [email protected]


FOREWORD 1

The national landscape for diabetes mellitus (DM) is grim. It is undeniably one of our top priorities in tackling the burden of disease as the impact it has is significant not only upon families but also on the health system as well as the nation.

Against this backdrop, this baseline performance assessment of our health services in this area is timely. We now have estimation of some of the parameters of care and outcomes like rate of diabetes related admission and rate of diabetic complications based on validated clinical database. Not only that they are good for assessment, the distribution ie disaggregation by certain criteria such as state, district, urban/rural, ethnicity, gender and age-group would make policy makers and health managers more aware of variations in service provisions and outcomes and what can be done to approximate those gaps.

Although this report is limited concerning the level of data disaggregation, yet this preliminary report can be a catalyst for better data and hence better reports in future.

One cannot improve what one does not measure – with that I commend this effort at measuring our performance and I wish the team well for their future projects.

My advice is for all stakeholders to start using this and future reports for their policy and decision making with the common aim in mind for better care and better outcome for our rakyat!!

YBhg Datuk Dr Noor Hisham Abdullah


FOREWORD 2

MHP is the DG’s aspiration – as a mean to have a governance tool for monitoring and evaluating performance of the health system or subsystems.

2016 was MHP third year and this report is their second attempt at assessing a selected subsystem (disease-specific) performance assessment, their first being the cardiovascular care performance report.

The attempt to introduce performance assessment using scorecards is rather new in the nation’s health arena – people need to adapt to it and soon when they find it useful the work will become less challenging.

I must acknowledge that MHP have been successful in engaging the various stakeholders and data holders in converting readily available data into actionable information.

However there will always be room for future improvement, building on lessons learnt from this first report. I urge all the pertinent players to make use of the report.

Again I would like to remind that this report belongs to us; it is our assessment of our system, and thus we welcome any constructive feedbacks or comments.

Lastly I would like to thank all those who have contributed in many ways to make our DG’s aspiration a reality.

YBhg Datuk Dr Shahnaz Murad

ACKNOWLEDGEMENTS


We wish to thank YBhg. Datuk Dr Noor Hisham bin Abdullah, Director General of Health and Datuk Dr Shahnaz binti Murad, the Deputy Director General of Health (Research & Technical Support) for their guidance. We also sincerely thank the Director of National Clinical Research Centre, Dr Goh Pik Pin for her strong support. Acknowledgement of contribution goes to all state health directors and other stakeholders listed below:-

Y.Bhg. Datuk Dr Lokman Hakim b. Sulaiman Timbalan Ketua Pengarah Kesihatan (Kesihatan Awam) Kementerian Kesihatan Malaysia

Y.Bhg. Dato' Dr Hj Azman bin Abu Bakar Pengarah Bahagian Perkembangan Perubatan Kementerian Kesihatan Malaysia

Dr Md. Khadzir bin Sheikh Hj. Ahmad Timbalan Pengarah Pusat Informatik Kesihatan (PIK) Kementerian Kesihatan Malaysia

External reviewer

Professor Niek Klazinga

Health Care Quality Indicator Project Directorate for Employment, Label, and Social Affair (OECD)

Contributors

Dato’ Dr Hj Ahmad Razin Dato’ Hj Ahmad Mahir Pengarah Kesihatan Negeri Jabatan Kesihatan Negeri Kelantan

Dato’ Dr Hassan Merican Omar Naina Merican Wakil Pengarah Kesihatan Negeri Jabatan Kesihatan Negeri Pulau Pinang

Dato’ Dr Norhizan Ismail Pengarah Kesihatan Negeri Jabatan Kesihatan Negeri Kedah

Dr Wan Abdul Rahim Wan Muhammad Wakil Pengarah Kesihatan Negeri Jabatan Kesihatan Negeri Pahang

Dr Hjh Fatimah Othman Pengarah Kesihatan Negeri Jabatan Kesihatan Negeri Johor

Dr Fatimah Muda Wakil Pengarah Kesihatan Negeri Jabatan Kesihatan Negeri Terengganu

Dr Syed Mud Puad Syed Amran

Wakil Pengarah Kesihatan Negeri Jabatan Kesihatan Negeri Perak

Dr Bariyah Kadas Wakil Pengarah Kesihatan Negeri Jabatan Kesihatan Negeri Selangor

Dr Lokman Rejali Wakil Pengarah Kesihatan Negeri Jabatan Kesihatan Negeri Sembilan

Dr Amirullah Ahmad Arshad Wakil Pengarah Kesihatan Negeri Jabatan Kesihatan Negeri Melaka

Dr Mohd Zaki Abdul Hamid Pengarah Kesihatan Negeri Jabatan Kesihatan Labuan

Dr G. R. Letchuman Ramanathan


Pakar Perunding Kanan Perubatan & Ketua Jabatan Perubatan, Hospital Raja Permaisuri Bainun, Ipoh

Dr Muhammad Arif B Mohd Hashim Wakil : En Ahmad Bazli B Abu Shuib Pakar Perunding Kanan Patologi & Ketua Jabatan Patologi Hospital Kuala Lumpur

Dr Zanariah Bt Hussein Pakar Perunding Endokrinologi, Hospital Putrajaya

Dr Rozlan B Ishak Timbalan Pengarah Penyakit (PTB), Cawangan Penyakit Tidak Berjangkit (NCDC) Bahagian Kawalan Penyakit,

Dr Kamaliah Bt Mohamad Noh Timbalan Pengarah Cawangan Kesihatan Primer Bahagian Pembangunan Kesihatan Keluarga

Dr Fathullah Iqbal Abdul Rahim Timbalan Pengarah Pusat Informatik Kesihatan

Dr Vickneshwari Ayadurai Pakar Perubatan Keluarga Klinik Kesihatan Taman Medan

Dr Muhammad Fadhli Mohd Yusoff Pakar Kesihatan Awam Institute Kesihatan Umum

Dr.Noraini Mohd Yusoff Ketua Penolong Pengarah Kanan Cawangan Kesihatan Primer Bahagian Pembangunan Kesihatan Keluarga

Dr.Fatanah Binti Ismail

Ketua Penolong Pengarah Kanan Cawangan Kesihatan Primer Bahagian Pembangunan Kesihatan Keluarga

Dr.Noor Raihan Khamal Ketua Penolong Pengarah Kanan Cawangan Penyakit Tidak Berjangkit

Dr.Masliha Harun Ketua Penolong Pengarah Kanan Cawangan Penyakit Tidak Berjangkit Bahagian Kawalan Penyakit

Mohd Nazri Abdullah Cawangan Penyakit Tidak Berjangkit Bahagian Kawalan Penyakit

Siti Fairuz Mohd Zukri Jabatan Perangkaan Malaysia

Dr Rotina Bt Abu Bakar Ketua Penolong Pengarah Kanan, Bahagian Kawalan Penyakit

Datin Dr Siti Haniza Bt Mahmud Setiausaha Institut Penyelidikan Sistem Kesihatan

Dr Nazrila Hairizan Bt Nasir Pakar Kesihatan Keluarga Klinik Kesihatan Putrajaya

Dr Omar B Mihat Ketua Sektor Kesihatan Mental, VIP & AST, Khas C, Bahagian Kawalan Penyakit

Puan Suhaya Bt Komari Penolong Pengarah Kanan Gred E44 Pusat Informatik Kesihatan

Puan Viola Michael Pegawai Dietetik U54, NCD, Bahagian Kawalan Penyakit

Malaysian Healthcare Performance Unit

Team lead:

Dr. Jamaiyah Haniff

Project lead:

Dr Mohd Kamarulariffin Kamarudin

Members:

Dr Nor Aini Abdullah Dr Theyveeka Selvy Rajoo Dr Ariza Zakaria Cik Nadiah Hanis Hashim Cik Nuramalina Abdullah


CONTENTS

List of tables ........................................................................................................................................................................ ii

List of figures ..................................................................................................................................................................... iii

List of abbreviations ....................................................................................................................................................... iv

EXECUTIVE SUMMARY .................................................................................................................................................. 5

MALAYSIAN DIABETES CARE PERFORMANCE SCORECARD ........................................................................ 7

Chapter 1 Introduction ................................................................................................................................................... 9

Background ................................................................................................................................................................. 10

Objectives ..................................................................................................................................................................... 10

Methodology & Analysis ........................................................................................................................................ 11

Chapter 2 Malaysian Diabetes Profiling ............................................................................................................... 16

Demographics ............................................................................................................................................................ 17

Prevalence of DM ...................................................................................................................................................... 18

Prevalence of DM Risk Factors ............................................................................................................................ 20

Chapter 3 Diabetes Care Performance Indicators............................................................................................ 23

Where we stand ......................................................................................................................................................... 24

Human Resources & Primary Care Facilities ................................................................................................ 25

Process of Care ........................................................................................................................................................... 27

Health Outcome ......................................................................................................................................................... 29

Admission Rate ..................................................................................................................................................... 29

Chronic Complications ....................................................................................................................................... 31

Clinical Targets ..................................................................................................................................................... 33

Conclusion ........................................................................................................................................................................ 35

Recommendations......................................................................................................................................................... 36

APPENDIX ......................................................................................................................................................................... 37

BIBLIOGRAPHY .............................................................................................................................................................. 61

GLOSSARY ......................................................................................................................................................................... 62

LIST OF TABLES


Table 1: Data availability, coverage, and levels of disaggregation of selected performance indicators .......................................................................................................................................................................... 13 Table 2: Prevalence of diabetes, 2006-2015 ..................................................................................................... 18 Table 3: State variation of diabetes prevalence, 2015 .................................................................................. 19 Table 4: Diabetes risk factor prevalence, 2015 ................................................................................................ 21 Table 5: State variations of diabetes risk factor prevalence, 2015 .......................................................... 21 Table 6: Human resources for health and primary care facility distribution, 2015 or the nearest year ...................................................................................................................................................................................... 25 Table 7: Clinical target achievement, 2012 ........................................................................................................ 34

LIST OF FIGURES

Figure 1: Breakdown of cases by age group, 2009-2012 ............................................................................. 17 Figure 2: Breakdown of cases by ethnicity, 2009-2012 ............................................................................... 17 Figure 3: Trend of national prevalence of diabetes, 2006-2015 ............................................................... 18 Figure 4: Distribution of undiagnosed diabetes by age group, 2015 ...................................................... 19 Figure 5: Trend of diabetes risk factors prevalence, 2006-2015 ............................................................. 20 Figure 6: Malaysian rankings of selected performance indicators .......................................................... 24 Figure 7: State variation of primary care facility density, 2012................................................................ 26 Figure 8: Yearly trend of diabetes process of care, 2009-2012 ................................................................ 27 Figure 9: Hospital admission rate for uncontrolled dm with or without complications per 100 000 population, 2010-2014 ...................................................................................................................................... 29 Figure 10: Rate of diabetic complications, 2011 vs 2012 ............................................................................ 31 Figure 11: Yearly trend of diabetes clinical target achievement, 2009-2012 ..................................... 33


LIST OF ABBREVIATIONS

AAR Average Annual Rate

BMI Body Mass Index

BP Blood Pressure

BPKK Bahagian Pembangunan Kesihatan Keluarga

CPG Clinical Practice Guideline

DM Diabetes Mellitus

FBG Fasting Capillary Blood Glucose

FMS Family Medicine Specialist

GP General Practitioner

IDF International Diabetes Federation

IFG Impaired Fasting Glucose

KK Klinik Kesihatan (Health clinics)

LDL Low Density Lipoprotein

MEMS Malaysian Endocrine & Metabolic Society

MOH Ministry of Health Malaysia

NADI National Diabetes Institute

NCD Non-communicable Disease

NDR National Diabetes Registry

NGO Non-profit Non-governmental Organization

NHEWS National Health Care Establishment and Workforce Statistics

NHMS National Health and Morbidity Survey

OECD Organization for Economic Co-operation and Development

SMRP Sistem Maklumat Rawatan Perubatan

WHO World Health Organization


EXECUTIVE SUMMARY

Malaysian diabetic scenarios

The prevalence of diabetes mellitus (DM) is on the rise (the average annual growth is about 5%); about half consists of undiagnosed cases. This phenomenon is likely attributable to the increasing prevalence of obesity and pre-diabetes state in the general population.

Among the states, Kedah recorded the highest prevalence of DM while WP Putrajaya recorded the highest prevalence of adult obesity.

Age-standardized prevalence of DM in Malaysia is almost three times higher than that of OECD-34 average, and remarkably higher than the majority of comparator countries.

Age-standardized prevalence of obesity in Malaysia is almost double the prevalence of obesity in China, Singapore, Indonesia and India.

The Malaysian prevalence of physical inactivity in 2010 was the highest among the comparator countries.

Resources and facilities

We observe a positive growth in human capital and number of health care establishments that provide diabetes services in Malaysia from 2009 to 2015.

There are unequal distributions of resource between states. State of Kelantan has comparatively lower primary care facility density per population but is among the states with highest prevalence of DM. On the other hand, Kuala Lumpur has the highest primary care facility density per population but is among the states with the lowest prevalence of DM.

Private General Practitioners (GP) provide the widest coverage of primary care services inclusive of diabetes care services in Malaysia. This phenomenon may put affordability as the key driver to accessibility.

Process of care

From 2009 to 2012, the proportion of patients with diabetes in the health clinics who received the expected care (funduscopy, urine microalbumin and HbA1c testings) increased by an annual rate of 6%, 8% and 5% respectively. In the same time period, proportion of patients who achieved clinical target for HbA1c and LDL had increased from 19% to 24% and 31% to 38% respectively.

However, the number of patients with diabetes with normal BMI (< 23kg/m2) is less than one fifth of the total population with diabetes. In addition, patients with diabetes that have blood pressure controlled within the accepted range (< 130/80mm/Hg) are only less than half of the total population with diabetes.


Outcomes

There was an overall decrease in the rate of diabetes related admission from 2010 to 2015 in public hospitals. However, the admission rate trend particularly for diabetes with short term complications was the opposite; the rate in 2015 was higher than the rate in 2010.

The rate of diabetic retinopathy, nephropathy, myocardial infarction, diabetes foot ulcer and lower limb amputation has decreased by a factor of about one to seven per cent. However, rate of cerebrovascular complications have increased by five per cent since 2011.

The reported prevalence of complications among diabetes patients in primary care settings in Malaysia is generally lower than the comparator countries potentially due to under-reporting.


