Primary  Care  and  Valuing  Diabetes  Care  in  Hong  Kong:    

Implica;ons  for  Developing  Health  Services  in  Mainland  China  

A  work  in  progress  Gabriel  M  Leung  and  Chao  Quan  

 

May  7,  2015      

Innova&ons  in  Primary  Care  Seminar  Series  Asia  Health  Policy  Program  

Shorenstein  APARC,  Stanford  University    

DM  BoD  China  v  US  

•  In  absolute  terms  – 113.9  million  in  China  (largest  in  the  world)  – 1  in  4  of  all  cases  worldwide  are  in  China  – 29.1  million  in  US  

•  In  relaKve  terms  – 11.6%  of  Chinese  adults  in  2010  – 9.3%  of  US  populaKon  in  2012    (12.3%  of  people  aged  ≥20  )    

Xu  et  al.  JAMA.  2013;310(9):948-­‐59  2014  NaKonal  Diabetes  StaKsKcs  Report,  CDC    IDF  Diabetes  Atlas  Sixth  EdiKon  Update,  InternaKonal  Diabetes  FederaKon  2014  

•  One  order  of  magnitude  increase  in  prevalence  •  1980:  <1%    •  1994:  2.5%  •  2010:  11.6%    

•  Pre-­‐diabetes    •  Prevalence  of  50.1%  =  493.4  million  •  Based  on  ADA  2010  criteria  

•  impaired  fasKng  glucose,  impaired  glucose  tolerance,    or  raised  HbA1c    

Xu  et  al.  JAMA.  2013;310(9):948-­‐59  NaKonal  Diabetes  Research  Group.    Zhonghua  Nei  Ke  Za  Zhi.  1981;20(11):678-­‐683.  Pan  et  al.,  Diabetes  Care.  1997;20(11):1664-­‐1669.    

An  ongoing  challenge  •  Develop  diabetes  at  younger  age  and  lower  BMI  

– High  prevalence  of  diabetes  despite  lower  levels  of  obesity  –  “normal-­‐weight  metabolically  obese”  

•  NutriKon  transiKon  –  Fast  food  and  refined  carbohydrates  (e.g.  white  rice)  

•  Economic  development  and  urbanizaKon  –  Sedentary  occupaKons  in  a  service-­‐led,  high  value-­‐added  economy  

•  Life  style  trends    –  Reduced  physical  acKvity  – High  smoking  rates  (52.9%  of  men)  

•  Macro  impact  of  socioeconomic  development  

Hu  FB.  Diabetes  Care.  2011;34(6):1249-­‐125  Xu  et  al.  JAMA.  2013;310(9):948-­‐59  Li  Q  et  al.,  N  Engl  J  Med  2011;  364:2469-­‐2470  Schooling  and  Leung.  JECH.  2010;64:941-­‐9    

“Rule  of  Halves”  

•  69.9%  are  unaware  of  their  diabetes  status  – US:  27.8%  

•  Only  25.8%  are  receiving  treatment  for  diabetes  – Of  which,  only  39.7%  had  adequate  glycaemic  control  (HbA1c  below  7.0%)  

•  Underdeveloped  and  unequal  health  care  access  •  Diabetes  treatment  guidelines  largely  based  on  evidence  from  non-­‐Asian  populaKons  

 Xu  et  al.  JAMA.  2013;310(9):948-­‐59  2014  NaKonal  Diabetes  StaKsKcs  Report,  CDC    

Economic  Cost  

•  China  spends  RMB  173.4b  (USD  25  billion)  annually  on  direct  diabetes  treatment  – 13%  of  total  medical  spending  – US:  direct  medical  costs  of  $176b  plus  indirect  costs  of  $69b  

•  Projected  annual  cost  of  RMB  360b  (USD  60b)  by  2030  

hjp://www.idf.org/china-­‐spends-­‐rmb-­‐1734-­‐billion-­‐us25-­‐billion-­‐year-­‐diabetes-­‐treatment  2014  NaKonal  Diabetes  StaKsKcs  Report,  CDC  hjp://www.thelancet.com/series/diabetes-­‐in-­‐china    

Anatomy of the HK Health System

Inpatient (bed-days) (admission)

