Quantifying Tuberculosis y gBurden and Underrepresentation
in Malaysia 1990 2014in Malaysia, 1990-2014
Nurhuda Ismail, Awang M Bulgiba, Sanjay Rampal, Nicolaas JD Nagelkerke, Jiloris F. Dony, Omar AwangNicolaas JD Nagelkerke, Jiloris F. Dony, Omar Awang
22 October 201522 October 2015
Contents
1. Introduction
2. Methods
3 Results3. Results
4 Discussion4. Discussion
2
Question:
Does TB incidence data in Malaysia reflectDoes TB incidence data in Malaysia reflect true burden?
125
103
17
Prevalence, incidence, and mortality rates per 100 000 population in countries and areas with an intermediate burden of TB in the Western Pacific Region 2006
4
Source: WHO Tuberculosis Control WPR Report, 2008
COMMUNICABLE DISEASES INCIDENCE RATE MORTALITY RATEAIDS 6.91 1.43HIV (all forms) 21.88 3.66
Incidence Rate and Mortality Rate of ( )
Chancroid 0.00 0.00Cholera 0.89 0.01Dengue fever 64.37 0.01Dengue Haemorrhagic fever 4.10 0.25
Mortality Rate of Communicable Diseases per 100 000 Population in Malaysia, 2006
Diphteria 0.00 0.00Dysentry 0.39 0.01Food Poisoning 26.04 0.01Gonoccocal Infection 1.90 0.00
y ,
•16,665 new TB cases Leprosy 0.89 0.00Malaria 19.87 0.08Measles 2.27 0.00Plague 0.00 0.00
,(mean ~ 46 cases /day)
• 1460 TB deathPolio Myelitis 0.00 0.00Rabies 0.00 0.00Relapsing Fever 0.00 0.00Syphillis 3.06 0.01
(mean ~ 4 death/day)
Tetanus Neonatorum 0.04 0.01Tetanus (Adult) 0.06 0.00
Tuberculosis 62.56 5.32Typhoid and Paratyphoid 0 77 0 02Typhoid and Paratyphoid 0.77 0.02Typhus 0.06 0.01Viral Enchepalitis 0.09 0.00Viral Hepatitis 9.37 0.18Whooping cough 0 02 0 00
Source: Ministry of Health, 2008Whooping cough 0.02 0.00Yellow fever 0.00 0.00Hand Foot and Mouth 19.30 0.02Ebola 0.00 0.00
Top 5 Communicable Disease,Top 5 Communicable Disease,Top 5 Communicable Disease, Malaysia (2010- 2013)
Top 5 Communicable Disease, Malaysia (2010- 2013)
Disease2011 2012 2013
IncidenceRate
Mortality Rate
IncidenceRate
Mortality Rate
IncidenceRate
Mortality RateRate Rate Rate Rate Rate Rate
Dengue Fever 63.75 0 72.20 0 143.27 0.21
Tuberculosis 71.35 5.68 77.41 4.82 81.0 5.4Hand, Food & Mouth Diseases 24.17 0 24.17 0 78.52 0.0Mouth Diseases Food Poisoning 56.25 0.03 56.25 0.03 47.79 0.04
Malaria 18.32 0.06 16.11 0.05 1.30 0.01
Source: Ministry of Health, 2014Note:*Incidence Rate was per 100,000 population except for Malaria per 10,000 population*Mortality Rate was per 100 000 population except for Malaria per 10 000 population
6
Mortality Rate was per 100,000 population except for Malaria per 10,000 population
BCG vaccination coverage: 98% DOTS coverage: 98% XDR/MDRTB cases: 55-135 Success treatment rate: 85%
8182
82
86
77
81
74
78
82
opul
atio
n
6867 67
65 66 65 64
68
71
66
70
74
100,
000
po
65
61 6160
63 63
6059 60
6264
6562
59
64
60 6163 62 63 64
58
62
66
Rat
e pe
r
54
58
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
000
001
002
003
004
005
006
007
008
009
010
2011 012
013
014
Incidence rate of TB in Malaysia 1985 - 2014
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Year
Source: TBIS, Ministry of Health, 2015
TB Projection in Malaysia 1990-203035000
25000
30000
es
Projection of TB cases
Retrospective projection of TB cases
20000
f TB
cas
e Retrospective projection of TB cases
15000
umbe
r of
5000
10000Nu
0
5000
Time/year Source: Ismail N et al. 2012
Contents
1. Introduction
2. Methods
3 Results3. Results
4 Discussion4. Discussion
11
Study Profile
• Study design/approach: infectious disease mathematical modelling
• Study and target population: Malaysian population
• Main Data Sources:– Tuberculosis Information System (TBIS), Ministry of Health, Malaysia
(data from 1990-2010)– International Union Against Tuberculosis & Lung Disease (IUATLD)– WHO and World Bank
• Model system: deterministic, compartmental, SEIRode syste dete st c, co pa t e ta , S
• Numerical analysis system: ODE, 4th order Runge Kutta (RK4) iterative method
• Modelling Software: ModelMaker 4, Berkeley Madonna