MALAYSIAN DIABETES CARE PERFORMANCE SCORECARD

Year Achievement AAR (%)

AAR

period Standard

Input

1. Endocrinologist per 100 000 populationPP

2015 0.24 - - -

2. Number of Family Medicine Specialist (FMS)P

2015 281 ↑8.6 2009-2015 -

3. KK to population ratio 2015 1: 32 519 - - -

4. Number of KK with FMS 2015 242 ↑6.2 2009-2015 -

5. Primary care facility per 10 000 populationPP

2012 2.2 - - -

Process of care

1. % Patients who had funduscopy done within the last 1 yearp

2012 44.0% ↑6.4 2009-2012 -

2. % Patients who had foot examination done within the last 1 yearp

2012 73.0% ↑0.3 2009-2012 -

3. % Patients who had urine checked for micro-albumin within the last 1 yearp

2012 56.7% ↑7.7 2009-2012 -

4. % Patients who had HbA1c test done at least once within the last 1 yearp

2012 78.0% ↑4.7 2009-2012 -

Outcome (Hospital admission per 100 000 population)

1. Uncontrolled DM without complicationspp

2015 58 ↓ 7.6 2010-2015 -

2. DM with short term complicationspp 2015 19 ↑ 7.7 2010-2015 -

3. DM with long term complicationspp 2015 69 ↓ 1.0 2010-2015 -


Year Achievement AAR*

(%) AAR period Standard

Outcome (Diabetic complications in primary care)

1. Rate of retinopathyp 2012 7.9% ↓6.5 2011-2012 -

2. Rate of nephropathyp 2012 8.9% ↓6.6 2011-2012 -

3. Rate of myocardial infarctionp 2012 6.0% ↓1.3 2011-2012 -

4. Rate of cerebrovascular diseasep 2012 1.4% ↑4.7 2011-2012 -

5. Rate of diabetic foot ulcerp 2012 1.4% ↓3.0 2011-2012 -

6. Rate of amputationp 2012 0.7% ↓0.6 2011-2012 -

Outcome (Clinical target achievements in primary care)

1. % Patients who achieved BP target ≤ 130/80mm/Hgp

2012 40.9% ↓ 0.2 2009-2012 -

2. % Patients who achieved HbA1c target < 6.5%p

2012 23.8% ↑ 7.1 2009-2012 >30%1

3. % Patients who had BMI < 23kg/m2

p 2012 16.6% ↓ 1.2 2009-2012 -

4. % Patients who achieved LDL target ≤ 2.6mmol/Lp

2012 37.8% ↑ 7.3 2009-2012 -

Impact

1. Prevalence of obesity (BMI ≥ 27.5 kgm

-2)

2015 30.6% ↑ 3.0 2011-2015 -

2. Prevalence of overweight (BMI ≥ 23.0 kgm

-2)

2015 64.0% ↑ 1.4 2011-2015 -

3. Prevalence of IFG 2015 4.7% ↑ 1.3 2006-2015 -

4. Prevalence of insufficient physical activity

2015 33.5% ↓ 2.9 2006-2015 -

5. Prevalence of overall DM 2015 17.5% ↑ 4.7 2006-2015 15.0%2

P Public

PP Public and Private

1 Target from Malaysian Clinical Practice Guideline “Management of Type 2 Diabetes Mellitus (5th Edition) 2 Target for Malaysia by 2025: WHO NCD Global Monitoring Framework. Baseline year 2010 WHO


CHAPTER 1 INTRODUCTION


BACKGROUND

The progression of diabetes and cardiovascular diseases is a concern worldwide. National Health and

Morbidity (NHMS) survey, a series of community-based survey on the pattern of common health

problems, health service utilisation and health expenditure in Malaysia reports a steady uprising of

cardiovascular disease risk factor since 1986. One in three Malaysians were hypertensive; about one

in five were diagnosed with hypercholesterolemia; and one fifth of the population were found to

have diabetes (Institute for Public Health, 2015). These problems are expected to contribute further

to the growing incidence of cardiovascular complications namely the acute coronary syndrome (ACS)

and stroke.

To reduce the burden of disease, health promotion and disease prevention remain the most efficient

strategies as they could provide opportunities to curb diseases at earlier stages when the diseases

are often more responsive to treatment. Having implemented preventive measures or interventions,

there must be a mechanism in place for regular monitoring and evaluation in order to understand

underlying processes that may have resulted in any unintended outcomes.

Many different divisions within the Ministry of Health have been collecting data in various aspects of

health care as part of administrative, academic, clinical and quality improvement work. Malaysian

Healthcare Performance unit (MHPU) was established to transform those various databases into

actionable information as well as to benchmark health performance against best practices locally or

internationally. We aim to identify variation in practices and health outcomes in order to promote

health care innovation and improvement in the care delivery.

This report is intended to serve as a foundation for a more comprehensive work pertaining to

reporting of diabetes care performance in Malaysia. This current report focuses on performance

assessment of diabetes care before the year 2015 following a framework that incorporates various

health care system and quality domains used by the World Health Organization (WHO)(Organization

2003).

This report consists of three chapters. The first chapter is an introduction to health system

performance assessment, the report methodology and analysis. The second chapter describes the

demographic profiling of the population with type 2 diabetes in Malaysia. The third chapter reports

statistics concerning the service inputs and resources, the critical aspect of process of care in adults

with type 2 diabetes and the system outcomes.

OBJECTIVES

1. To describe the magnitude of current diabetic burden in Malaysia

2. To describe the performance of diabetes care based on latest available data.

3. To show the performance trending information

4. To describe variation in performance by state

5. To benchmark performance against OECD countries or other selected comparator countries.


METHODOLOGY & ANALYSIS

We employed disease-based approach to reflect performance across health programme and services.

The choice of diabetes as the topic is in keeping with the findings of Malaysian Burden of Disease and

Injury study (Ahmad Faudzi Yusoff, Gurpreet Kaur, Mohd Azahadi Omar, & Amal Nasir Mustafa, 2004)

that reported diabetes, ischemic heart disease and cerebrovascular disease as the top contributors to

mortality and disability-adjusted life year (DALY) in Malaysia.

Literature search was done on the topic related to quality of care and health care performance

assessment for diabetes using online search engines. The searching was purposive with priority given

to review articles and local studies that mention performance assessment in diabetes care or its

equivalents. A table comprising a list of diabetes care indicators was constructed and filled along as

new indicators were found during the literature search period. The indicators were grouped into the

domains of input, process, outcome and impact that reflect Donabedian’s conceptual model of

assessing quality in health system (Avedis Donabedian, John R. C. Wheeler, & Leon Wys, 1982). Each

indicator was deliberated by the MHPU team on the suitability (relevance in local context) and

potential data sources.

Majority of the indicators and their definition were taken verbatim from their respective source

documents. These are the commonly used indicators to describe health system performance

internationally. However, some of the indicators were replaced with an equivalent proxy to

accommodate local data definition and data availability. Corresponding data were synthesised from

published related documents and surveillance reports; they were reconstructed into a data frame in

an electronic spread sheet. Majority of the published data were aggregated numbers or rates at

either state or national level. Detailed information on the datasets used and their sources, coverage,

and levels of disaggregation are given in Table 1.

Data cleaning process involved manual tracking of wrongly entered value by at least one other

person using the original source documents as reference. A statistician service was utilised for data

proof-reading and reviewing of mathematical formula and rate calculation. If any discrepancies, the

correction was done on the master data spread sheet by the same person who constructed the data

frame.

Analysis largely involved visual analytics; plotting data using column or line graph- looking at the

trending over time or constructing three dimensional scatter plots- assessing the visual correlation.

No adjustment or standardization of rate was attempted.

In this report, performance is measured in four ways:

1. Whether or not Malaysia has reached a specific achievement target/standard.

- Health related activities and outcomes are measurable; a target is ideally set to assess

the progress of any implemented strategy in health promotion or intervention.

2. Whether there have been desirable changes in the latest achievement compared to the

achievement of previous years.

- Desirable changes are expected trend of achievement based on historical data. An

increasing trend is desirable for positive outcomes while decreasing trend is desirable for

unwanted outcomes.


3. The average annual rate of change (AAR) of the achievements over a time period ;

- AAR is calculated by taking the geometric mean of the annual percentage difference

between the baseline achievement (beginning value) and the current achievement

(ending value) with the assumption that the achievements have been compounding over

the specified time period. The mathematical formula is given by:

AAR = (

)(

)

4. Malaysia achievement in comparison with selected OECD countries and other comparator

countries.

- OECD has an online updated database that conglomerate common statistics from various

member countries in time series. OECD as a benchmark is our attempt to close the gap

so as to achieve the standard enjoyed by developed nations.

All indicators along with the data were discussed with and presented to the respective stakeholders

for reconciliation. Improvement and additional work were tailored according to the stakeholders’

suggestions and needs.

Findings are reported using the following symbols and colour codes: ↑ Increased since previous year Desirable change ↓ Decreased since previous year Indicator of concern → Remained the same since previous year Status quo - Missing or unavailable information

A note about making comparisons (limitation):

All indicators are presented in crude measure without adjustment to baseline characteristics or risks. Therefore, comparability of rates between different periods of data collection or regions is limited.

The report findings are based on secondary aggregated data. Degree of ascertainment, duplications, and missing data could not be fully verified.

Majority of data represents only the public sectors and Ministry of Health institutions although the interpretations in this report are meant to describe the Malaysian scenarios as whole. Potentially, there are data from other sectors that have yet to be explored by the time this report is published

Due to the nature of this report that used aggregated data from multiple sources, direct causality or longitudinal relationship cannot be established and the findings cannot be inferred without estimate adjustment or predictive modelling.


Table 1: Data availability, coverage, and levels of disaggregation of selected performance indicators

Indicator Time period Coverage Data source Domain

1. Prevalence of DM

Overall

Known DM

Undiagnosed DM

2006-2015 By state NHMS (Published)

Impact

2. Prevalence of obesity (≥ 27.5 kg/m2)


Impact

3. Prevalence of pre-obese (23-<27.5 kg/m2)


Impact

4. Prevalence of impaired fasting glucose (IFG) 2006-2015 By state

NHMS (Published)

Impact

5. Prevalence of insufficient physical activity 2006-2015 By state

NHMS (Published)

Impact

6. Diabetes age-adjusted comparative prevalence 2015

National aggregate

IDF (Published)

Impact

7. Obesity age-standardized adjusted prevalence 2014

National aggregate

WHO (Published)

Impact

8. Insufficient physical activity age-standardized prevalence 2010

National aggregate

WHO (Published)

Impact

9. Number of Endocrinologist 2009-2015 By state

NHEWS (Published) MEMS (Unpublished)

Input

10. Number of FMS 2008-2015

By state

MOH only

BPKK (Unpublished)

Input

11. Primary care clinics to population ratio 2012 By state

NHEWS (Published)

Input

12. Endocrinologist density per 100 000 population by state 2015 By state

MEMS (Unpublished)

Input


Table 1: Data availability, coverage, and levels of disaggregation of selected performance indicators

(continued)

Indicator Time period Coverage Data source Category

13. Proportion of diabetics who have had:

• Urine test for micro-albumin • Funduscopy • Foot examination • HbA1c test

Done within the last 1 year of the audit period.

2009-2012

National aggregate

MOH only

NDR (Published)

Process of care

14. Proportion of diabetics who have achieved clinical target:

• BP ≤ 130/80 mmHg • BMI < 23 kg/m

2

• LDL ≤ 2.6 mmol/L • HbA1c < 6.5 %

2009-2012

National aggregate

MOH only

NDR (Published)

Outcome

15. Proportion of diabetics with complications:

• Retinopathy • Nephropathy • MI • Cerebrovascular • DFU • Amputation

2009-2012

National aggregate

MOH only

NDR (Published)

Outcome

16. Admission rate by state

• Uncontrolled DM without complications

• DM with short term complications

• DM with long term complications • Total DM related admission

2010-2014

National aggregate

MOH only

SMRP (Unpublished)

Outcome

NHEWS: National Healthcare Establishments & Workforce Statistics (Hospital) 2008-2009

National Healthcare Establishments & Workforce Statistics (Hospital) 2012-2013

National Healthcare Establishments & Workforce Statistics (Primary Care) 2012

NDR: National Diabetes Registry Report, Volume 1, 2009-2012

NMCS: National Medical Care Statistics (Primary Care) 2014

NHMS: The Third National Health and Morbidity Survey (NHMS III) 2006, Vol 2

National Health and Morbidity Survey 2011 (NHMS 2011). Vol. II: Non-Communicable Diseases

National Health and Morbidity Survey 2015 (NHMS 2015). Vol. II: Non-Communicable Diseases, Risk Factors & Other Health

Problems

OECD: Health Statistics 2015 stats.oecd.org


BPKK: Bahagian Pembangunan Kesihatan Keluarga

IDF: International Diabetes Federation, Annual Report 2013

International Diabetes Federation, Annual Report 2015

WHO: Global Status Report on Non-Communicable Disease 2014

SMRP: Sistem Maklumat Rawatan Perubatan

MEMS: Malaysian Endocrine & Metabolic Society


CHAPTER 2 MALAYSIAN DIABETES PROFILING


DEMOGRAPHICS

Demographic breakdown for T2DM population in Malaysia based on National Diabetes Registry

(NDR) report is illustrated in Figure 1 and Figure 2 for age groups and ethnicity respectively. From

2009 to 2012 there were 653,326 registered T2DM patients. The mean age was 59.7 years old;

41.6% were men and 58.4% were women. In term of ethnicity, 58.9% were Malay, 21.4% were

Chinese and 15.3% were Indian (Ministry of Health, 2013).

Half of the population with T2DM were from the 45-54 age group or younger. The racial distribution

of the registry population reflected the general Malaysian ethnic compositions which were made up

mostly by the Malay ethnic group (Jabatan Perangkaan Malaysia, 2015). However, until 2012 only

644 KKs out 959 KKs were involved in NDR data entry. Due to potential case ascertainment bias,

population inference of any statistical estimates in the report may be limited.

FIGURE 1: BREAKDOWN OF CASES BY AGE GROUP,

2009-2012

FIGURE 2: BREAKDOWN OF CASES BY ETHNICITY,

2009-2012

Data source: National Diabetes Registry Report 2009-2012

<18 0.2

18-29 2.1

30-44 20.1

45-54 32.6

55-64 28.7

65-79 15.2

≥80 1.1

Malay 58.9

Chinese 21.4

Indian 15.3

Other Malaysian

4.2

Foreigner/ Unknown

0.2


PREVALENCE OF DM

T2DM is a global public health concern. Four out of five people with T2DM now live in low to middle

income countries (World Health Organization, 2010). The rise of T2DM in those regions may be due

to urbanisation and economic development that have led to changes in the population lifestyle.

Furthermore, the phenomenon of population aging may have also contributed to the increase in the

number of population at risk. Malaysian scenario is no different than the observed global trend.

National Health Morbidity Survey (NHMS) demonstrates the phenomenon of high blood glucose

level progression in Malaysia over a period of two decades (2006-2015). The prevalence of diabetes

among the respondents aged 18 and above was showed to have increased over time.