90% 80%

10% 20%

Overall outpatient incl. TCM Specialist GP

30% 50% 30%

70% 50% 70%

System  

Funding  sources  

Purchasers  

Providers  

Consumers  

Market  share  

Department  of  Health  &  Centre  for  Health  Protec;on  • Disease  prevenKon  and  control  (communicable  and  non-­‐communicable  diseases)  • Elderly  health  • Health  educaKon  • HIV/AIDS  service  • Maternal  and  child  health  • Port  health  • Student  health  • Tobacco  control  • Tuberculosis  service  

General  populaKon    

Hospital  Authority    •   38  hospitals  •   GOPCs,  SOPCs    (predominantly  Western  allopathic  medicine)    

Public (Food and Health Bureau)

Private

Employers   Individuals  

Private insurers/MCOs

Government general revenue

Minimal out of pocket fees (waived for the indigent)

Universal  coverage  

Mostly individuals from middle and upper socioeconomic strata (except for Chinese medicine use)

Private providers

Western allopathic medicine

(73%)

Chinese medicine

(10%)

Dental medicine

(12%)

Laboratories (4%)

Public Health Personal Health Care

How much does HK spend on health ?

0

1

2

3

4

5

6

0

20,000

40,000

60,000

80,000

100,000

120,000

Perc

ent

HK$ M

illio

n

Financial year

TEH % GDP

11  

Public and private health spending shares

39.4 42.1 45.6 46.4 47.3 48.2 49.4 50.4 50.9 53.9 54.5 54.9 56.9 57.5 57.7 54.4 52.3 50.5 49.2 49.3 49.8 48.7

60.6 57.9 54.4 53.6 52.7 51.8 50.6 49.6 49.1 46.1 45.5 45.1 43.1 42.5 42.3 45.6 47.7 49.5 50.8 50.7 50.2 51.3

0

10

20

30

40

50

60

70

80

90

100

Shar

e of T

EH (%

)

Financial year

Public Private

Health spending by financing source

HK’s health expenditure by financing source

0

10

20

30

40

50

60

Shar

e of T

EH (%

)

Financial year

GovernmentEmployersInsuranceHouseholdsNon-profit institutionsOthers

Health  spending  by  healthcare  func;on  and  financing  source  (2010/11)  

58

89

37

94

97

83

60

3

91

12

73

42

11

63

6

3

17

40

97

9

88

27

0 5 10 15 20 25 30 35

Inpatient curative care

Day patient hospital services

Ambulatory services

Home care

Rehabilitative and extended care

Long-term care

Ancillary services to health care

Medical goods outside the patient care setting

Prevention and public health services

Health programme administration and health insurance

Investment in medical facilities

Share of TEH (%)

Public Private

Hong Kong has spent relatively less on health compared to OECD countries (2010)

Australia

Austria

Belgium

Canada

Chile

Czech  Republic

Denmark

Estonia

Finland

France

Germany

Greece

Hong  Kong  SAR,  China

Hungary

IcelandIreland

Israel

Italy

Japan

Korea

Luxembourg

Mexico

Netherlands

New  Zealand

Norway

Poland

PortugalSingapore

Slovak  RepublicSlovenia

Spain

Sweden

Switzerland

United  Kingdom

United  States

0  

1,000  

2,000  

3,000  

4,000  

5,000  

6,000  

7,000  

8,000  

9,000  

0   10,000   20,000   30,000   40,000   50,000   60,000   70,000   80,000   90,000  

Per  C

apita

 TEH

 (US$  PPP

)

Per  Capita  GDP  (US$  PPP)

…although  public  spending  is  commensurate  with  the  different  levels  of  public  revenue  between  countries  (2010)  

Australia

Austria

BelgiumCanada

Chile

Czech  Republic

Denmark

Estonia

Finland

France

Germany

Greece

HungaryHong  Kong  SAR,  China

Iceland

Ireland

Israel

Italy

Japan

Korea

Luxembourg

Mexico

New  Zealand

Norway

Poland

Portugal

Singapore

Slovak  Republic

Slovenia

Spain

Sweden

Switzerland

United  Kingdom

United  States

0  

500  

1,000  

1,500  

2,000  

2,500  

3,000  

3,500  

4,000  

4,500  

5,000  

0   5,000   10,000   15,000   20,000   25,000   30,000   35,000   40,000  

Per  C

apita

 Pub

lic  Expen

diture  on  He

alth  (U

S$  PPP

)

Per  Capita  Public  Revenues  (US$  PPP)