12
Refinement of ModelRefinement of Model
Homogenous model
Homogenous modelmodelmodel
0 - 14 15 - 29 30 - 44˄ ˅
45 - 59 60 - 74 75 - 89
90+
Gender-structured model
Age-structured model
Age-structured model
90+
13
Contents
1. Introduction
2. Methods
3 Results3. Results
4 Discussion4. Discussion
14
Transmission of TB and Interventions
Non-infectedNon-infected •Early diagnosisNon-infectedPersons
Non-infectedPersons Infectious
TBC
Infectious TB
C
y g•Current treatment regimes
CasesCases
Infected Persons(LTBI)
Infected Persons(LTBI)
Hi h Ri kHigh RiskInfected Persons
•Identify with TST/IGRA•Intervention (IPT)•Identify with TST/IGRA•Intervention (IPT)
The Tuberculosis Transmission Dynamic Model for Malaysia
S R٨ µ
δ2kxS Rµ
Lλ rp
LE αdpµ
Id
(1 ‐ α)kt
LLdn
δ1kxµ
µ + µt
DIFFERENCE EQUATIONS DIFFERENTIAL EQUATIONS
St+1 = ٨tN + (1 - βIt - µt)St
LEt+1 = βItSt + δ2kxtRt+ (1 - µt - αdpt – ktt + αktt)LEt
LLt+1 = (1 ‐ α)kttLEt + (1 - δ1kxt - µt - dnt)LLt
It+1 = αdptLEt + δ1kxtLLt + dntLLt + ptRt + (1 - µtbt - µt - rt)It
Rt+1 = rtIt + (1 - pt - µt - δ2kxt)Rt
Various states of population in tuberculosis transmission dynamic of the model
State Descriptions Assumptions
S Number of susceptible individuals
S Number of susceptible individuals
LE
Number of early latent tuberculosis infected individuals
History of contacts within the first 5 years of exposure regardless of prior exposure status. Includes exogenous reinfection i e those reinfected following successfulreinfection i.e. those reinfected following successful treatment (recurrent exposure)
LL Number of late latent tuberculosis infected individuals
History of contacts more than 5 years of exposure individuals
I
Number of infectious tuberculosis cases
Equates annual incidence of tuberculosis at t time
R Number of those recovered from
tuberculosis following successful treatment
*N Total population N equals to total Malaysian population i.e. aggregation of all other states at time ‘t’, N(t) = S(t) + LE(t) + LL(t) + ( ) ( ) ( ) ( )I(t) + R(t)
18
Parameter Descriptions & Assumptions
Value/Unit
Reference
List of parameters used in the model (1)
٨
Recruitment rate. Equates only birth rate. BCG immunization does not play any role in this type of tuberculosis transmission dynamic
0.03/year
Malaysian Department of Statistic 2009; Malaysian
MOH, 2009
µ
Mortality rate due to other causes. The annual crude death rate minus µt
0.015/year
Malaysian Department of Statistic 2009; Malaysian
MOH, 2009
µt Mortality rate due to infectious tuberculosis. 0.1/year Malaysian MOH, 2009µt
yTakes into account the national TB death notification rate and does not include those with successful treatment
y
y ,
λ Force of infection, taking into account frequency dependent mechanism or mass
derivatives according to age
frequency dependent mechanism or mass
action with homogenous mixing: λ =
according to age and gender
Probability of effective tuberculosis transmission
0.35
Dye et al., 1998
c
Per capita contact rate
10/year
Malaysian MOH, 2009
α
Probability of those who develop primary infection within 5 years of exposure
0.07 Dye et al., 1998
kt
Rate of progression of those who have been infected yet to develop the disease into late latent group
0.2/year
Dye et al., 1998
kx Rate of exogenous reinfection. Exogenous reinfection rate k defined as k = aλ where
derivatives according to age
19
reinfection rate kx defined as kx = aλ where a is the co-efficient of exogenous reinfection and similar infection and reinfection rate, a = 1
according to age and gender
D i ti & A ti
V l /U it
R f
List of parameters used in the model (2)Parameter Descriptions & Assumptions Value/Unit Reference
δ1kx Rate of exogenous reinfection where
individuals at the state of late latent infection where δ1 is the coefficient of the first pathway of exogenous reinfection and δ k = δ k
0.75/year
Styblo K., 1986; Comstock G.W., 1982;
Verver S., 2005
δ1kx = δ2kx
