Quantifying Tuberculosis Burden and Underrepresentation. Nurhuda Ismail, Awang M Bulgiba, Sanjay Rampal,

Transcription

1 Quantifying Tuberculosis Burden and Underrepresentation in Malaysia, Nurhuda Ismail, Awang M Bulgiba, Sanjay Rampal, Nicolaas JD Nagelkerke, Jiloris F. Dony, Omar Awang October 015

2 Contents 1. Introduction. Methods 3. Results 4. Discussion

3 Question: Does TB incidence data in Malaysiareflect Does TB incidence data in Malaysia reflect true burden?

4 Prevalence, incidence, and mortality rates per population in countries and areas with an intermediate burden of TB in the Western Pacific Region 006 Source: WHO Tuberculosis Control WPR Report, 008 4

5 Incidence Rate and Mortality Rate of ( ) Communicable Diseases per Population in Malaysia, ,665 new TB cases (mean ~ 46 cases /day) 1460 TB death (mean ~ 4 death/day) Source: Ministry of Health, 008 COMMUNICABLE DISEASES INCIDENCE RATE MORTALITY RATE AIDS HIV (all forms) Chancroid Cholera Dengue fever Dengue Haemorrhagic fever Diphteria Dysentry Food Poisoning Gonoccocal Infection Leprosy Malaria Measles Plague Polio Myelitis Rabies Relapsing Fever Syphillis Tetanus Neonatorum Tetanus (Adult) Tuberculosis Typhoid and Paratyphoid Typhus Viral Enchepalitis Viral Hepatitis Whooping cough Yellow fever Hand Foot and Mouth Ebola

6 Top 5 Communicable Disease, Malaysia ( ) Disease Incidence Mortality Incidence Mortality Incidence Mortality Rate Rate Rate Rate Rate Rate Dengue Fever Tuberculosis Hand, Food & Mouth Diseases Food Poisoning Malaria Source: Ministry of Health, 014 Note: *Incidence Rate was per 100,000 population except for Malaria per 10,000 population *Mortality Rate was per 100, population except for Malaria per 10, population 6

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8 BCG vaccination coverage: 98% DOTS coverage: 98% XDR/MDRTB cases: Success treatment rate: 85% 100,000 po opulation Rate per Year Incidence rate of TB in Malaysia Source: TBIS, Ministry of Health, 015

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10 TB Projection in Malaysia Nu umber of TB case es Projection of TB cases Retrospective projection of TB cases Time/year Source: Ismail N et al. 01

11 Contents 1. Introduction. Methods 3. Results 4. Discussion 11

12 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 ) International Union Against Tuberculosis & Lung Disease (IUATLD) WHO and World Bank Model system: deterministic, compartmental, ta SEIR Numerical analysis system: ODE, 4 th order Runge Kutta (RK4) iterative method Modelling Software: ModelMaker 4, Berkeley Madonna 1

13 Refinement of Model Homogenous model Age-structured model Gender-structured model 13

14 Contents 1. Introduction. Methods 3. Results 4. Discussion 14

15 Transmission of TB and Interventions Non-infected Persons Early y diagnosis Current treatment regimes Infectious TB Cases Infected Persons (LTBI) High Risk Infected Persons Identify with TST/IGRA Intervention (IPT)

16 The Tuberculosis Transmission Dynamic Model for Malaysia ٨ µ µ S δ k x R λ p r µ L E αd p (1 α)k t I µ L L δ 1 k x d n µ + µ t

17 DIFFERENCE EQUATIONS DIFFERENTIAL EQUATIONS S t+1 = ٨ t N+ (1 - βi t - µ t )S t LE t+1 = βi t S t + δ kx t R t + (1 - µ t - αdp t kt t + αkt t )LE t LL t+1 = (1 α)kt t LE t + (1 - δ 1 kx t - µ t - dn t )LL t I t+1 = αdp t LE t + δ 1 kx t LL t + dn t LL t + p t R t + (1 - µtb t - µ t - r t )I t R t+1 = r t I t + (1 - p t - µ t - δ kx t )R t