Highly  subsidized  public  service  

General outpatient

Accident & Emergency

Specialist outpatient

Inpatient

0 20 40 60 80 100

User charges as a percentage of cost, %

Government subsidyUser fee

Mainland  China  vs  HKSAR  

China  mainland   Hong  Kong  SAR  

PopulaKon  (million)   1  347   7  

of  which   Urban   691   N/A  

Rural   657   N/A  

GDP  per  capita  (constant  2005  US$)   3  122   32  608  

Total  health  spending  per  capita   156.9   1  719  

Total  health  spending  as  %  of  GDP   5.1%   5.2%  

General  government  (government  +  social)  spending  as  %  of  total  government  spending  

12.5%   13.5%  

PharmaceuKcals  as  share  of  total  health  spending   N/A   11.9%  

Financing  mix  

$US  at  2005  price  level  

THE  per  capita  

Financing  mix  

Gov’t   SHI   PHI   OOP  

China  mainland  

1995   26.1   18.1%   32.4%   0%   46.4%  

2003   68.1   16.9%   19.3%   3.7%   55.9%  

2011   156.9   18.4%   37.4%   2.8%   34.8%  

Hong  Kong  SAR  

1998   1  037   53.9%   N/A   12.3%   32.4%  

2004   1  290   54.4%   N/A   12.3%   32.3%  

2011   1  719   48.3%   N/A   14.9%   34.9%  

Service  delivery  supply  

Human  resources   China  mainland   Hong  Kong  SAR  

Western  allopathic  doctors  per  100  000  populaKon   135.7   181.3  

Chinese  medicine  pracKKoners  per  100  000  populaKon   14.2   130.5  

Nurses  per  100  000  populaKon   166.6   650.0  

Hospitals  

Hospitals  per  100  000  populaKon   1.6   0.7  

of  which   Public   1.0   0.5  

Private   0.6   0.2  

Hospital  beds  per  100  000  populaKon   275.0   440.3  

of  which   Public   240.7   382.4  

Private   34.2   58.0  

Primary  care  providers  

Primary  care  providers  per  100  000  populaKon   N/A   57.7  

Data  Sources  

•  Hospital  Authority  Clinical  Management  System  •  6.1  million  unique  paKents  during  2006-­‐2013  •  InpaKents,  outpaKents  (primary  care  and  specialist)  and  accident  &  emergency  ajendances  

•  Demographics  •  Diagnosis  and  procedure  codes  •  Basic  clinical  data  •  Laboratory  results  •  MedicaKons  

PopulaKon  Coverage  of  HA  

2006 2007 2008 2009 2010 2011 2012 2013

Percentage of HK population attending public services

0

10

20

30

40

0

5

10

15

20

25

30

35

40

45

InpatientA&EGOPCSOPCTotal

(%)

Ascertainment  of  diabetes  WHO  (2011)   ADA  (2015)   HKU  

Any  of  the  following:  

HbA1c   One  measurement  ≥6.5%  (≥48  mmol/mol)  

Fas;ng  plasma  glucose   One  measurement  ≥7.0mmol/l  (≥126mg/dl)  

OGTT1   One  measurement  ≥11.1  mmol/l  (≥200mg/dl)  

Random  plasma  glucose      One  measurement  ≥11.1  mmol/l  (≥200mg/dl)  with  

symptoms  

TWO  measurements  of  ≥11.1  mmol/l  on  

separate  days  

ICD-­‐9  diagnosis  codes           250.x  

ICPC-­‐2  diagnosis  code           T89  &  T90  

1  OGTT  (Venous  plasma  glucose  2–h  auer  ingesKon  of  75g  oral  glucose  load)  

LAB  GLUCOSE  TESTS  (n=318,502)  • HbA1c  (n=188,584)  • FasKng  glucose  (n=109,899)  • OGTT  (n=7,872)  • TWO  random  glucose  (n=12,147)  

ATTENDANCE  DIAGNOSIS  CODES  (n=362,658)  • InpaKent  (n=170,562)  • Specialist  outpaKent  (n=2,259)  • General  outpaKent  (n=186,109)  • A&E  (n=3,728)  

EXCLUDE  AGE  UNDER  20  AT  TIME  OF  DIAGNOSIS  (n=1,028)  

TOTAL  PATIENTS  (n=680,132)  

All cases

HbA1c

Fasting glucose

General outpatient

Inpatient

2x Random glucose

Specialist outpatient

A&E

OGTT

Number of diabetes cases identified by diagnosis criterion

'000 cases

0 100 200 300 400 500 600 700

HbA1cFasting glucoseOGTTRandom glucoseInpatientSpecialist outpatientGeneral outpatientA&E