δ2kx Rate of recurrent infection of tuberculosis following successful treatment (recurrent exposure). where δ2 is the coefficient of the second pathway of exogenous reinfection and δ1kx = δ2kx
0.75/year Styblo K., 1986; Comstock G.W., 1982;
Verver S., 2005
1 x 2 x
dp
Rate of developing infectious tuberculosis from early latent state (primary infection)
0.25/year
Vynnycky & Fine, 1997
dn Rate of developing infectious tuberculosis from late latent group via reactivation
0.00256/year Vynnycky & Fine, 1998
(endogenous route)
r
Rate of effective treatment of those with infectious tuberculosis
0.8/year Malaysian MOH, 2007
p Rate of relapse for tuberculosis. Relapse b k t i f ti t t dl f
0.025/year Malaysian MOH, 2007back to infectious state regardless of bacteriological status
Θ
Rate of effective IPT for early LTBI. Individuals in the early latent (LE) who do not progress into infectious state due to effective treatment of Isoniazid preventive
0.03/year Model output
effective treatment of Isoniazid preventive therapy
π Rate of effective IPT for late LTBI. Individuals in late latent (LL) who do not progress into infectious state due to effective treatment of Isoniazid preventive
0.007/year Model output
20
ptherapy
The optimized and best-fitted model showing predicted tuberculosis cases against observedtuberculosis cases in Malaysia from 1990 till 2010 indicated as blue line (Inftotal) and red dots(C N tifi ti ) ti l Th l d b d j ti f th d l l
21
(Case Notifications) respectively. The lower and upper border projections of the model are alsoshown as pink dot-dash line (minimum) and green dash line (maximum) respectively. Theoptimization statistics derived using Marquardt method yield r2 = 0.9315, p<0.0001.
TB Projection in Malaysia 1990-203035000
25000
30000
es
Projection of TB cases
Retrospective projection of TB cases
20000
f TB
cas
e Retrospective projection of TB cases
15000
umbe
r of
5000
10000Nu
0
5000
Time/year Source: Ismail N et al. 2012
TB Projection in Malaysia 1990-203035000
25000
30000
es
Observed number of TB casesProjection of TB casesR i j i f TB
20000
f TB
cas
e Retrospective projection of TB cases
15000
umbe
r of
5000
10000Nu
0
5000
Time/year Source: Ismail N et al. 2012
Inftotal Case Notifications
Estimates and Projection based on TB notification data in Malaysia from 1990‐2011
30000
35000
25000
ases
15000
20000
mbe
r of T
B c
10000
Num
0
5000
Year
Using data between 1990‐2010, the annual mean difference or underrepresentation is 13.11% (95%CI: 10.40;16.58)
Estimates and Projection based on TB notification data in Malaysia from 1990‐2014
Inftotal Case Notifications
30000
35000
25000
30000
20000
15000
5000
10000
0
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Using predicted data between 1990‐2010 and notification data between 1990‐2014, the annual mean difference or underrepresentation is 13.49% (95%CI:
10 39 15 84)
1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
TB Underrepresentation in Malaysia, 1990‐2014
30
20
25
ntation%
15
rrerep
resen
5
10
TB und
er
0
990
991
992
993
994
995
996
997
998
999
000
001
002
003
004
005
006
007
008
009
010
011
012
013
014
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
Year
Di ib i f l diff d i % b 1990 2014Distribution of annual mean difference or underrepresentation % between 1990‐2014. The mean difference or underrepresentation range is between 13.11%‐13.49%
Contents
1. Introduction
2. Methods
3 Results3. Results
4 Discussion4. Discussion
27
• Underrepresentation is common
• Quantification provides evidence
• Knowledge for• Knowledge for– Evidence‐based planning and policy– Targeted/selective programme– Problem solvingg