FIGURE 3: TREND OF NATIONAL PREVALENCE OF DIABETES, 2006-2015

Data source: National Health and Morbidity Survey

Table 2: Prevalence of diabetes, 2006-2015

2006 (%)

2011 (%)

2015 (%)

AAR (%)

1.1. Prevalence of overall DM 11.6 15.2 17.5 ↑ 4.7

1.2. Prevalence of undiagnosed DM 4.5 8 9.2 ↑ 8.3

1.3. Prevalence of known DM 7 7.2 8.3 ↑ 1.9

* AAR is calculated for period 2006-2015 Data source: National Health and Morbidity Survey

11.6

15.2

17.5

39%

53% 53%

2006 2011 2015

%

Undiagnosed

Known


Data source: National Health and Morbidity Survey 2015

Table 3: State variation of diabetes prevalence, 2015

Prevalence of Overall DM

Prevalence of Undiagnosed DM

Prevalence of Known DM

Kedah 25.4 ↑ 7.2% 16.1 ↑ 15.8% 9.3 = 0%

Perlis 20.6 ↑ 4.8% 9.7 ↑ 6.5% 10.9 ↑ 3.5%

Johor 19.8 ↑ 6.6% 11 ↑ 11.6% 8.8 ↑ 2.6%

Perak 19.4 ↑ 4.9% 7.4 ↑ 4.5% 11.9 ↑ 5.1%

Negeri Sembilan 19.3 ↑ 2.6% 8.8 ↑ 3.2% 10.5 ↑ 2.0%

WP Putrajaya 19.2 ↑ 21.5% 13.9 ↑ 34.9% 5.3 ↑ 3.6%

Terengganu 18.6 ↑ 5.9% 9.9 ↑ 7.0% 8.7 ↑ 4.8%

Kelantan 18.5 ↑ 5.2% 11.3 ↑ 9.7% 7.1 ↑ 0.5%

Pulau Pinang 18.1 ↑ 2.2% 9.1 ↑ 4.7% 9 ↑ 0.1%

WP Kuala Lumpur 17.4 ↑ 3.7% 9.3 ↑ 5.0% 8.1 ↑ 2.3%

Melaka 16.7 ↑ 1.1% 8.4 ↑ 8.9% 8.3 ↓ 3.5%

Selangor 15.5 ↑ 2.9% 7.7 ↑ 8.2% 7.8 ↓ 0.7%

Pahang 14.8 ↑ 2.3% 8.6 ↑ 8.9% 6.3 ↓ 2.8%

Sarawak 14.8 ↑ 4.5% 6.6 ↑ 1.1% 8.3 ↑ 8.2%

Sabah & WP Labuan 14.2 ↑ 12.1% 8.3 ↑ 6.7% 5.9 ↑ 22.7%

* AAR is calculated for period 2006-2015 2011-2015 for WP Putrajaya and Sabah & WP Labuan Data source: National Health and Morbidity Survey

What does this mean for Malaysia? 1. The prevalence of DM among the general population is on the rise and about half were

undiagnosed. Kedah, Perlis and Johor are the states with the highest DM prevalence. 2. Prevalence of undiagnosed DM cases in Malaysia is growing more rapidly than the

prevalence of known DM with annual increment of 8.3% and 1.9% respectively. Higher prevalence of undiagnosed cases can be found in Kedah and WP Putrajaya.

3. About 90% of cases below the age of 30 were undiagnosed. 4. The proportion of undiagnosed cases and the rate at which it grows is a concern. Unless

efforts are concentrated on active screening on seemingly healthy population, the burden of disease related to cardiovascular disease and diabetes will only become more evident.

0%

20%

40%

60%

80%

100%

Known DM Undiagnosed DM

FIGURE 4: DISTRIBUTION OF UNDIAGNOSED DIABETES BY AGE GROUP,

2015


PREVALENCE OF DM RISK FACTORS

Despite the efforts undertaken since 1990s through “Kempen Cara Hidup Sihat”, the prevalence of

NCDs and NCD risk factors continue to rise. A situational analysis in 2010 revealed that the

implementation of NCD programs and activities were confined only within the Ministry of Health

territories (Ministry of Health, 2010). Therefore, National Strategic Plan for NCD was implemented

to encourage inter-sectorial collaboration and address lack of policy in creating health promoting

environment in Malaysia (Ministry of Health, 2010). In 2013 the Ministry of Health initiates a

program “Komuniti Sihat Perkasa Negara” (KOSPEN) that serves as an impetus to boost the existing

health promoting mechanism and intervention at the community level

(http://www.infosihat.gov.my/infosihat/projekkhas/kospen.php). Subsequently, we have seen

improvement in some areas based on findings from National Health and Morbidity (NHMS) surveys

from 2006 to 2015.

*Prevalence for overweight in 2006 follows BMI ≥25kg/m

2


Obesity is defined using a lower threshold (BMI ≥ 27.5kg/m2) than the WHO international standard

(BMI ≥ 30 kg/m2) to reflect higher percentage of body fat and risk of cardiovascular disease or

diabetes at lower BMI among Asian populations (World Health Organization, 2004).

Impaired fasting glucose (IFG) denotes pre-diabetes state and is categorized based on capillary whole

blood glucose level within 5.6-6.1mmol/L range taken from subject who fasted for at least 8 hours

(Institute for Public Health, 2015).

Insufficient physical activity is considered when the total MET/minute based on recalled activity is

less than accumulated 150 minutes per week of moderate-intensity activity or equivalent (Institute

for Public Health, 2015).

FIGURE 5: TREND OF DIABETES RISK FACTORS PREVALENCE, 2006-2015

4% 5% 5%

2006 2011 2015

Prevalence of IFG

44% 35% 34%

2006 2011 2015

Prevalence of insufficient physical activity

43%*

61% 64%

2006 2011 2015

Prevalence of overweight BMI ≥ 23 kgm-2

Prevalence of obesity

Prevalence of pre-obese


Table 4: Diabetes risk factor prevalence, 2015

Period Value

(%) Target

3

AAR (%)

Comparison (%)

4.1. Prevalence of obesity 2015 30.6 No

increase ↑ 3.0 -

4.2. Prevalence of pre-obese 2015 33.4 No

increase ↑ 0.1 -

4.3. Prevalence of IFG 2015 4.7 - ↑ 1.3 -

4.4. Prevalence of insufficient physical activity

2015 33.5 30.0† ↓ 2.9 -

4.5. Male waist circumference > 90cm 2015 38.2 - ↑0.5 -

4.6. Female waist circumference > 80cm 2015 60.2 - ↑2.7 -

3 Target for Malaysia by 2025: WHO NCD Global Monitoring Framework. Baseline year 2010.

† Target by 2025; Reduction by 10% from baseline year 2010 * AAR is calculated for period 2006-2015 for 4.3 - 4.6; 2011-2015 for 4.1 & 4.2 Data source: National Health and Morbidity Survey

Table 5: State variations of diabetes risk factor prevalence, 2015

Prevalence of

obesity Prevalence of pre-

obese Prevalence of

IFG

Prevalence of insufficient

physical activity

WP Putrajaya 43.0 ↑ 12% 34.4 ↓ 1.3% 6.5 ↑ 53% 32.5 ↓ 13%

Melaka 36.0 ↑ 5.3% 33.6 ↓ 0.1% 3.5 ↓ 0.9% 37.8 ↓ 0.4% Perlis 36.0 ↑ 1.0% 30.7 ↑ 1.3% 4.0 ↑ 1.8% 27.8 ↓ 6.1%

Negeri Sembilan 35.6 ↑ 6.0% 30.4 ↓ 0.7% 6.4 ↑ 11% 33.7 ↓ 0.3% Kedah 33.2 ↑ 4.2% 32.4 ↓ 1.2% 6.4 ↑ 4.3% 33.6 ↓ 1.0%

Selangor 32.7 ↑ 3.8% 33.4 ↑ 0.8% 4.3 ↓ 0.7% 39.9 ↓ 2.9% Pahang 32.5 ↑ 3.6% 31.5 ↓ 0.5% 4.3 ↑ 2.6% 26.0 ↓ 2.1%

Terengganu 32.5 ↑ 3.7% 29.7 ↓ 2.4% 3.5 ↓ 1.5% 30.7 ↓ 0.6% Sarawak 32.3 ↑ 4.9% 31.0 ↓ 1.1% 2.9 ↓ 5.4% 40.8 ↓ 1.8%

Johor 29.8 ↑ 1.0% 33.5 ↑ 0.4% 7.2 ↑ 11% 32.9 ↓ 3.6% WP Kuala Lumpur 29.6 ↑ 6.7% 34.2 ↓ 1.9% 3.8 ↓ 5.1% 36.4 ↓ 3.8%

Perak 29.5 ↓ 0.5% 34.7 ↑ 2.5% 5.3 ↑ 0.6% 28.1 ↓ 4.8% Kelantan 28.8 ↓ 0.5% 30.7 ↓ 3.4% 6.6 ↑ 4.3% 25.8 ↓ 3.1%

Pulau Pinang 27.8 ↑ 0.8% 37.3 ↑ 0.7% 4.2 ↓ 4.1% 25.5 ↓ 5.2% Sabah & WP Labuan 23.9 ↑ 3.2% 36.9 ↑ 1.7% 3.3 ↓ 6.9% 30.1 ↑ 5.0%

* AAR is calculated for period 2006-2015

AAR is calculated for period 2011-2015 for Sabah & WP Labuan and WP Putrajaya


3 NCD Global Monitoring Framework. (2015). Retrieved October 06, 2016, from http://www.who.int/nmh/global_monitoring_framework/en/


What does this mean for Malaysia? 1. 64% of general population were overweight (BMI ≥ 23) and half of that exceeded the obesity

threshold (BMI ≥ 27.5) in 2015. 2. WP Putrajaya has the highest prevalence of obesity compared to other states. 3. There is a modest increase in the prevalence of IFG from 4.2% in 2006 to 4.7% in 2015. 4. People are getting more physically active with decreasing proportion of population with

insufficient physical activity over the 2006-2015 periods. Nonetheless, about one third of the population still remained inactive in 2015.

5. The increasing prevalence of obesity and pre-diabetic states are among the key contributors to diabetic burden in Malaysia. However, a more comprehensive review must be made in the light of other important covariates like dietary habit or other unmeasured social confounders.

6. Weight circumference (WC) is an independent risk factor for diabetes. WC as an indicator highlights predisposition to diabetes even among population with normal BMI.


CHAPTER 3 DIABETES CARE PERFORMANCE INDICATORS


WHERE WE STAND

Our national health priorities include enhancing the health care delivery system to increase access to quality care, and reducing the disease burden, both communicable and non-communicable diseases. While we have achieved commendable improvement in the life expectancy and maternal and child mortality indicators, we are still behind in our performance in comparison to the countries with higher economic status. World Health Organization (WHO) and International Diabetes Federation (IDF); two important international advocators for diabetes prevention came out with the global action plan focusing particularly on lifestyle interventions in hope of reducing the burden of diabetes that is affecting all over the world especially in the developing countries (World Health Organization, 2013). In line with the global aspiration, we continue to work within our capacity to pursue the goal of delivering a world-class health care system. There are indeed variations and differences between health system of different countries in term of its capacity, processes and outcomes depending on the system intended objectives. However, making OECD countries as our benchmark will give us insight into the potential improvement that can be undertaken at our local settings given the aspiration of Malaysia becoming one of the developed nations.

FIGURE 6: MALAYSIAN RANKINGS OF SELECTED PERFORMANCE INDICATORS

Diabetes age-adjusted

comparative prevalence 2015

Obesity age-standardized adjusted prevalence

2014

Insufficient physical activity age-standardized

prevalence, 2010

Malaysia 17.9 United States 33.7 Malaysia 52.3 United States 10.8 New Zealand 29.2 New Zealand 39.8

Singapore 10.5 Australia 28.6 United Kingdom 37.3 China 9.8 United Kingdom 28.1 Singapore 33.1 India 9.3 OECD-34 23 United States 32.4

Germany 7.4 Germany 20.1 OECD-31 26.1 New Zealand 7.3 Malaysia 13.3 China 24.1

OECD-34 7 China 6.9 Australia 23.8 Indonesia 6.5 Singapore 6.2 Indonesia 23.7 Australia 5.1 Indonesia 5.7 Germany 21.1

United Kingdom 4.7 India 4.9 India 13.4

Data source: IDF Diabetes Atlas 2015 (7

th Edition)

WHO Global Status Report on NCDs 2014

What does this mean for Malaysia?

1. The age-standardized prevalence of diabetes in Malaysia is 2.6 times higher than the OECD-34, and remarkably higher than some regional countries.

2. The age-standardized prevalence of obesity in Malaysia almost doubles the prevalence in China, Singapore, Indonesia and India.

3. Malaysian prevalence of insufficient physical activity is the highest among the OECD countries.

4. Statistics regarding sedentary lifestyle and obesity are commonly reported but both factors are only few out of many other risk factors that can be associated with diabetes. However, the chances are likely that these two risk factors contributed a significant portion to the diabetic scenario in Malaysia.


HUMAN RESOURCES & PRIMARY CARE FACILITIES

Generating resources or inputs is one of the functions a health system performs along with stewardship, financing and health service provision. Inputs to the health system are combined to allow the delivery of a series of interventions or health actions with the final objectives mainly to improve the population health status. These inputs particularly human resources, physical resources such as facilities and equipment, and knowledge are factors that would enable health system to perform to its potential. Thus, a well-thought strategy for input generation is very critical. (The World Health Report Health Systems Improving Performance, 2000). Inputs in term of adequate number of health care facilities and resources are also requisite to ensure universal accessibility to health care. In Malaysia, despite the highly subsidized public health care provision and additional service coverage offered by the private practices, issues like long queues, drug rationing or poor transportation system can be potential contributors to inequity in service delivery (Ministry of Health, 2015).

Table 6: Human resources for health and primary care facility distribution, 2015 or the nearest year

Year Value Target AAR (%)

Public Private Public Private

6.1. Number of Endocrinologist 2015 54 22 - ↑9.1 ↑4.4

6.2. Total endocrinologist per 100 000 population

2015 0.24 - -

6.3. Number of Family Medicine Specialist (FMS)

2015 281 - ↑8.6

6.4. Number of health clinic (KK) 2015 959 - ↑2.9

6.5. KK to population ratio 2015 1: 32 519 -

-

6.6. Number of KK with FMS 2015 242 - ↑6.2

6.7. Total primary care facility per 10 000 population

2012 2.2 - -

FMS refers to family medicine specialist in Klinik Kesihatan. (Data until July 2015) Primary care facility includes both health clinics and private General Practitioners AAR is calculated for period 2009-2015 Data source: National Health Establishment & Workforce Statistics (NHEWS) Survey Bahagian Pembangunan Kesihatan Keluarga (BPKK) Ministry of Health Malaysian Endocrine and Metabolic Society (MEMS)


FIGURE 7: STATE VARIATION OF PRIMARY CARE FACILITY DENSITY, 2012

Data source: National Medical Care Statistics (Primary Care) 2014

Table 7: State variation of number and density (per 100 000 population) of human resources for health, 2015.