HbA1c Fasting glucose Inpatient General outpatient

PaKents  outside  the  public  sector  

•  Number  of  cases  was  adjusted  for  individuals  who  had  only  sought  care  from  the  private  sector  

•  EsKmated  the  proporKon  of  paKents  with  diabetes,  straKfied  by  age  and  sex,  who  only  sought  follow-­‐up  care  in  private  sector  

•  Based  on  Government’s  ThemaKc  Household  Survey  2011    Follow-­‐up  care  for  pa&ents  with  diabetes  (overall  figures)  

Public  Sector     Private  sector   Both  public  and  private   No  follow-­‐up  

Male   84.7%   8.5%   1.9%   4.9%  Female   87.1%   7.5%   1.1%   4.3%  

Census  and  StaKsKcs  Department.  ThemaKc  Household  Survey  Report  No.  45.  Hong  Kong  SAR    

Incidence  and  Prevalence  

•  Counted  as  an  incident  case  in  the  year  of  diagnosis  date,  then  excluded  from  the  numerator  and  denominator  for  subsequent  years  

•  PaKents  with  a  diagnosis  date  before  Jan  1,  2007  were  classed  as  pre-­‐exisKng  diabetes  and  excluded  from  the  incidence  figures.    

•  New  cases  were  assumed  to  have  occurred  at  the  beginning  of  each  calendar  year;  thus,  the  person-­‐Kme  at-­‐risk  was:      (Mid-­‐year  populaKon  aged  20y  or  over  –  Cases  of  diabetes  on  January  1  of  that  year)  x  1  year  

•  Prevalence  was  calculated  as  the  number  of  diabetes  paKents  alive  on  January  1  divided  by  the  Hong  Kong  populaKon  esKmate  (aged  20y  or  over)  on  January  1  

•  PopulaKon  esKmates  of  residents  based  on  Government  Census  &  StaKsKcs  Department  published  figures  

Prevalence  of  diagnosed  diabetes  in  Hong  Kong,  %  (2013)  

20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85+ total

Age

%

0

5

10

15

20

25

30

35 MaleFemaleBoth Sexes

Prevalence in 2013 (%)

Overall: 8.9%

Incidence  of  diagnosed  diabetes  in  Hong  Kong,  per  1,000  person-­‐years  (2013)  

20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85+ total

Age

per 1

,000

per

son-

year

s

0

5

10

15

20

25

30

35 MaleFemaleBoth Sexes

Incidence in 2013 (per 1,000 person-years)

Overall: 10.1

ComparaKve  Prevalence  for    HKSAR  vs  mainland  China    

•  Prevalence  of  8.9%  in  Hong  Kong  auer  adjusKng  for  paKents  who  only  use  private  care  in  2013  –  unadjusted  prevalence  of  8.2%  

–  EsKmated  prevalence  (by  populaKon-­‐based  tesKng)  of  11.6%  among  mainland  Chinese  adults  in  2010  –  Prevalence  of  14.3%  in  urban  developed  areas  –  69.9%  unaware  of  their  diabetes  status  

•  Hong  Kong  is  the  most  and  longest  developed  Chinese  city    –  ConservaKvely  assuming  27.8%  are  unaware  as  per  US    

 à  actual  prevalence  rate  of  12.3%  

Xu  et  al.  JAMA.  2013;310(9):948-­‐59  2014  NaKonal  Diabetes  StaKsKcs  Report,  CDC  

Stable  incidence  rate,  by  age  group  

0  

5  

10  

15  

20  

25  

30  

35  

40  

45  

2007   2008   2009   2010   2011   2012   2013  

Incide

nce  rate,  p

er  1000  pe

rson

-­‐years  

Year  

Incidence  rate  by  age  group,  per  1000  person-­‐years  

20-­‐24  

25-­‐29  

30-­‐34  

35-­‐39  

40-­‐44  

45-­‐49  

50-­‐54  

55-­‐59  

60-­‐64  

65-­‐69  

70-­‐74  

75-­‐79  

80-­‐84  

85+  

Total  

Measuring  gains  in  health  status  

•  Assessed  changes  in  mean  modifiable  5-­‐year  risk  using  UKPDS  and  Hong  Kong-­‐specific  (CUHK)  risk  predicKon  models  

•  Compared  across  four  2-­‐year  periods:  2006-­‐07,  2008-­‐09,  2010-­‐11  and  2012-­‐13  