Thank you for your attentionOther research supporting members:Dr Mohamed Paid Yusof, Disease Control Division, MOHDr Fuad Hashim, Institute of Public Health, MOHDr Fuad Hashim, Institute of Public Health, MOHDr Mariam bt Mohamad, UiTM Selayang CampusDr Badrul Hisham Abd Samad, Johor State Health Dept, MOHDr Mokhtar bin Pungut, Segamat District Health Dept, MOHg , g p ,Dr Mohd. Yusof bin Hashim, Seberang Prai District Health DeptDato’ Dr Haji Abdul Razak Muttalif, Inst. of Resp MedicineDatuk Dr Hjh Aziah bt Ahmad Mahayiddin, Inst. Resp MedicineAssoc. Prof. Dr Pang Yong Kek, UMMCDr Karina Razali, HART Consultancy
Acknowledgement:Director General of Health, Ministry of HealthDisease Control Division Ministry of HealthDisease Control Division, Ministry of HealthInstitute of Respiratory Medicine, Ministry of Health
The Tuberculosis Transmission Dynamic Model for Malaysia
S R٨ µ
δ2kxS Rµ
Lλ rp
θ LE αdpµ
θ
Id
(1 ‐ α)kt
LLdn
δ1kxµ
ϕ
µ + µt
• Framework (scope, epidemiology, heterogeneity)• Data collection and analysis of parameters (year 1990-2010)
Model development
• Model baseline output• Model validation (optimization, fitting & comparability)
• Model used as a tool• Estimate and projection of tuberculosis burden in Malaysia 1990-2030
Model application
Estimate and projection of tuberculosis burden in Malaysia 1990 2030• Effectiveness of potential intervention strategies• Quantification of effectiveness of IPT (coverage) as a combined strategy• Selection of LTBI for optimal coverage• Overall potential impact of IPT combined strategy
Formulation of policy
• Translation of model outputs to ‘reality’• Recommendation of evidence for implementation• Integration into current management protocol & health system• Return on investment (ROI)policy
recommend-ation
• Informed decision making
31
TB SituationTB SituationTB Burden Ranking TB among HCW
Disease
Number of casesIncidence rate (IR)
2003 2004 2005 2006 2007 2007Dengue Infection
14,76161 65
12.75551 50
15,86264 53
17,14768 47
23,31090 65
46,517177 92Infection 61.65 51.50 64.53 68.47 90.65 177.92
TB 15,58263.29
15,42960.30
15,99161.20
16,66562.56
16,91862.26
17,50663.10
FP 6,62426.45
5,95723.40
4,64117.76
6,93826.04
14,45553.19
17,32262.47
HFMD 1,265 378 6,325 5,141 12,558 15,564, , , 12,55846.21
15,56456.13
HIV 6,75631.27
6,42729.61
6,12028.09
5,83028.79
4,57721.01
4,57716.71
TB among Immigrant TB‐HIV Co‐infection
12
14
Peratus kes TB di kalangan wagra asing, 2003 ‐ 2008
TB among Immigrant TB‐HIV Co‐infection
4
6
8
10
%
32
0
2
2003 2004 2005 2006 2007 2008
TB SituationTB SituationTB in Malaysia vs ASEAN Country TB Cases and IR 1985‐2008
Disease
Number of casesIncidence rate (IR)
2003 2004 2005 2006 2007 2007Dengue Infection
14,76161 65
12.75551 50
15,86264 53
17,14768 47
23,31090 65
46,517177 92
Country No. of cases
IR EstimatedNo. of cases
EstimatedIR
%case
detectionBrunei 207 53 230 59 90
S 64
66
68
70
14000160001800020000
0s
TB Cases and Incidence Rate 1985-2008
Infection 61.65 51.50 64.53 68.47 90.65 177.92TB 15,582
63.2915,42960.30
15,99161.20
16,66562.56
16,91862.26
17,50663.10
FP 6,62426.45
5,95723.40
4,64117.76
6,93826.04
14,45553.19
17,32262.47
HFMD 1,265 378 6,325 5,141 12,558 15,564
Singapore 1359 31 1176 27 115.5
Malaysia 16129 61 27439 103 58.8
Thailand 54793 86 90878 142 60.3 52
54
56
58
60
62
64
02000400060008000
1000012000
IR/1
00,0
00
No.
of c
ases
,46.21
,56.13
HIV 6,75631.27
6,42729.61
6,12028.09
5,83028.79
4,57721.01
4,57716.71
TB among Children Death Associated with TB
Indonesia 275193 119 528063 228 52.1 Year
TB among Children Death Associated with TB
45
ge 3456
r 100
,000
la
tion
0123
2000 2001 2002 2003 2004 2005 2006 2007 2008
Perc
enta
g
012
2000 2001 2002 2003 2004 2005 2006 2007 2008
Year
Rat
e pe
rpo
pu
Year Death Rate (per 100,000 population) Target: <3 per 100,000 population
TBHIV in TB : 1990‐2012
12,025000
8,0
10,020000
6,0
10000
15000
% T
BH
IV in
TB
No
of C
ases
2,0
4,0
5000
%
0,00
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
YearNo.TB cases Number Of New TB Cases With HIV Positive %TBHIV inTB
The optimized and best-fitted model for predicting tuberculosis in Malaysia.
35
The optimization statistics derived using Marquardt method yield r2 = 0.9315, p<0.0001.