Endocrinologist

Density Number of FMS

2 2015

Number of KK

2015

Number of KK with FMS

2015

Public Private WP Kuala Lumpur 14 8 1.24 19 16 14

Negeri Sembilan 2 0 0.18 16 47 15 Selangor 9 9 0.29 40 74 36

Pulau Pinang 5 2 0.41 15 30 15 Melaka 2 1 0.34 11 29 10

Perak 3 1 0.16 21 85 20 Johor 3 0 0.08 21 94 18

Pahang 2 0 0.12 19 84 18 Kedah 1 0 0.05 25 58 23 Perlis 0 0 0.00 4 9 4

Terengganu 1 0 0.09 21 46 19 Sarawak 3 1 0.15 27 204 17 Kelantan 4 0 0.23 20 80 18

WP Putrajayaa

4 0 4.82 - - - WP Labuan - - - 1 1 1

Sabah & WP Labuan 1 0 0.03 21 102 14 2FMS refers to family medicine specialist in Klinik Kesihatan. (Data until July 2015)

aPutrajaya data for KK and FMS are incorporated into Kuala Lumpur

WP Labuan data for endocrinologist is incorporated into Sabah Data source: National Health Establishment & Workforce Statistics (NHEWS) Survey Bahagian Pembangunan Kesihatan Keluarga (BPKK) Ministry of Health

4.1

2.8 2.6 2.6 2.5 2.4 2.3 2.2

1.8 1.8 1.8 1.6 1.6 1.6

1.1

Malaysia 2.2

per 10,000 population

Klinik Kesihatan Private clinics Average


What does this mean for Malaysia? 1. There is an increasing number of Endocrinologist and FMS at an annual rate of 4.8% and 8.6%

respectively. However lack of utilization data here (e.g. patient turnover rate) limit the interpretation of these indicators in term of service accessibility.

2. Private General Practitioners (GP) provided the widest coverage of primary care services in Malaysia. This phenomenon may put affordability as the key driver to accessibility.

3. The increasing disease prevalence will create demand for services. Efficient services therefore will rely on optimal allocation of resources especially in public sectors.

PROCESS OF CARE

Model of good care (MOGC) for diabetes in primary care setting involves monitoring of glycaemic control and annual screening for any development of diabetic chronic complications (Ministry of Health, 2008). MOGC incorporates selected clinical examinations made routine for all patients with diabetes during their follow up appointments at the health clinics. For example, a standard qualitative urine dipstick test for proteinuria or urine test for microalbuminuria will be performed in all diabetic patients annually to monitor progression of nephropathy. Other routine basic tests include electrocardiogram (ECG), foot examination, blood test for HbA1c and funduscopy. To help with monitoring and auditing, all diabetic patients in Malaysia are registered with the National Diabetes Registry (NDR). NDR is a database that records clinical data of patients with diabetes in Malaysia. The main data comprises of demographic as well as conservative number of diabetes-related variables. Data with more comprehensive variables that cover follow up status, medication list, lab results and processes of care are collected separately through audit surveys. The audits are done only on sampled medical records and involve examining the type of test and screening scheduled for patients within previous 1 year from the audit date. Since audit samples are generated randomly every year, individual compliance to annual screening or follow up cannot be determined. Data source: National Diabetes Registry Report 2009-2012

37% 38% 39% 44%

2009 2010 2011 2012

Funduscopy examination

72% 77% 70% 73%

2009 2010 2011 2012

Foot examination

45% 47% 51% 57%

2009 2010 2011 2012

Urine test for microalbumin

68% 63%

71% 78%

2009 2010 2011 2012

HbA1c test

FIGURE 8: YEARLY TREND OF DIABETES PROCESS OF CARE, 2009-2012


Table 8: Percentage of patients with diabetes who received standard care, 2012.

Period

Rate (%)

AAR (%)

Standard Comparison

(%)

8.1. % Patients who had funduscopy done within the last 1 year

2012 44.0 ↑6.4 - The US

4

62.8

8.2. % Patients who had foot examination done within the last 1 year

2012 73.0 ↑0.3 - The UK

5

87.0

8.3. % Patients who had urine checked for micro-albumin within the last 1 year*

2012 56.7 ↑7.7 - The UK

5

77.9

8.4. % Patients who had HbA1c test done at least once within the last 1 year

2012 78.0 ↑4.7 - The UK

5

91.3

*Does not include qualitative test for proteinuria †AAR is calculated for period 2009-2012 Data source: National Diabetes Registry Report 2009-2012

What does this mean for Malaysia? 1. The percentage of diabetic patients who received standard follow-up care in health clinics is

increasing since 2009. The overall achievements however, were still lower compared with the UK and US.

2. The positive trend in achievements reflects our on-going efforts to ensure that the best clinical practice and quality of care have applied.

3. The proportion of diabetics who received the standard follow-up care ranges from 40% to 80%. Few circumstances should be taken into consideration when interpreting the proportions:

a. Date for clinical examinations for some patients was given later than the NDR audit period.

b. In addition, some of the patients may be recent defaulters or may have their follow-up clinic records not updated due to having test like funduscopy done in other health clinics.

4 Centers for Disease Control and Prevention. Diabetes Report Card 2012. Atlanta, GA:

Centers for Disease Control and Prevention, US Department of Health and Human Services; 2012. 5 Health and Social Care Information Centre, National Diabetes Audit 2012-2013.

Report 1: Care Processes and Treatment Targets. UK.


HEALTH OUTCOME

ADMISSION RATE

The indicators related to hospital admission for diabetes used in this report include admission rate

for uncontrolled DM without complications (ICD-10 codes: E10.9, E11.9, E13.9, E14.9) and DM with

short or long term complications (OECD, Definitions for Health Quality Indicators).

The short term complications refer to complications including coma, hyperosmolarity and

ketoacidosis, caused by relative shortage of insulin in the body (ICD-10 codes: E10.0, E10.1, E11.0,

E11.1, E13.0, E13.1, E14.0, E14.1) (OECD, Definitions for Health Quality Indicators). These

complications can happen to any person with diabetes especially during an acute stress. Long term

complications on the other hand, includes renal, eye or circulatory complications (ICD-10 codes:

E10.2-10.8, E11.2-11.8, E13.2-13.8, E14.2-14.8) (OECD, 2012). These complications are prevalent

among diabetes sufferers who have had diabetes for a long period of time.

Generally, a high admission rate can mean a true need for services. However, it can also reflect a low

threshold for admission, or a lack of accessible primary care services hence the poor glycaemic

control that leads to complications that needed admission. Admission rate indicators in this report

are meant to describe the outcome of quality of care. When quality of care has applied, we would

expect low or reducing rate of admission. The rate is calculated as a ratio between the number of

hospital discharges (ICD-10 coded) and the number of population aged 15 and above.

Data source: Sistem Maklumat Rawatan Perubatan (SMRP)

86 82

54 55 78

58

2010 2011 2012 2013 2014 2015

per

10

0 0

00

Uncontrolled DM without complications

Public Private

13 12 8

11

18 19

2010 2011 2012 2013 2014 2015

per

10

0 0

00

DM with short term complications

Public Private

72 76

41 49 67 69

2010 2011 2012 2013 2014 2015

per

10

0 0

00

DM with long term complications

Public Private

FIGURE 9: HOSPITAL ADMISSION RATE FOR UNCONTROLLED DM WITH OR WITHOUT COMPLICATIONS PER 100 000 POPULATION, 2010-2014


Table 9: Hospital admission rate (per 100 000 population) for uncontrolled DM with or without complications, 2014.

Period

Rate (Per 100 000)

AAR† (%)

Target Comparison

(Per 100 000)

9.1. Uncontrolled DM without complications

2015 58 ↓7.6% EU-15

6

53.8

9.2. DM with short term complications

2015 19 ↑7.7% - Australia

7

27.4

9.3. DM with long term complications

2015 69 ↓1.0% - Australia

7

97.3

9.4. DM hospital admission (total)

2015 145 ↓3.2% - OECD-31

8 149.8

† AAR is calculated for period 2010-2015 6

EU-15: Data for 2011: Age-sex standardized to the 2010 OECD population 15+ 8

OECD-31: Data for 2013: Age-sex standardized to the 2010 OECD population 15+ Data source: Sistem Maklumat Rawatan Perubatan (SMRP) Table 10: State variation of hospital admission rate for diabetes (per 100 000 population), 2015.

Uncontrolled DM

without complications Uncontrolled DM with short

term complications

Uncontrolled DM with long term

complications

WP Putrajaya 125 ↑ 71% 86 ↑ 199% 182 ↑ 69% Melaka 115 ↓ 5.8% 40 ↑ 13% 75 ↓ 1.8%

Terengganu 66 ↑ 6.3% 22 ↑ 11% 83 ↓ 15%

Johor 76 ↓ 12% 17 ↑ 7.2% 87 ↑ 0.9%

Kedah 83 ↓ 5% 25 ↑ 2.3% 115 ↓ 5.5%

Negeri Sembilan 60 ↓ 13% 19 ↑ 1.5% 114 ↓ 3.8%

Perlis 70 ↓ 24% 24 ↑ 14% 97 ↑ 1.3%

Perak 74 ↓ 12% 28 ↓ 2.2% 94 ↑ 6.6%

Pahang 73 ↓ 9.3% 18 ↑ 16% 72 ↓ 3.0%

Kelantan 48 ↓ 7.1% 20 ↑ 4.3% 48 ↓ 7.9%

Pulau Pinang 73 ↓ 3.2% 18 ↑ 8.3% 116 ↑ 4.9%

Sarawak 35 ↓ 5.4% 19 ↑ 15% 29 ↓ 2.8%

Selangor 33 ↓ 0.5% 13 ↑ 17% 55 ↑ 0.2%

WP Kuala Lumpur 60 ↓ 8.5% 22 ↑ 13% 69 ↑ 3.6%

Sabah & WP Labuan 42 ↓ 5.3% 9 ↑ 8.3% 20 ↑ 11% * AAR is calculated for period 2013-2015 for WP Putrajaya Note: Data ascertainment for year 2012 was affected by transition of database system from standalone programme to web-based client hence the notch observed in rate trending for that year. Data source: Sistem Maklumat Rawatan Perubatan (SMRP)

6 OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en. 7 Australian Institute of Health and Welfare 2014. OECD Health Care Quality Indicators for Australia

2011–12. Cat. no. PHE 174. Canberra: AIHW. 8 OECD Health Statistics 2014


What does this mean for Malaysia? 1. Overall diabetes related admission is decreasing for period 2010-2014 at an average annual

rate of 10% in public sectors. However, admission of DM with short term complications is increasing.

2. Short term complications essentially reflect poor diabetes control. Thus, an increasing trend is a concern.

CHRONIC COMPLICATIONS

Good glycaemic control can reduce micro-vascular complications (Stratton, Irene M et al., 2000).

Achieving glycaemic control essentially involves active participation from both the patients and their

care providers. Unfortunately, the degree of diabetes self-management is still poor in Malaysia (M.Y.

Tan & J. Magarey, 2008). As a result, development of complications from poor disease control

remains an issue as evidence by findings from a series of DiabCare surveys, a hospital-based cross-

sectional study on patients with type 2 diabetes (Zanariah Hussein, Sri Wahyu Taher, Harvinder Kaur

Gilcharan Singh, & Winnie Chee Siew Swee, 2015).

FIGURE 10: RATE OF DIABETIC COMPLICATIONS, 2011 VS 2012


9.6

8.4

6.1

1.3 1.4 0.7

8.9

7.9

6.0

1.4 1.3 0.7

%

2011

2012


Table 11: Percentage of patients with documented complications, 2012.

Period

Rate* (%)

AAR†

(%) Comparison

9,10,11,12

(%)

11.1. Prevalence of Retinopathy 2012 7.9 ↓6.5 The US, Australia,

Europe, Asia 35.36

11.2. Prevalence of Nephropathy 2012 8.9 ↓6.6 The US

34.5

11.3. Prevalence of MI 2012 6.0 ↓1.3 The UK

1.47

11.4. Prevalence of Cerebrovascular disease

2012 1.4 ↑4.7 The UK

1.79

11.5. Prevalence of DFU 2012 1.4 ↓3.0 Saudi Arabia

3.3

11.6. Prevalence of Amputation 2012 0.7 ↓0.6 The UK

<0.3

*Recalculated from NDR report 2009-2012 to exclude missing data. Each patient may have one or more complications †AAR calculated for period 2011-2012 Data source: National Diabetes Registry Report 2009-2012


1. Rate of retinopathy, nephropathy, myocardial infarction, diabetes foot ulcer and lower limb amputation has decreased by about 1% to 7%. However, cerebrovascular complication has increased by 5% since 2011.

2. The reported rate of retinopathy, nephropathy, stroke and diabetes foot ulcer (DFU) in Malaysia are generally lower than the comparator countries due to the different setting and methods used for recruitment of study population.

9 Yau, Joanne W.Y. et al. “Global Prevalence and Major Risk Factors of Diabetic Retinopathy.” Diabetes Care 35.3 (2012):

556–564. PMC. Web. 8 June 2016. 10 De Boer, Ian H. et al. “Temporal Trends in the Prevalence of Diabetic Kidney Disease in the United States.” JAMA : the journal of the American Medical Association 305.24 (2011): 2532–2539. PMC. Web. 8 June 2016. 11 Health and Social Care Information Centre, National Diabetes Audit 2011-2012.

Report 2: Complications and Mortality. UK. 12 Al-Rubeaan, Khalid et al. “Diabetic Foot Complications and Their Risk Factors from a Large Retrospective Cohort Study.”

Ed. Fabio Santanelli, di Pompeo d’Illasi. PLoS ONE 10.5 (2015): e0124446. PMC. Web. 8 June 2016.


CLINICAL TARGETS

Clinical Practice Guideline Malaysia 2009 (4th edition) for Type 2 Diabetes Mellitus management

outlined the desirable targets for patients with diabetes such as the following:

1. HbA1c ≤ 6.5% 2. Weight loss 5-10% of initial weight 3. BP ≤ 130/80 mmHg 4. LDL ≤ 2.6 mmol/L

However, depending on the person’s co-morbidity, some of the targets may be inapplicable due to

higher risk of causing harm. Therefore, the current CPG in 2015 (5th edition) advocated achievement

of individual targets that are tailored to individual risks (Ministry of Health, 2015).