•  Calculated  the  latest  risk  esKmates  in  each  period  keeping  age  (and  diabetes  duraKon)  at  baseline  values  

•  Thus  the  difference  between  risk  esKmates  reflected  changes  in  risk  factors  potenKally  ajributable  to  clinical  care  (modifiable  risk)  

Modifiable  risks  and  other  covariables  UKPDS  Stroke  

UKPDS  CHD  

CUHK  Stroke  

CUHK  CHD   CUHK  Mortality  

Systolic  BP   AnK-­‐hypertensives  

Lipid  raKo  /  Cholesterol  

StaKns   StaKns  

HbA1c   AnK-­‐glycaemics  

Urine  ACR   ACE-­‐Inhibitors    

Co-­‐linearity?  eGFR  

Haemoglobin  

Other  factors   Age,  Sex,  Smoking,  DuraKon  of  DM,  (and  AF  for  stroke)  

Age,    Hx  of  CHD  

Age,  Sex,  Smoking,  DuraKon  of  DM  

Age,  Sex,  Peripheral  arterial  disease,  Hx  of  Cancer,  BMI,  Insulin  use  

Stevens,  R.J.  et  al.,  Clin  Sci  (Lond).  2001;101:671–679;.  Kothari  V  et  al.,  Stroke.  2002  Jul;33(7):1776-­‐81.  Yang  X.  et  al,  Diabetes  Care.  2007  Jan;30(1):65-­‐70;.  Yang  X.  et  al.  Arch  Intern  Med.  2008  Mar  10;168(5):451-­‐7.      

CUHK  formulae  underesKmated  the  risk  of  death  

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC= 0.81

ROC curve

Male Female Overall

PredictedObserved

0

2

4

6

8

10

12

Death at 5-years, %

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC= 0.804

Male

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC= 0.813

Female

UKPDS  overesKmated  the  risk  of  CHD  

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC= 0.632

ROC curve

Male Female Overall

PredictedObserved

0

5

10

15

CHD at 5-years, %

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC= 0.605

Male

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC= 0.649

Female

Both  formulae  overesKmated  the  risk  of  stroke  

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC= 0.654

UKPDS

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC= 0.686

CUHK

Male Female Overall

PredictedObserved

0

2

4

6

8

10

12

UKPDS: Stroke at 5-years, %

Male Female Overall

PredictedObserved

0

2

4

6

8

10

12

CUHK: Stroke at 5-years, %

Known  caveats  of  risk  predicKon  models  

•  Original  Framingham  CHD  risk  assessment  tools  overesKmated  the  risk  of  CHD  for  Chinese  populaKons  

•  UKPDS  overesKmated  coronary  heart  disease  and  stroke  risk  in  type  2  diabetes  mellitus  for  Chinese  populaKons  

•  CUHK  models  suffered  from  co-­‐linearity  and  lack  of  widely  available  clinical  measurements  

•  We  are  currently  working  on  calibraKng  our  own  risk  predicKon  esKmates  

Liu  J.  et  al.,  JAMA.  2004  Jun  2;291(21):2591-­‐9.  Yang  X.  et  al.,  Am  J  Cardiol.  2008  Mar  1;101(5):596-­‐601.  Yang  X.  et  al,  Diabetes  Care.  2007  Jan;30(1):65-­‐70.          

Defining  subcohorts  

•  Divided  the  diabetes  paKents  based  on  the  year  of  diagnosis  into  four  subcohorts:  – Pre-­‐2006  (defined  by  full  data  availability)  – 2006-­‐2007  – 2008-­‐2009  – 2009-­‐2010  

•  Divided  up  paKents  based  on  age  of  diabetes  onset:  – Under  vs  at  least  60y  at  onset  

CharacterisKcs  of  Diabetes  paKents  Diagnosis  cohort  

Characteris;cs   Before  2006   2006-­‐2007   2008-­‐2009   2010-­‐2011   En;re  sample  

PaKents,  n   186,805   106,585   86,696   80,078   460,164  

Cohort  entry  date   Jan  1,  2006   Date  of  diagnosis  Date  of  

diagnosis  Date  of  

diagnosis   -­‐  

Mean  age  at  cohort  entry  (SD),  y   62.86  (12.08)   62.12  (12.96)   61.85  (13.53)   60.77  (13.29)   62.13  (12.8)  

Mean  years  since  diagnosis  (SD)   7.46  (6.18)   1.37  (0.59)   1.12  (0.6)   1.15  (0.6)   3.76  (5.01)  