18 Various states of population in tuberculosis transmission dynamic of the model State Descriptions Assumptions S L E L L Number of susceptible individuals Number of early latent tuberculosis infected individuals Number of late 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 successful treatment (recurrent exposure) History of contacts more than 5 years of exposure 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

19 List of parameters used in the model (1) Parameter Descriptions & Assumptions Value/Unit Reference ٨ Recruitment rate. Equates only birth rate. BCG immunization does not play any role in this type of tuberculosis transmission dynamic µ Mortality rate due to other causes. The annual crude death rate minus µ t 0.03/year 0.015/year Malaysian Department of Statistic 009; Malaysian MOH, 009 Malaysian Department of Statistic 009; Malaysian MOH, 009 µ t Mortality rate due to infectious tuberculosis. 0.1/year Malaysian MOH, 009 Takes into account the national TB death notification rate and does not include those with successful treatment λ Force of infection, taking into account frequency dependent mechanism or mass action with homogenous mixing: λ = Probability of effective tuberculosis transmission derivatives according to age and gender 0.35 Dye et al., 1998 c Per capita contact rate 10/year Malaysian MOH, 009 α Probability of those who develop primary infection within 5 years of exposure 0.07 Dye et al., 1998 k t Rate of progression of those who have been infected yet to develop the disease into late latent group 0./year Dye et al., 1998 k x Rate of exogenous reinfection. Exogenous reinfection rate k x defined as k x =aλ where a is the co-efficient of exogenous reinfection and similar infection and reinfection rate, a = 1 derivatives according to age and gender 19

20 List of parameters used in the model () Parameter Descriptions & Assumptions Value/Unit Rf Reference δ 1 k x Rate of exogenous reinfection where individuals at the state of late latent infection where δ 1 is the coefficient of the first pathway of exogenous reinfection and δ 1 k x = δ k x 0.75/year Styblo K., 1986; Comstock G.W., 198; Verver S., 005 δ k x d p d n r p Rate of recurrent infection of tuberculosis following successful treatment (recurrent exposure). where δ is the coefficient of the second pathway of exogenous reinfection and δ 1k x = δ k x Rate of developing infectious tuberculosis from early latent state (primary infection) Rate of developing infectious tuberculosis from late latent group via reactivation (endogenous route) Rate of effective treatment of those with infectious tuberculosis Rate of relapse for tuberculosis. Relapse back kto infectious state t regardless of bacteriological status 0.75/year Styblo K., 1986; Comstock G.W., 198; Verver S., /year Vynnycky & Fine, /year Vynnycky & Fine, /year Malaysian MOH, /year Malaysian MOH, 007 Θ Rate of effective IPT for early LTBI. Individuals in the early latent (L E ) who do not progress into infectious state due to effective treatment of Isoniazid preventive therapy 0.03/year Model output π Rate of effective IPT for late LTBI. Individuals in late latent (L L ) who do not progress into infectious state due to effective treatment of Isoniazid preventive therapy 0.007/year Model output 0

21 The optimized and best-fitted model showing predicted tuberculosis cases against observed tuberculosis cases in Malaysia from 1990 till 010 indicated as blue line (Inftotal) and red dots (Case Notifications) respectively. The lower and upper border projections of the model are also shown as pink dot-dash line (minimum) and green dash line (maximum) respectively. The optimization statistics derived using Marquardt method yield r = , p<

22 TB Projection in Malaysia Nu umber of TB case es Projection of TB cases Retrospective projection of TB cases Time/year Source: Ismail N et al. 01

23 TB Projection in Malaysia Nu umber of TB case es Observed number of TB cases Projection of TB cases Retrospective projection of TB cases Time/year Source: Ismail N et al. 01