19% 25% 23% 24%

2009 2010 2011 2012

HbA1c < 6.5%

41% 45% 42% 41%

2009 2010 2011 2012

BP ≤ 130/80 mmHg

17% 16% 16% 17%

2009 2010 2011 2012

BMI ≥ 23 kg/m2

31% 34% 35% 38%

2009 2010 2011 2012

LDL ≤ 2.6 mmol/L

FIGURE 11: YEARLY TREND OF DIABETES CLINICAL TARGET ACHIEVEMENT, 2009-2012


Table 7: Clinical target achievement, 2012

Period

Achievement (%)

Target AAR†

(%) Comparison

(%)

12.1. % Patient who achieved BP target ≤ 130/80mm/Hg

2012 40.9% - ↓ 0.2 The UK

13

66.7

12.2. % Patient who achieved HbA1c target < 6.5%

2012 23.8% > 30%* ↑ 7.1 The UK

13

26.2

12.3. % Patient who had BMI < 23kg/m

2

2012 16.6% - ↓ 1.2 Hong Kong

14

28.5

12.4. % Patient who achieved LDL target ≤ 2.6mmol/L

2012 37.8% - ↑ 7.3 Hong Kong

14

65.6

* Target from Malaysian Clinical Practice Guideline “Management of Type 2 Diabetes Mellitus (5th Edition)” † AAR is calculated for period 2009-2012



1. There is a positive trend in percentage of patients achieving clinical target for HbA1c and LDL, although the overall achievement is still lower than the countries like the UK and Hong Kong.

2. The proportion of diabetic patients with normal BMI (< 23kg/m2) is only about 17% of the total diabetic patients.

3. Additionally, proportion of diabetic patients with blood pressure that fall within the accepted range (< 130/80mm/Hg) is about 41%.

13 Health and Social Care Information Centre, National Diabetes Audit 2012-2013.

Report 1: Care Processes and Treatment Targets. UK. 14 Fung, C. S. C., Wan, E. Y. F., Jiao, F., & Lam, C. L. K. (2015). Five-year change of clinical and complications profile of

diabetic patients under primary care: a population-based longitudinal study on 127,977 diabetic patients.

Diabetology & Metabolic Syndrome, 7, 79. http://doi.org/10.1186/s13098-015-0072-x


CONCLUSION

Areas where Malaysia is doing well:

Malaysia is seeing positive growth in human resources and facility for primary care services

More Malaysians are adopting active lifestyle as evident by recent NHMS survey

NDR is a national database that covers almost all public primary care facilities for diabetes

cases. Patient with diabetes receiving treatment in health clinics are all registered into the

database for monitoring purposes

Diabetes related admission to public hospital is reducing.

Processes of care related to diabetes management at the clinics are improving.

Areas for concern:

Diabetes prevalence is increasing steadily and about half of the cases were undiagnosed.

Pre-diabetes state is under-diagnosed. A complete assessment of pre-diabetic state must

also include measurement of impaired glucose tolerance (IGT); a pre-diabetes state that is

more prevalent than IFG. Our national survey couldn’t include full assessment of pre-

diabetes state to include both IFG and IGT due to practicality issue.

Malaysian prevalence of overweight, obesity and diabetes is the highest in the Asia Pacific

region

Half of the population with type 2 diabetes are those 54 years old or younger

Only less than half of population with diabetes had achieved the preferred clinical targets.

(lipid, blood pressure and weight targets are still not achieved)

Admission for uncontrolled diabetes with short term complications is increasing indicative of

poor diabetes control. Discharge data in SMRP potentially include misdiagnosis or miscoding

due to human and technical error. Latest error rate study conducted as part of Quality

Assurance Programme in 2013 reveal a moderate error rate of 19%.

Less than half of diabetic patients had received annual funduscopy test.


RECOMMENDATIONS

Unless management of diabetes is considered in a broader context beyond what transpired in the

doctor’s consultation room, the burden of disease will become more evident. The community and

the patient need to be empowered to manage diabetes especially in the aspect of primary and

secondary prevention respectively. To help achieve this goal, collaboration from key players with the

authority to provide built environment for health that facilitate healthy lifestyle should be more

encouraged.

Extensive screening programme with more resources put into screening possibly through KOSPEN may be worth considering as cases of undiagnosed risk factor are increasing and can pose an imminent threat to public health status. Health promotion strategy and primary prevention program must take priority in national health policy specifically to ameliorate this issue. We need baseline data on pre-diabetic state prevalence among Malaysians. Assessment of impaired glucose tolerance (IGT) could be incorporated as part of the KOSPEN program instead of a large scale population survey like NHMS. Individual patients need to focus on self-care behaviour and us as health provider can facilitate.

Systematic monitoring of self-care variables along with medication compliance during clinic

appointment could improve outcomes.

Primary care service should establish a multidisciplinary team that deal with and focus on NCD. Hence, one doctor one family concept should be fully implemented. Strategy for allocation of human resources must take into account the number of available facility and the disease burden at each locality. We could outsource fund for diabetes educator program from NGO like NADI. Future reports ought to include data on diabetes educator and the medical officers, as they are key human resources for diabetes care. Malaysia Diabetes Educators Society (MDES) may be of help with regard to diabetic educator database. Future reporting must also include soft outcomes like quality of life and hard outcomes including the mortality associated with DM. Analysis must narrow down to each locality so as to identify niche issues of importance. Other suggestions for report improvement include:

1. To include indicator on compliance to best practice (e.g. right treatment indication) 2. Number of equipment available in each clinic 3. To emphasis more on rate of change as a measure of improvements 4. Use of indicators that reflect performance in developing countries 5. To include indicator on rate of diabetic foot amputation by locality

Primary health care facilities including that of private sectors to fully utilise NDR and to update

patients progress on the registry to ensure completeness of data. NDR may also be expanded to

include data entry for other commonest chronic diseases like hypertension.


APPENDIX


Indicator: Number of endocrinologist (Table 6 & Table 7)

Data table 1: Number of Endocrinologist

State 2009 2010 2011 2013 2015

Johor 0 0 0 1 0

Kedah 0 0 0 0 0

Kelantan 0 0 0 0 0

WP Kuala Lumpur 3 5 5 8 8

Melaka 0 0 0 1 1

Negeri Sembilan 0 0 0 1 0 Pahang 0 0 0 0 0

Perak 1 1 1 1 1

Perlis 0 0 0 0 0

Pulau Pinang 3 4 3 2 2

Sarawak 1 1 0 2 1

Terengganu 0 0 0 0 0

Sabah &WP Labuan 0 0 0 1 0

Selangor & WP Putrajaya 9 8 9 10 9

WP Putrajaya - 0 0 0 0

Selangor - 8 9 10 9

Malaysia 17 19 18 23 22

Johor 1 0 0 0 3

Kedah 0 0 0 1 1

Kelantan 3 3 4 2 4

WP Kuala Lumpur 13 13 13 12 14

Melaka 1 1 1 1 2

Negeri Sembilan 1 1 1 0 2

Pahang 0 0 0 0 2 Perak 1 1 1 2 3

Perlis 0 0 0 0 0

Pulau Pinang 3 2 2 4 5

Sarawak 1 1 1 0 3

Terengganu 0 0 0 0 1

Sabah & WP Labuan 1 1 1 0 1

Selangor & WP Putrajaya 7 10 10 9 13

WP Putrajaya - 8 8 5 4

Selangor - 2 2 4 9

Malaysia 32 33 34 36 54 Data source: NHEWS (hospital) 2008-2009 (page 102) NHEWS (hospital) 2012-2013 (page 62) MEMS for data 2015

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Indicator: Density of endocrinologist (Table 6 & Table 7)

Data table 2: Density of Endocrinologist

State 2009 2010 2011 2013 2015

Johor 0 0 0 0 0.08

Kedah 0 0 0 0.05 0.05

Kelantan 0.02 0.19 0.25 0.24 0.23

WP Kuala Lumpur 0.09 1.07 1.06 1.15 1.24

Melaka 0.01 0.12 0.12 0.12 0.34

Negeri Sembilan 0.01 0.1 0.1 0.09 0.18

Pahang 0 0 0 0.06 0.12

Perak 0.01 0.09 0.08 0.12 0.16

Perlis 0 0 0 0 0

Pulau Pinang 0.04 0.38 0.31 0.37 0.41

Sarawak 0.01 0.08 0.04 0.08 0.15

Terengganu 0 0 0 0 0.09

Sabah & WP Labuan 0 0.03 0.03 0.03 0.03

Selangor 0.03 0.18 0.2 0.24 0.29

WP Putrajaya - 11.05 10.47 6.06 4.82

Malaysia 0.02 0.18 0.18 0.2 0.24

2009 per 10 000 population 2010, 2011, 2013& 2015 per 100 000 population Data source: NHEWS (hospital) 2008-2009 (page 102) NHEWS (hospital) 2012-2013 (page 62)


Indicatore: Number of FMS (Table 6)

Data table 3: Number of FMS

State 2008 2009 2010 2011 2012 2013 2014 2015

Perlis 2 3 3 3 2 4 4 4

Kedah 10 17 16 20 19 19 23 25

Pulau Pinang 4 8 10 10 11 11 13 15

Perak 7 12 12 13 17 20 22 21

Selangor 17 26 27 30 30 34 40 40

WP Putrajaya & WP K.L 5 14 14 15 12 15 19 19

Negeri Sembilan 9 11 12 12 14 15 17 16

Melaka 5 6 6 9 9 9 9 11

Johor 12 18 19 20 17 17 18 21

Pahang 9 15 14 16 17 18 17 19

Kelantan 11 14 15 15 17 18 19 20

Terengganu 10 16 17 19 17 18 21 21

Sarawak 2 8 8 10 19 13 13 27

Sabah 5 3 4 4 16 16 14 21

WP Labuan 0 0 0 0 1 1 1 1

Malaysia 108 171 177 196 218 228 250 281

Data source: Bahagian Pembangunan Kesihatan Keluarga (BPKK) updated until July 2015

Indicator: Number of KK (Table 6)

Data table 4: Number of KK

State 2008 2009 2010 2011 2012 2013 2014 2015

Perlis

9 9 9 9 9 9 9

Kedah

51 51 56 56 57 58 58

Pulau Pinang

26 27 29 29 30 30 30

Perak

73 73 79 83 83 84 85

Selangor

55 55 61 73 73 74 74

WP Putrajaya & WP K.L

14 15 17 17 17 16 16

Negeri Sembilan

38 41 46 46 46 46 47

Melaka

25 26 29 29 29 29 29

Johor

88 88 94 94 94 94 94

Pahang

64 64 76 79 82 84 84

Kelantan

53 53 59 69 69 80 80

Terengganu

39 39 45 45 46 46 46

Sarawak

194 194 197 196 198 203 204

Sabah

77 77 81 93 100 101 102

WP Labuan

1 1 1 1 1 1 1

Malaysia

807 813 879 919 934 955 959



Indicator: No of KK with FMS (Table 6)

Data table 5: No of KK with FMS

State 2008 2009 2010 2011 2012 2013 2014 2015

Perlis 2 3 3 3 2 4 4 4

Kedah 10 17 16 20 19 18 23 23

Pulau Pinang 4 8 10 10 11 11 13 15

Perak 7 12 12 13 17 20 21 20

Selangor 17 26 26 29 30 33 34 36

WP Putrajaya & WP K.L 4 12 11 12 9 13 15 14

Negeri Sembilan 9 11 12 12 14 15 16 15

Melaka 5 6 6 9 9 8 9 10

Johor 12 18 19 20 17 17 18 18

Pahang 9 15 14 16 17 18 16 18

Kelantan 11 14 15 15 17 18 20 18

Terengganu 10 16 17 19 17 18 20 19

Sarawak 2 8 8 10 19 13 13 17

Sabah 5 3 4 4 16 16 13 14

WP Labuan 0 0 0 0 1 1 1 1

Malaysia 107 169 173 192 215 223 236 242


Indicator: Number of Malaysian Primary Care Clinics (Figure 7)

Data table 6: Number of Malaysian Primary Care Clinics per 10,000 Population in 2012

State Number of clinics Population Density

Malaysia 6557 29510000 2.2

Johor 802 3450400 2.3

Kedah 354 2001100 1.8

Kelantan 256 1651000 1.6

Melaka 215 843200 2.5

Negeri Sembilan 279 1057700 2.6

Pahang 280 1547100 1.8

Perak 593 2427000 2.4

Perlis 39 239400 1.6

Pulau Pinang 428 1623200 2.6

Sabah & WP Labuan 403 3523200 1.1

Sarawak 421 2569700 1.6

Selangor & WP Putrajaya 1596 5780700 2.8

Terengganu 193 1094100 1.8

WP Kuala Lumpur 698 1702100 4.1

Data source: National Medical Care Statistics (Primary Care) 2014 (page 13 & 34)


Reference: Involved in calculation for admission rate

Data table 7: Population by states aged 15 years old and above

State 2010 2011 2012 2013 2014 2015

Malaysia 20766700 21271000 21733400 22430000 22935000 23410300

Johor 2448900 2500900 2546500 2575100 22935000 2717900

Kedah 1378300 1407900 1442300 1487100 2665100 1553500

Kelantan 1049000 1081000 1113700 1143900 1516400 1210300

WP Kuala Lumpur 1306000 1325600 1337200 1356100 1180000 1406200

Labuan 61700 63100 64100 65100 1368000 68500

Melaka 605500 618700 632900 645800 67200 678100

Negeri Sembilan 751400 771500 784300 801600 661000 822900

Pahang 1051800 1080700 1105800 1131900 811600 1176800

Perak 1733200 1768700 1802600 1838000 1157800 1888100

Perlis 173600 176100 178100 181100 1863300 186700

Pulau Pinang 1213400 1243100 1267100 1308200 183700 1350100

WP Putrajaya 50200 51800 51400 51700 1325500 52100

Sabah 2341000 2426600 2511500 2677100 51300 2784500

Sarawak 1768100 1814800 1859100 1936900 2735000 2010500

Selangor 4129800 4213900 4295000 4471700 4598200 4703900

Terengganu 704400 726700 741500 758800 783700 800500

Sabah & WP Labuan 2402700 2489700 2575600 2742200 2802200 2853000

Data source: Department of Statistics Malaysia


Indicator: Admission rate for uncontrolled DM without complications (Table 9 & Table 10)

Data table 8: Number of admission for uncontrolled DM without complications (aged 15 years old and above)

State 2010 2011 2012 2013 2014 2015

Johor 3469 3255 2224 2149 2419 2069

Kedah 1482 1496 1117 813 1647 1290

Kelantan 726 707 681 708 869 581

WP Kuala Lumpur 1222 1239 106 497 1236 844

Labuan 0 0 135 52 0 0

Melaka 945 875 541 714 825 783

Negeri Sembilan 913 789 724 511 974 496

Pahang 1260 930 775 889 1121 864

Perak 2373 2808 1468 1609 1767 1404

Perlis 474 351 293 214 177 131

Pulau Pinang 1046 870 227 586 1435 988

WP Putrajaya 0 0 0 22 79 65

Sabah 1332 1425 1012 1185 0 0

Sarawak 806 805 601 847 763 695

Selangor 1392 1523 1441 1203 2808 1543

Terengganu 345 331 374 297 391 532

Sabah & WP Labuan 1332 1425 1147 1237 1390 1203

Malaysia 17785 17404 11719 12296 17901 13488


Data table 9: Admission rate for uncontrolled DM without complications

State 2010 2011 2012 2013 2014 2015

Johor 142 130 87 83 91 76

Kedah 108 106 77 55 109 83

Kelantan 69 65 61 62 74 48

WP Kuala Lumpur 94 93 8 37 90 60

Labuan 0 0 211 80 0 0

Melaka 156 141 85 111 125 115

Negeri Sembilan 122 102 92 64 120 60

Pahang 120 86 70 79 97 73

Perak 137 159 81 88 95 74

Perlis 273 199 165 118 96 70

Pulau Pinang 86 70 18 45 108 73

WP Putrajaya 0 0 0 43 154 125

Sabah 57 59 40 44 0 0

Sarawak 46 44 32 44 39 35

Selangor 34 36 34 27 61 33

Terengganu 49 46 50 39 50 66

Sabah & WP Labuan 55 57 45 45 50 42

Malaysia 86 82 54 55 78 58


Indicator: Admission rate for DM with short term complications (Table 9 & Table 10)