Sex,  n  (%)  

Male   88135  (47.2)   51197  (48)   45070  (52)   40770  (50.9)   225172  (48.9)  

Female   98670  (52.8)   55388  (52)   41626  (48)   39308  (49.1)   234992  (51.1)  Mean  BMI  (SD),    kg/m2   25.21  (4.01)   25.76  (4.19)   26.16  (4.31)   26.1  (4.33)   25.64  (4.18)  Smoking  status  ,  n  (%)  

Current   12682  (6.8)   7908  (7.4)   8433  (9.7)   7328  (9.2)   36351  (7.9)  

Former   29619  (15.9)   15703  (14.7)   12865  (14.8)   12182  (15.2)   70369  (15.3)  

Non-­‐smoker   144504  (77.4)   82974  (77.8)   65398  (75.4)   60568  (75.6)   353444  (76.8)  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

6.0

6.5

7.0

7.5

8.0

HbA1c, %

2006-20072008-20092010-20112012-2013

Under  60  years  of  age  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

6.0

6.5

7.0

7.5

8.0

HbA1c, %

2006-20072008-20092010-20112012-2013

60  years  and  over  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

6.0

6.5

7.0

7.5

8.0

HbA1c, %

2006-20072008-20092010-20112012-2013

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

120

125

130

135

140

145

Systolic BP, mmHg

2006-20072008-20092010-20112012-2013

Under  60  years  of  age  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

120

125

130

135

140

145

Systolic BP, mmHg

2006-20072008-20092010-20112012-2013

60  years  and  over  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

120

125

130

135

140

145

Systolic BP, mmHg

2006-20072008-20092010-20112012-2013

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Lipid ratio (Total:HDL-Cholesterol)

2006-20072008-20092010-20112012-2013

Under  60  years  of  age  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Lipid ratio (Total:HDL-Cholesterol)

2006-20072008-20092010-20112012-2013

60  years  and  over  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Lipid ratio (Total:HDL-Cholesterol)

2006-20072008-20092010-20112012-2013

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

0

5

10

15

20

25

Urine Albumin:Creatinine ratio

2006-20072008-20092010-20112012-2013

Under  60  years  of  age  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

0

5

10

15

20

25

Urine Albumin:Creatinine ratio

2006-20072008-20092010-20112012-2013

60  years  and  over  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

0

5

10

15

20

25

Urine Albumin:Creatinine ratio

2006-20072008-20092010-20112012-2013

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

024681012141618202224

UKPDS-predicted 5 year risk for CHD, %

2006-20072008-20092010-20112012-2013

Under  60  years  of  age  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

024681012141618202224

UKPDS-predicted 5 year risk for CHD, %

2006-20072008-20092010-20112012-2013

60  years  and  over  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

024681012141618202224

UKPDS-predicted 5 year risk for CHD, %

2006-20072008-20092010-20112012-2013

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

0

2

4

6

8

10

12

14

16

18

UKPDS-predicted 5 year risk for Stroke, %

2006-20072008-20092010-20112012-2013

Under  60  years  of  age  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

0

2

4

6

8

10

12

14

16

18

UKPDS-predicted 5 year risk for Stroke, %

2006-20072008-20092010-20112012-2013

60  years  and  over  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

0

2

4

6

8

10

12

14

16

18

UKPDS-predicted 5 year risk for Stroke, %

2006-20072008-20092010-20112012-2013

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

0

2

4

6

8

10

CUHK-predicted 5 year risk for Stroke, %

2006-20072008-20092010-20112012-2013

Under  60  years  of  age  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

0

2

4

6

8

10

CUHK-predicted 5 year risk for Stroke, %

2006-20072008-20092010-20112012-2013

60  years  and  over  

Before 2006 2006-2007 2008-2009 2010-2011 Overall

Diagnosis cohort

0

2

4

6

8

10

CUHK-predicted 5 year risk for Stroke, %

2006-20072008-20092010-20112012-2013

Net  value  of  benefits  and  spending  

•  We  are  analyzing  the  uKlizaKon  data  to  empirically  esKmate  the  value  of  saved  medical  treatment  costs  from  avoiding  diabetes-­‐related  complicaKons  such  as  CHD  and  stroke.    

•  We  will  also  esKmate  the  monetary  value  of  improved  survival  based  on  different  assumpKons  of  the  value  of  a    life-­‐year.    

…