24 Estimates and Projection based on TB notification data in Malaysia from Inftotal Case Notifications ases Num mber of TB c Year Using data between , the annual mean difference or underrepresentation is 13.11% (95%CI: 10.40;16.58)

25 Estimates and Projection based on TB notification data in Malaysia from Inftotal Case Notifications Using predicted data between and notification data between , the annual mean difference or underrepresentation is 13.49% (95%CI: )

26 TB Underrepresentation in Malaysia, TB underrerepresentation % Year Distribution ib i of annual mean difference or underrepresentation % between The mean difference or underrepresentation range is between 13.11% 13.49%

27 Contents 1. Introduction. Methods 3. Results 4. Discussion 7

28 Underrepresentation is common Quantification provides evidence Knowledge for Evidence based planning and policy Targeted/selective programme Problem solving

29 Thank you for your attention Other research supporting members: Dr Mohamed Paid Yusof, Disease Control Division, MOH Dr Fuad Hashim, Institute of Public Health, MOH Dr Mariam bt Mohamad, UiTM Selayang Campus Dr Badrul Hisham Abd Samad, Johor State Health Dept, MOH Dr Mokhtar bin Pungut, Segamat District Health Dept, MOH Dr Mohd. Yusof bin Hashim, Seberang Prai District Health Dept Dato Dr Haji Abdul Razak Muttalif, Inst. of Resp Medicine Datuk Dr Hjh Aziah bt Ahmad Mahayiddin, Inst. Resp Medicine Assoc. Prof. Dr Pang Yong Kek, UMMC Dr Karina Razali, HART Consultancy Acknowledgement: Director General of Health, Ministry of Health Disease Control Division, Ministry of Health Institute of Respiratory Medicine, Ministry of Health

30 The Tuberculosis Transmission Dynamic Model for Malaysia ٨ µ µ S δ k x R λ p r L θ L αd E p µ (1 α)k t I ϕ µ L L δ 1 k x d n µ + µ t

31 Model development Framework (scope, epidemiology, heterogeneity) Data collection and analysis of parameters (year ) Model baseline output Model validation (optimization, fitting & comparability) Model application Model used as a tool Estimate and projection of tuberculosis burden in Malaysia 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 recommendation Translation of model outputs to reality Recommendation of evidence for implementation Integration into current management protocol & health system Return on investment (ROI) Informed decision making 31

32 TB Situation TB Burden Ranking TB among HCW Diseas e Number of cases Incidence rate (IR) Dengue Infection 14, , , , , TB 15, , , , , , FP 6, , , , , , HFMD 1, ,35 5,141 1,558 15, HIV 6, , , , , , TB among Immigrant TB HIV Co infection 14 Peratus kes TB di kalangan wagra asing, %

33 TB Situation TB in Malaysia vs ASEAN Country TB Cases and IR Country No. of IR Estimated Estimated % TB Cases and Incidence Rate Diseas Number of cases cases No. of IR case e Incidence rate (IR) cases detection Brunei Dengue 66 14, ,86 17,147 3,310 46, Singapore Infection TB 15,58 15,49 15,991 16,665 16,918 17, Malaysia FP 6,64 5,957 4,641 6,938 14,455 17, Thailand HFMD 1, ,35 5, ,558 15, Indonesia HIV ,756 6,47 6,10 5,830 4,577 4,577 Year s No.of cases 0 IR/100,000 TB among Children Death AssociatedwithTB Percentag ge Year Year Death Rate (per 100,000 population) Target: <3 per 100,000 population r 100,000 la tio n Rate per popu

34 TBHIV in TB : ,0 10,0 8,0 % TBHIV in TB ,0 4,0,0 0,0 No of Cases No.TB cases Number Of New TB Cases With HIV Positive %TBHIV intb Year

35 The optimized and best-fitted model for predicting tuberculosis in Malaysia. The optimization statistics derived using Marquardt method yield r = , p<