Data table 10: Discharge number for DM with short term complications (aged 15 years old and above)

State 2010 2011 2012 2013 2014 2015

Johor 297 327 256 412 752 467

Kedah 310 213 216 130 322 391

Kelantan 173 178 120 180 192 246

WP Kuala Lumpur 155 180 19 81 303 312

WP Labuan 0 0 4 5 0

Melaka 133 217 99 184 171 273

Negeri Sembilan 133 124 95 119 175 157

Pahang 91 136 142 207 211 210

Perak 545 292 137 247 421 530

Perlis 22 11 22 38 38 45

Pulau Pinang 145 166 25 177 233 240


Sabah 139 160 121 154 0

Sarawak 169 150 148 202 260 378

Selangor 251 310 243 267 593 623

Terengganu 93 180 145 157 193 177

Sabah & WP Labuan 139 160 125 159 186 246

Malaysia 2656 2644 1792 2565 4079 4340


Data table 11: Admission rate for DM with short term complications

State 2010 2011 2012 2013 2014 2015

Johor 12 13 10 16 28 17

Kedah 22 15 15 9 21 25

Kelantan 16 16 11 16 16 20


WP Labuan 0 0 6 8 0 0

Melaka 22 35 16 28 26 40

Negeri Sembilan 18 16 12 15 22 19

Pahang 9 13 13 18 18 18

Perak 31 17 8 13 23 28

Perlis 13 6 12 21 21 24

Pulau Pinang 12 13 2 14 18 18


Sabah 6 7 5 6 0 0

Sarawak 10 8 8 10 13 19

Selangor 6 7 6 6 13 13

Terengganu 13 25 20 21 25 22

Sabah & WP Labuan 6 6 5 6 7 9

Malaysia 13 12 8 11 18 19


Indicator: Admission rate for DM with long term complications (Table 9 & Table 10)

Data table 12: Discharge number for DM with long term complications (aged 15 years old and above)

State 2010 2011 2012 2013 2014 2015

Johor 2042 2081 1637 1502 2075 2371

Kedah 2094 2008 1228 591 1425 1781

Kelantan 767 713 420 728 618 586

WP Kuala Lumpur 760 872 69 342 688 976

WP Labuan 0 0 17 17 0 0

Melaka 494 489 198 558 708 506

Negeri Sembilan 1039 921 751 730 948 939

Pahang 889 898 606 733 769 852

Perak 1180 1628 795 995 1902 1772

Perlis 159 242 237 320 230 182

Pulau Pinang 1108 1219 329 1199 1493 1564

WP Putrajaya 0 0 0 33 128 95

Sabah 286 312 238 374 0 0

Sarawak 583 475 445 506 609 574

Selangor 2273 2764 1101 1147 2439 2610

Terengganu 1294 1467 928 1139 863 667

Sabah & WP Labuan 286 312 255 391 421 578

Malaysia 14968 16089 8999 10914 15316 16053

Data source: Sistem Maklumat Rawatan Perubatan (SMRP) Data table 13: Admission rate for DM with long term complications

State 2010 2011 2012 2013 2014 2015

Johor 83 83 64 58 78 87

Kedah 152 143 85 40 94 115

Kelantan 73 66 38 64 52 48


WP Labuan 0 0 27 26 0 0

Melaka 82 79 31 86 107 75

Negeri Sembilan 138 119 96 91 117 114

Pahang 85 83 55 65 66 72

Perak 68 92 44 54 102 94

Perlis 92 137 133 177 125 97

Pulau Pinang 91 98 26 92 113 116

WP Putrajaya 0 0 0 64 250 182

Sabah 12 13 9 14 0 0

Sarawak 33 26 24 26 31 29

Selangor 55 66 26 26 53 55

Terengganu 184 202 125 150 110 83

Sabah & WP Labuan 12 13 10 14 15 20

Malaysia 72 76 41 49 67 69


Indicator: Admission rate for DM (total) (Table 9 & Table 10)

Data table 14: Discharge number for DM (total) (aged 15 years old and above)

State 2010 2011 2012 2013 2014 2015

Johor 5808 5663 4117 4063 5246 4907

Kedah 3886 3717 2561 1534 3394 3462

Kelantan 1666 1598 1221 1616 1679 1413

WP Kuala Lumpur 2137 2291 194 920 2227 2132

WP Labuan 0 0 156 74 0 0

Melaka 1572 1581 838 1456 1704 1562

Negeri Sembilan 2085 1834 1570 1360 2097 1592

Pahang 2240 1964 1523 1829 2101 1926

Perak 4098 4728 2400 2851 4090 3706

Perlis 655 604 552 572 445 358

Pulau Pinang 2299 2255 581 1962 3161 2792

WP Putrajaya 0 0 0 60 236 205

Sabah 1757 1897 1371 1713 0 0

Sarawak 1558 1430 1194 1555 1632 1647

Selangor 3916 4597 2785 2617 5840 4776

Terengganu 1732 1978 1447 1593 1447 1376

Sabah & Labuan 1757 1897 1527 1787 1997 2027

Malaysia 35409 36137 22510 25775 37296 33881


Data table 15: Admission rate for DM (total)

State 2010 2011 2012 2013 2014 2015

Johor 237 226 162 158 197 181

Kedah 282 264 178 103 224 223

Kelantan 159 148 110 141 142 117

WP Kuala Lumpur 164 173 15 68 163 152

WP Labuan 0 0 243 114 0 0

Melaka 260 256 132 225 258 230

Negeri Sembilan 277 238 200 170 258 193

Pahang 213 182 138 162 181 164

Perak 236 267 133 155 220 196

Perlis 377 343 310 316 242 192

Pulau Pinang 189 181 46 150 238 207

WP Putrajaya 0 0 0 116 460 393

Sabah 75 78 55 64 0 0

Sarawak 88 79 64 80 83 82

Selangor 95 109 65 59 127 102

Terengganu 246 272 195 210 185 172

Sabah & WP Labuan 73 76 59 65 71 71

Malaysia 171 170 104 115 163 145


Indicator: % Patients who had funduscopy done within the last 1 year (Table 8) % Patients who had foot examination done within the last 1 year % Patients who had urine checked for micro-albumin within the last 1 year % Patients who had HbA1c test done at least once within the last 1 year

Data table 16: Proportion of patients receiving clinical investigations [AuditDataset]

Investigation 2009 % 2010 % 2011 % 2012 %

BP 79202 98.8 63138 81.8 66940 93.4 116265 93.7

HbA1c 54431 67.9 48765 63.2 51018 71.2 96694 78

FBG 48019 59.9 46217 59.9 44565 62.2 71386 57.6

RBG 50744 63.3 43281 56.1 39169 54.7 74801 60.3

2HPP 9719 12.1 6150 8 4200 5.9 5862 4.7

Creatinine 65875 82.2 53067 68.8 51940 72.5 96248 77.6

Total cholesterol 66203 82.6 52724 68.3 53091 74.1 97362 78.5

LDL 51421 64.2 34220 44.3 35950 50.2 73332 59.1

HDL 52306 65.3 34461 44.7 36508 51 73772 59.5

TG 65648 81.9 52360 67.8 52506 73.3 97045 78.3

Urine protein 45794 57.2 44802 58.1 41830 58.4 80224 64.7

Urine microalbumin 36300 45.3 35859 46.5 36842 51.4 70273 56.7

Foot exams 58001 72.4 59643 77.3 50115 69.9 90558 73

Fundus 29263 36.5 29642 38.4 27806 38.8 54590 44

ECG 35926 44.8 35975 46.6 35848 50 67068 54.1

Patients audited 80,134

77,179

71,655

124,023

Data source: NDR 2009-2012 (page 17)


Indicator: Rate of Retinopathy (Table 11) Rate of Nephropathy Rate of MI Rate of Cerebrovascular disease Rate of DFU Rate of Amputation

Data table 17: Complications and co-morbidities in 2011 and 2012 [Audit Dataset]

Co-morbidities

2011 2012

n % n %

Nephropathy

Present 5429 7.6 9707 7.8

Absent 51350 71.7 99016 79.8

Unknown 14850 20.7 15256 12.3

Retinopathy

Present 4627 6.5 8255 6.7

Absent 50455 70.4 96872 78.1

Unknown 16547 23.1 18853 15.2

Ischaemic Heart Disease

Present 3467 4.8 6508 5.3

Absent 53387 74.5 101630 81.9

Unknown 14775 20.6 15842 12.8

Cerebrovascular Disease

Present 788 1.1 1550 1.3

Absent 56966 79.5 106953 86.2

Unknown 13875 19.4 15476 12.5

Diabetic Foot Ulcer

Present 841 1.2 1527 1.2

Absent 58044 81 108726 87.7

Unknown 12744 17.8 13725 11.1

Amputation

Present 387 0.5 721 0.9

Absent 58487 81.6 109652 88.4

Unknown 12755 17.8 13605 11



Indicator: % Patient who achieved BP target ≤ 130/80mm/Hg (Table 12) % Patient who had BMI < 23kg/m2 % Patient who achieved LDL target ≤ 2.6mmol/L

Data table 18: Target achievement based on clinical investigations [Audit Dataset]

2009 2010 2011 2012

Clinical test Treatment

target

% achieved

target

Mean test

result 95% CI

% achieved

target

Mean test

result 95% CI

% achieved

target

Mean test

result 95% CI

% achieved

target

Mean test

result 95% CI

Urine microalbumin

Negative N/A N/A N/A 64.3 N/A N/A 71.1 N/A N/A 71.9 N/A N/A

BP ≤130/80 mmHg

41.2 N/A N/A 45 N/A N/A 42 N/A N/A 40.9 N/A N/A

Total cholesterol

<4.5 mmol/l

24.1 5.3 (5.3-5.3) 25.8 5.3 (5.2-5.3) 26.3 5.2 (5.2-5.2) 28.5 5.2 (5.2-5.2)

LDL ≤2.6

mmol/l 30.6 3.2 (3.2-3.2) 33.6 3.2 (3.2-3.2) 34.5 3.2 (3.1-3.2) 37.8 3.1 (3.1-3.1)

BMI <23

kg/m2

17.2 28 (27.7-28.3) 15.9 30 (28.1-31.9) 16.3 27.4 (27.4-27.5) 16.6 27.4 (27.3-27.4)

Waist circumference

<90 cm (Male)

35.3 93.4 (93.2-93.5) 34.2 94 (93.8-94.1) 35.1 93.6 (93.5-93.8) 33.8 94 (93.9-94.1)

<80 cm (Female)

15.6 90.1 (89.9-90.2) 14.8 90.2 (90.1-90.4) 15.2 90.4 (90.3-90.6) 14.4 90.7 (90.6-90.8)



Indicator: % Patient who achieved HbA1c target < 6.5% (Table 12)

Data table 19: Proportion of patients achieving HbA1c treatment target (HbA1c <6.5%) and mean HbA1c by state [Audit Dataset] (All age group)

2009 2010 2011 2012

% achieved

target

Mean HbA1c

95% CI %

achieved target

Mean HbA1c

95% CI %

achieved target

Mean HbA1c

95% CI %

achieved target

Mean HbA1c

95% CI

Malaysia 19.4 8.3 (8.3-8.3) 24.8 8 (8.0-8.0) 22.6 8.2 (8.2-8.2) 23.8 8.1 (8.1-8.1)

Kelantan 14.7 8.9 (8.8-8.9) N/A N/A

19.9 8.6 (8.5-8.7) 14.9 8.8 (8.7-8.9)

Terengganu 23 8.6 (8.5-8.7) 22.2 8.4 (8.3-8.5) 18.2 8.7 (8.6-8.8) 17.6 8.8 (8.7-8.9)

Pulau Pinang 18.2 8.3 (8.2-8.4) 21.9 8 (8.0-8.1) 22 8.1 (8.0-8.1) 21 8 (7.9-8.0)

Johor 18.7 8.3 (8.3-8.4) 20.6 8.2 (8.2-8.3) 18.5 8.4 (8.4-8.5) 21.9 8.1 (8.1-8.2)

Kedah 15.9 8.6 (8.5-8.7) N/A N/A

25 8.4 (7.1-9.6) 22.4 8.3 (8.2-8.3)

Pahang 18.8 8.6 (8.5-8.7) 25.2 8.2 (8.2-8.3) 20.1 8.4 (8.4-8.5) 22.4 8.3 (8.3-8.4)

Selangor 22.3 8.2 (8.1-8.3) 30.5 7.8 (7.7-7.8) 22.3 8.2 (8.2-8.3) 23 8.3 (8.3-8.4)

Perak 18.3 8.3 (8.2-8.3) 26.1 8 (7.9-8.1) 24.6 8.1 (8.0-8.2) 24.3 8.2 (8.2-8.3)

Negeri Sembilan 18.7 8.2 (8.2-8.3) 22.7 8 (8.0-8.1) 24.2 8.1 (8.1-8.2) 24.4 8 (7.9-8.0)

Melaka 19.5 8.2 (8.1-8.3) 20.7 8.1 (8.0-8.2) 24.5 7.9 (7.9-8.0) 25.2 7.8 (7.8-7.8)

Perlis 29.1 7.8 (7.6-7.9) 27.9 8.1 (8.0-8.3) 27.7 8.1 (7.9-8.2) 29.2 8.1 (8.0-8.2)

WP Kuala Lumpur

19 8.1 (8.1-8.2) 30.4 7.7 (7.6-7.7) 25.3 8 (7.9-8.0) 30.5 7.7 (7.7-7.8)

WP Putrajaya 16.1 7.8 (7.6-8.1) 26.2 7.7 (7.5-7.8) 17.2 8 (7.9-8.2) 31.1 7.9 (7.8-8.1)

Sabah 38.4 7.4 (7.2-7.6) 36.1 7.2 (7.1-7.3) 31.3 7.5 (7.4-7.6) 33.4 7.4 (7.4-7.5)

Sarawak 34.4 7.5 (7.4-7.7) 26.3 7.8 (7.6-8.0) 30 7.7 (7.5-7.9) 39.1 7.4 (7.3-7.6)

WP Labuan N/A N/A N/A 39.4 7.3 (7.1-7.5) 32.1 7.4 (7.0-7.8) 54 6.9 (6.7-7.1)



Data table 20: Prevalenve of overall, known and undiagnosed diabetes (aged 18 years old and above)

Diabetes Mellitus (%)

Overall Known Undiagnosed

2006 2011 2015 2006 2011 2015 2006 2011 2015

Malaysia 11.6 15.2 17.5 7 7.2 8.3 4.5 8 9.2

Johor 11.1 13.4 19.8 7 7.6 8.8 4.1 5.9 11

Kedah 13.6 22.5 25.4 9.3 9.1 9.3 4.3 13.4 16.1

Kelantan 11.7 19.7 18.5 6.8 8 7.1 4.9 11.7 11.3

Melaka 15.2 17.1 16.7 11.4 10.4 8.3 3.9 6.6 8.4

Negeri Sembilan 15.3 22 19.3 8.8 11.5 10.5 6.6 10.5 8.8

Pahang 12.1 16.7 14.8 8.1 8.6 6.3 4 8.1 8.6

Pulau Pinang 14.9 15 18.1 8.9 8.5 9 6 6.4 9.1

Perak 12.6 16.2 19.4 7.6 10.1 11.9 5 6.1 7.4

Perlis 13.5 24.8 20.6 8 8.7 10.9 5.5 16.1 9.7

Selangor 12 16.5 15.5 8.3 6.5 7.8 3.8 10.1 7.7

Terengganu 11.1 11.6 18.6 5.7 7.1 8.7 5.4 4.5 9.9

Sabah 4.9 9 14.2 2.4 2.6 5.9 2.5 6.4 8.3

Sarawak 10 12.3 14.8 4.1 5.1 8.3 6 7.3 6.6

WP Kuala Lumpur 12.6 11.3 17.4 6.6 7.2 8.1 6 4.1 9.3

WP Labuan 7.9

4.8

3.2

WP Putrajaya

8.8 19.2

4.6 5.3

4.2 13.9

Data source: NHMS 2006 Vol.2 (page 254) NHMS 2011 Vo.2 (page 13) NHMS 2015 Vol.2 (page 25)


Indicator: Prevalence of impaired fasting glucose (Table 4 & Table 5)

Data table 21: Prevalenve of impaired fasting glucose (aged 18 years old and above)

Impaired Fasting Glucose (FBG 5.6 – 6.1 mmol/L)

2006 2011 2015

Malaysia 4.2 4.9 4.7

Johor 2.9 3.5 7.2

Kedah 4.4 7.8 6.4

Kelantan 4.5 7.6 6.6

Melaka 3.8 4.4 3.5

Negeri Sembilan 2.6 7 6.4

Pahang 3.4 5.2 4.3

Pulau Pinang 6.1 2.9 4.2

Perak 5 3.6 5.3

Perlis 3.4 11.9 4

Selangor 4.6 6.3 4.3

Terengganu 4 4.5 3.5

Sabah 2.7 4.4 3.3

Sarawak 4.8 3.5 2.9

WP Kuala Lumpur 6.1 2.2 3.8

WP Labuan 4.5

WP Putrajaya

1.2 6.5



Indicator: Prevalence of obesity (Table 4 & Table 5)

Data table 22: Prevalence of obesity among adults (aged 18 years old and above)

Obesity (BMI ≥ 27.5 kg/m2)

2006 2011 2015

Malaysia 14 27.2 30.6

Johor 14.1 28.6 29.8

Kedah 15.5 28.2 33.2

Kelantan 12.5 29.4 28.8

Melaka 17.4 29.3 36

Negeri Sembilan 18.6 28.2 35.6

Pahang 15.3 28.2 32.5


Perak 12.9 30.1 29.5

Perlis 17.2 34.6 36

Selangor 16 28.2 32.7

Terengganu 15.2 28.1 32.5

Sabah 9.7 21.1 23.9

Sarawak 11.5 26.7 32.3


WP Labuan 14.6

WP Putrajaya

27.4 43



Indicator: Prevalence of pre-obese (Table 4 & Table 5)

Data table 23: Prevalence of pre-obese among adults (aged 18 years old and above)

Pre-obese (BMI 23-27.4 kg/m2)

2006 2011 2015

Malaysia 29.1 33.3 33.4

Johor 28.9 33 33.5

Kedah 31.1 34 32.4

Kelantan 28.3 35.2 30.7

Melaka 31.1 33.7 33.6


Pahang 28.8 32.2 31.5


Perak 27.6 31.4 34.7

Perlis 32.1 29.1 30.7

Selangor 31 32.3 33.4


Sabah 24.9 34.5 36.9

Sarawak 28.7 32.4 31


WP Labuan 30.6

WP Putrajaya

36.3 34.4



Data table 24: Prevalence of overweight among adults (aged 18 years old and above)

Overweight (BMI ≥ 23kg/m2)

2006 2011 2015

Malaysia 29.1 33.3 33.4

Johor 28.9 33 33.5

Kedah 31.1 34 32.4

Kelantan 28.3 35.2 30.7

Melaka 31.1 33.7 33.6


Pahang 28.8 32.2 31.5


Perak 27.6 31.4 34.7

Perlis 32.1 29.1 30.7

Selangor 31 32.3 33.4


Sabah 24.9 34.5 36.9

Sarawak 28.7 32.4 31


WP Labuan 30.6

WP Putrajaya

36.3 34.4



Indicator: Prevalence of insufficient physical activity (Table 4 & Table 5)

Data table 25: Prevalence of Insufficient Physical Activity

Insufficient Physical Activity(%)

2006 2011 2015

Malaysia 43.7 35.2 33.5

Johor 45.6 41.4 32.9

Kedah 36.8 34.4 33.6

Kelantan 34.2 45 25.8

Melaka 39.3 39.7 37.8


Pahang 31.4 42.1 26


Perak 43.7 33.2 28.1

Perlis 49.1 32.4 27.8

Selangor 52.1 38.7 39.9

Terengganu 32.3 41 30.7

Sabah 42.5 24.8 30.1

Sarawak 48 32.4 40.8


WP Labuan 49.2

WP Putrajaya

57.3 32.5



Indicator: Male waist circumference > 90cm (Table 4) Female waist circumference > 80cm

Data table 26: Prevalence of abdominal obesity

Abdominal obesity(%)

2006 2011 2015

Malaysia - 45.4 48.6

Johor - 47.7 52.8

Kedah - 58.6 47.4

Kelantan - 36.8 39

Melaka - 45 54.2

Negeri Sembilan - 45.5 45.7

Pahang - 41.3 48.8

Pulau Pinang - 44.4 56.8

Perak - 56.5 48.6

Perlis - 57.7 51.6

Selangor - 45.1 47.2

Terengganu - 34.7 46.5

Sabah & WP Labuan - 34.6 46.4

Sarawak - 45.2 48

WP Kuala Lumpur - 46.1 51.8

WP Putrajaya - 41.3 61.3

Male 7.2 37.4 38.2

Female 26 54.1 60.2

Cut off points (>102 cm in men and >88 cm in woman) for data 2006 Cut off points (>90 cm in men and >80 cm in woman) for data 2011 & 2015 Data source: NHMS 2006 Vol.2 (page 788) NHMS 2011 Vo.2 (page 69) NHMS 2015 Vol.2 (page 70)


Data table 27: Characteristics of T2DM patients enrolled from 2009 to 2012 [Registry Dataset]

State

No of patients (%)

Male (%)

Mean Age

95% CI

Ethnicity

Malay (%) Chinese (%) Indian (%) Other Malaysian

(%)

Foreigner/ Unknown

(%)

Johor 92,750 (14.2) 38,386 (41.4) 59.8 59.7-59.9 58,306 (62.9) 22,724 (24.5) 11,219 (12.1) 397 (0.4) 104 (0.1)

Kedah 42,344 (6.5) 16,482 (38.9) 59.1 59.0-59.2 31,515 (74.4) 5,059 (11.9) 5,274 (12.5) 453 (1.1) 43 (0.1)

Kelantan 27,002 (4.1) 9,692 (35.9) 59.3 59.2-59.4 25,497 (94.4) 1,066 (3.9) 145 (0.5) 278 (1.0) 16 (0.1)

Melaka 42,974 (6.6) 18,640 (43.4) 61.0 60.9-61.1 28479 (66.3) 9,883 (23.0) 4,264 (9.9) 292 (0.7) 56 (0.1)

Negeri Sembilan 57,869 (8.9) 25,288 (43.7) 60.4 60.3-60.5 33,317 (57.6) 10,810 (18.7) 13,347 (23.1) 314 (0.5) 81 (0.1)

Pahang 38,119 (5.8) 15,972 (41.9) 58.9 58.8-59.1 29,700 (77.9) 5,450 (14.3) 2,664 (7.0) 201 (0.5) 104 (0.3)

Perak 74,492 (11.4) 31,604 (42.4) 61.1 61.1-61.2 38,867 (52.2) 18,869 (25.3) 16,113 (21.6) 588 (0.8) 55 (0.1)

Perlis 13,388 (2.1) 5,311 (39.7) 58.9 58.7-59.1 11,521 (86.1) 1,217 (9.1) 326 (2.4) 314 (2.3) 10 (0.1)

Pulau Pinang 40,439 (6.2) 17,271 (42.7) 60.6 60.5-60.7 17,758 (43.9) 14,534 (35.9) 7,876 (19.5) 210 (0.5) 61 (0.2)

Sabah 11,302 (1.7) 4,933 (43.6) 58.8 58.6-59.0 560 (5.0) 3,594 (31.8) 104 (0.9) 6,888 (60.9) 156 (1.4)

Sarawak 43,333 (6.6) 17,046 (39.3) 59.3 59.2-59.4 12,030 (27.8) 14,850 (34.3) 254 (0.6) 16,088 (37.1) 111 (0.3)

Selangor 106,101 (16.2) 45,019 (42.4) 58.5 58.4-58.6 55,245 (52.1) 19,664 (18.5) 29,603 (27.9) 1067 (1.0) 522 (0.5)

Terengganu 22,272 (3.4) 8,275 (37.2) 58.3 58.2-58.5 21,786 (97.8) 427 (1.9) 21 (0.1) 23 (0.1) 15 (0.1)

WP Kuala Lumpur 37,713 (5.8) 16,261 (43.1) 60.5 60.4-60.7 17,258 (45.8) 11,587 (30.7) 8,448 (22.4) 317 (0.8) 103 (0.3)

WP Labuan 524 (0.1) 202 (38.5) 55.8 54.8-56.8 363 (69.3) 72 (13.7) 4 (0.8) 77 (14.7) 8 (1.5)

WP Putrajaya 2,704 (0.4) 1,408 (52.1) 54.5 54.1-54.9 2,494 (92.2) 62 (2.3) 128 (4.7) 12 (0.4) 8 (0.3)

Total 653,326 (100) 271790 (41.6) 59.7 59.7-59.7 384,696 (58.9) 139,868(21.4) 99,790 (15.3) 27,519 (4.2) 1,453 (0.2)



Data table 28: Diabetes among adults aged 20-79 years

Country 2015

Australia 5.1

Korea, Rep. 7.2

Austria 6.9

Luxembourg 4.7

Belgium 5.1

Mexico 15.8

Canada 7.4

Netherlands 5.5

Chile 10

New Zealand 7.3

Czech Republic 7.4

Norway 6

Denmark 7.2

Poland 6.2

Estonia 4.4

Portugal 9.9

Finland 6

Slovak Republic 7.8

France 5.3

Slovenia 7.8

Germany 7.4

Spain 7.7

Greece 5.2

Sweden 4.7

Hungary 7.3

Switzerland 6.1

Iceland 6.1

Turkey 12.8

Ireland 4.4

United Kingdom 4.7

Israel 7.5

United States 10.8

Italy 5.1

OECD-34 7.01

Japan 5.7

Age standardized Source: International Diabetes Federation 2015 (page 116)


Data table 29: Prevalence of overweight (BMI ≥ 25kg/m2) and obesity (BMI ≥ 30kg/m2) (population aged 18+ years)

Country 2014

Australia 28.6

Korea, Rep. 5.8

Austria 18.4

Luxembourg 23.1

Belgium 20.2

Mexico 28.1

Canada 28

Netherlands 19.8

Chile 27.8

New Zealand 29.2

Czech Republic 26.8

Norway 23.1

Denmark 19.3

Poland 25.2

Estonia 22.6

Portugal 20.1

Finland 20.6

Slovak Republic 25.7

France 23.9

Slovenia 25.1

Germany 20.1

Spain 23.7

Greece 22.9

Sweden 20.5

Hungary 24

Switzerland 19.4

Iceland 22.8

Turkey 29.5

Ireland 25.6

United Kingdom 28.1

Israel 25.3

United States 33.7

Italy 21

OECD-34 23.0

Japan 3.3

Data source: Global status report on Noncommunicable Diseases 2014 (page 232)

Data table 30: Prevalence of insufficient physical activity (adults 18 years old and above)

Country 2010

Australia 23.8

Korea, Rep. 33.4

Austria 23.8

Luxembourg 28.5

Belgium 33.2

Mexico 26

Canada 23.2

Netherlands 15.5

Chile 21.3

New Zealand 39.8

Czech Republic 23.8

Norway 25.8

Denmark 24.3

Poland 18.7

Estonia 11.9

Portugal 34.9

Finland 23.5

Slovak Republic 17.8

France 23.8

Slovenia 21.3

Germany 21.1

Spain 30.5

Greece 12.9

Sweden 28.7

Hungary 18.1

Turkey 32.8

Ireland 35.1

United Kingdom 37.3

Italy 33.2

United States 32.4

Japan 33.8

OECD-31 23.5 Data source: Global status report on Noncommunicable Diseases 2014 (page 172)


BIBLIOGRAPHY

Avedis Donabedian, John R. C. Wheeler, & Leon Wys. (1982). Quality, Cost, and Health: An Integrative Model. Medical Care, 975-992.

Dr.Ahmad Faudzi Yusoff, Dr.Gurpreet Kaur, Dr.Mohd Azahadi Omar, & Dr.Amal Nasir Mustafa. (2004). Malaysian Burden of Disease and Injury Study. Kuala Lumpur: Institute for Public Health.

M.Y. Tan, & J. Magarey. (2008). Self-care practices of Malaysian adults with diabetes and sub-optimal glycaemic control. Patient Education and Counseling, 252–267.

Ministry of Health (MOH). (2010). Annual Report 2010 Non-Communicable Disease (NCD) Section.

Ministry of Health Malaysia. (2010). National Strategic Plan For Non-communicable Disease (NSPNCD) 2011-2014. Putrajaya: Non-Communicable Disease Section (NCD), Disease Control Division, Ministry of Health Malaysia.

Ministry of Health Malaysia. (2015). Management of Type 2 Diabetes Mellitus (5th Edition). Retrieved 11 July, 2016, from Portal Rasmi Kementerian Kesihatan Malaysia-CPG-Endocrine: www.moh.gov.my

OECD. (2012). “Avoidable admissions: Uncontrolled diabetes”, in Health at a Glance: Europe 2012. OECD Publishing.

Stratton, Irene M et al. (2000). Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ : British Medical Journal, 405-412.

World Health Organization. (2003). Health systems performance assessment: debates, methods and empiricism. Retrieved 11 July, 2016, from WHO| World Health Organization: www.who.int

World Health Organization. (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. The Lancet, 157-163.

World Health Organization. (2010). Global status report on noncommunicable diseases. Retrieved 11 July, 2016, from WHO| World Health Organization: www.who.int

World Health Organization. (2013). Global action plan for the prevention and control of noncommunicable diseases 2013-2020. Retrieved 11 July, 2016, from WHO| World Health Organization: www.who.int

World Health Organization. (2013). Malaysia health system review. (Health Systems in Transition, Vol. 2 No. 1 2012). Retrieved 11 July, 2016, from WHO| World Health Organization: www.wpro.who.int

Zanariah Hussein, Sri Wahyu Taher, Harvinder Kaur Gilcharan Singh, & Winnie Chee Siew Swee. (2015). Diabetes Care in Malaysia: Problems, New Models, and Solutions. Annals of Global Health, 851–862.


GLOSSARY

Accessibility Accessibility refers to physical access to the health service, or where the service can be delivered to people. It involves the overall organisation of the health system and especially, its procurement, supply and dispensing systems. In order to embrace the notion of access to treatment, accessibility in this publication is also understood as encompassing factors such as access to prescribers and proper education and information. (International Diabetes Federation 2015)

Acute Coronary Syndrome (ACS)

ACS encompasses clinical features comprising chest pain or overwhelming shortness of breath, is defined by accompanying clinical, ECG and biochemical features. ACS comprises the following: 1. Unstable Angina Pectoris (UAP) 2. Non-ST-elevation Myocardial Infarct (NSTEMI) 3. ST-elevation Myocardial Infarct (STEMI). (NCVD-ACS 2009-2010)

Admission Admission (or inpatient admission) is the formal acceptance by a hospital of a patient who will occupy a hospital bed, crib or bassinet for observation, care, diagnosis or treatment and will have a medical record maintained for him/her. (NHEWS 2012 – 2013)

Amputation Amputation is the intentional surgical removal of a limb or body part. It is performed mainly to remove diseased tissue. In this publication, amputation refers to particularly of the lower limbs.

Average annual rate of change

AAR is calculated by taking the geometric mean of the annual percentage difference between the baseline achievement (beginning value) and the current achievement (ending value) with the assumption that the achievements have been compounding discretely over the specified period.

Benchmark A benchmark refers to the performance that has been achieved in the recent past by other comparable organizations, or what can be reasonably inferred to have been achieved in the circumstances (OECD). (http://www.who.int/hac/about/definitions/en/)

Cardiovascular diseases

Cardiovascular diseases cover a range of illnesses related to the circulatory system, including ischemic heart disease and cerebrovascular diseases such as stroke (Health at a Glance 2015: OECD Indicators)

Crude measure Crude measures are methods that attempt to rely on non-fully-specified features of the world to ensure that an underdefined or underpowered solution does manage to solve the problem. (http://lesswrong.com/lw/ly9/crude_measures/)

Current smoker Smoker who daily or occasionally smokes any tobacco product.

Daily smoker Person who currently smokes any tobacco product every day.

DALY The Disability Adjusted Life Year or DALY is a health gap measure that extends the concept of potential years of life lost due to premature death (PYLL) to include equivalent years of ‘healthy’ life lost by virtue of being

http://www.who.int/hac/about/definitions/en/

http://lesswrong.com/lw/ly9/crude_measures/


in states of poor health or disability. (http://www.who.int/hac/about/definitions/en/)

Demography Demography is a statistical and mathematical study of the size, composition, spatial distribution of human population, and of changes overtime in these aspects through the operation of the five processes of fertility, mortality, marriage, migration and social mobility. (Bogue, Donald, 1969. Principles of Demography, Wiley, New York.)

Diabetes complications

Acute and chronic conditions caused by diabetes. Chronic complications include retinopathy (eye disease), nephropathy (kidney disease), neuropathy (nerve disease), cardiovascular disease (disease of the circulatory system), periodontitis (inflammation of the tissue surrounding the tooth), foot ulceration and amputation. (International Diabetes Federation 2015)

Diabetes Mellitus Undiagnosed Known

A metabolic disorder of multiple etiologies characterized by chronic high blood glucose levels with disturbance of carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action or both (NHMS Vol.2 2006) Not known to have diabetes and has a fasting capillary blood glucose equal to or more than 6.1 mmol/L (or non-fasting blood glucose of more than 11.1 mmol/L)(NHMS Vol.2 2011) Self-reported by subject, as having diagnosed with diabetes previously by medical personnel (NHMS Vol.2 2011)

Endocrinologist Endocrinologists treat people who suffer from hormonal imbalances, typically from glands in the endocrine system. (http://www.hormone.org/contact-a-health-professional/what-is-an-endocrinologist)

Family Medicine Specialist

Family medicine is the medical specialty which provides continuing, comprehensive health care for the individual and family. It is a specialty in breadth that integrates the biological, clinical and behavioural sciences. The scope of family medicine encompasses all ages, both sexes, each organ system and every disease entity. (http://www.aafp.org/about/policies/all/family-medicine-definition.html)

Foot examinations Check feet and toes, inspecting the tops, sides, soles, heels, and the area in between the toes. (http://www.healthline.com/health/diabetes-foot-care)

Funduscopy Ophthalmoscopy (also called fundoscopy) is a test that allows a doctor to see inside the back of the eye (called the fundus) and other structures using a magnifying instrument (e.g. ophthalmoscope) and a light source.

HbA1c Glycosylated haemoglobin A1c (HbA1c) refers to haemoglobin which glucose is bound. Glycosylated haemoglobin is tested to determine the average level of blood glucose over the past two to three months (International Diabetes Federation 2015)

http://www.who.int/hac/about/definitions/en/

http://www.hormone.org/hormones-and-health/the-endocrine-system

http://www.hormone.org/contact-a-health-professional/what-is-an-endocrinologist

http://www.hormone.org/contact-a-health-professional/what-is-an-endocrinologist

http://www.aafp.org/about/policies/all/family-medicine-definition.html

http://www.aafp.org/about/policies/all/family-medicine-definition.html

http://www.healthline.com/health/diabetes-foot-care

http://www.healthline.com/health/diabetes-foot-care


Health care facility Health-care facilities are hospitals, primary health-care centres, isolation

camps, burn patient units, feeding centres and others. (http://www.who.int/environmental_health_emergencies/services/en/)

Hospital An institution with primary function to provide inpatient diagnostic and therapeutic services for a variety of medical conditions both surgical and non-surgical. (NHEWS 2012-2013)

Impaired Fasting Glucose

Impaired fasting glucose (IFG) is categorized based on capillary whole blood glucose level within 5.6-6.1mmol/L range taken from subject without diabetes who fasted for at least 8 hours. (NHMS 2011)

Incidence The number of new cases of a disease among a certain group of people for a certain period of time. (International Diabetes Federation 2015)

Inpatient care In-patient care refers to care for a patient who is formally admitted (or

‘hospitalised’) to an institution for treatment and/or care and stays for a minimum of one night in the hospital or other institution providing in-patient care. (https://stats.oecd.org/glossary/detail.asp?ID=1364)

Insufficient physical activity

No activity is reported OR some activities are reported but not enough to meet moderate or high categories (IPAQ)

Kempen Cara Hidup Sihat

Kempen Cara Hidup Sihat is another initiative of the Ministry of Health to develop a healthy lifestyle through physical activity among Malaysians, especially the youth. (http://www.infosihat.gov.my/infosihat/projekkhas/kempen_nak_sihat.php)

KOSPEN The program "Komuniti Sihat Perkasa Negara" (KOSPEN) is an initiative that was triggered by the Health Minister of Malaysia, Datuk Seri S. Subramaniam in July 2013 in order to address the problem of increasing burden of NCDs (Non Communicable Diseases, NCDs) in the country through empowerment and community participation in the expansion of public health programs by combining existing government mechanisms, particularly at the grassroots. (http://www.infosihat.gov.my/infosihat/projekkhas/kospen.php)

Microalbuminuria Microalbuminuria is defined as an abnormal increase in albumin excretion rate within the specific range of 30–299 mg of albumin/g of creatinine. In diabetes, microalbuminuria is an early sign of diabetic kidney disease. (https://www.ncbi.nlm.nih.gov/pubmed/15538104)

Myocardial infarction

Occurs when the flow of blood to the heart is blocked, most often by a build-up of fat, cholesterol and other substances, which form a plaque in the arteries that feed the heart (coronary arteries). The interrupted blood flow can damage or destroy part of the heart muscle. (Health at a Glance 2015: OECD Indicators)

Nephropathy Damage, disease, or dysfunction of the kidney, which can cause the kidneys to be less efficient or to fail altogether. (International Diabetes Federation 2015)

http://www.who.int/environmental_health_emergencies/services/en/

https://stats.oecd.org/glossary/detail.asp?ID=1364

http://www.infosihat.gov.my/infosihat/projekkhas/kempen_nak_sihat.php

http://www.infosihat.gov.my/infosihat/projekkhas/kempen_nak_sihat.php

http://www.infosihat.gov.my/infosihat/projekkhas/kospen.php

http://www.medicinenet.com/diabetes_mellitus/article.htm

https://www.ncbi.nlm.nih.gov/pubmed/15538104


Obesity Obesity is a complex, multifactorial condition characterized by excess

body fat (CPG 2004- BMI ≥ 27.5 kg/m2; WHO 1998- BMI ≥ 30 kg/m2). Generally, men with >25% body fat and women with >35% body fat are considered obese (CPG Management of Obesity, 2004).

Overweight BMI 23-27.49 kg/m2. (NHMS Vol.2 2015)

Performance measure

Selection and use of quantitative measures of capacities, processes, and outcomes to develop information about critical aspects of activities, including their effect on the public. (Guidebook for Performance Measure)

Population aging Population ageing defined as a process which increases the proportion of old people within the total population. (United Nations, Department of Economic and Social Affairs, Population Division (2013). World Population Ageing 2013)

Prevalence The proportion or number of individuals in a population that has a disease or condition at a particular time (be it a point in time or time

period). (International Diabetes Federation 2015)

Primary care Basic or general healthcare focused on the point at which a patient ideally first seeks assistance from the medical care system. (NHEWS (Primary Care) 2008-2009)

Quality of care Quality of care can be defined as the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge. (Health Care Quality Indicators Project Conceptual Framework Paper, OECD)

Retinopathy A disease of the retina of the eye which may cause visual impairment and

blindness. (International Diabetes Federation 2015)

Urbanization Urbanization is a process of global scale changing of the social and environmental landscape. Urbanization is a result of population migration from rural areas in addition to natural urban demographic growth. (http://www.who.int/globalchange/ecosystems/urbanization/en/)

Urine test Urine test is tests performed in a clinical laboratory or at home using self-test kits and a sample of the patient's urine. Urine tests can be performed for a variety of reasons, but in people with diabetes, they are most commonly used to look for ketones or microalbumin. (http://www.medicinenet.com/urine_tests_for_diabetes/article.htm#urine_tests_for_diabetes_facts)

http://www.who.int/globalchange/ecosystems/urbanization/en/

http://www.medicinenet.com/diabetes_quiz/quiz.htm

http://www.medicinenet.com/urine_tests_for_diabetes/article.htm#urine_tests_for_diabetes_facts

http://www.medicinenet.com/urine_tests_for_diabetes/article.htm#urine_tests_for_diabetes_facts


INDICATOR OPERATIONAL DEFINITION

No Indicator Numerator Denominator Constant

1 Density of endocrinologist

Number of endocrinologist

Total midyear populaton

per 100 000

2 Primary care facility Number of public and private health clinics

Total midyear populaton

per 10 000

3 % Patients who had funduscopy done within 1 last year

Number of DM patients who had funduscopy done within 1 last year

Total number of DM patients by end of index year

per 100

4

% Patients who had foot examination done within the last 1 year

Number of DM patients who had foot examination done within 1 last year


per 100

5

% Patients who had urine checked for micro-albumin within the last 1 year

Number of DM patients who had urine checked for micro-albumin within 1 last year


per 100

6

% Patients who had HbA1c test done at least once within the last 1 year

Number of DM patients who had HbA1c test done at least once within 1 last year


per 100

7

Admission rate for uncontrolled DM without complications

Number of DM patients with uncontrolled DM without complications

Total midyear population of 15 years old and above

per 100 000

8 Admission rate for DM with short term complications

Number of DM patients with short term complications


per 100 000

9 Admission rate for DM with long term complications

Number of DM patients with long term complications


per 100 000

10 Admission rate for DM related hospital admission

Total number of DM-related (uncontrolled+ short term+ long term) hospital admission


per 100 000

11 Rate of retinopathy Number of DM patients with retinopathy


per 100

12 Rate of nephropathy Number of DM patients with nephropathy

Total number of DM patients by end of

per 100


index year

13 Rate of MI Number of DM patients who have had MI


per 100

14 Rate of cerebrovascular disease

Number of DM patients who have had cerebrovascular disease


per 100

15 Prevalence of DFU Number of DM patients with DFU


per 100

16 Rate of amputation Number of DM patients who have had lower limb amputation


per 100

17 % Patient who achieved BP target ≤ 130/80mm/Hg

Number of DM patients who achieved BP target ≤ 130/80mm/Hg


per 100

18 % Patient who achieved HbA1c target < 6.5%

Number of DM patients who achieved HbA1c target < 6.5%


per 100

19 % Patient who had BMI < 23kg/m2

Number of DM patients who had BMI < 23kg/m2


per 100

20 % Patient who achieved LDL target ≤ 2.6mmol/L

Number of DM patients who achieved

LDL ≤ 2.6mmol/L


per 100

21 Prevalence of obesity

Number of persons with BMI ≥27.5 kg/m2 OR

BMI ≥ 30.0 kg/m2 (WHO)


per 100

22 Prevalence of overweight

Number of persons with BMI 23 – 27.49 kg/m2


per 100

23 Prevalence of impaired fasting glucose (IFG)

Number of persons not known to have diabetes but had a fasting capillary blood glucose between 5.6 -6.6 mmol/L.


per 100

24 Prevalence of insufficient physical activity

Number of persons with low physical activity level assessed by IPAQ


per 100


25 Prevalence of overall DM

Total number of persons with undiagnosed DM and known DM


per 100

26 Prevalence of undiagnosed DM

Number of persons not known to have diabetes but had a fasting capillary blood glucose of ≥ 6.1 mmol/L.


per 100

27 Prevalence of known DM

Number of persons with known diabetes


per 100

diabetic care performance report draft 08/12/16

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