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A System Dynamics Decision Support System for tuberculosis control prioritysetting.

Reda M LebcirThe Business School, University of Hertfordshire,College Lane, Hatfield, UKTel: ++.44(0)1707 285504Fax:++.44(0)1707 285554Email:[email protected]

Abstract

The aim of this study is to describe the application of a decision support system based ona system dynamic computer simulation modelling to an important global public healthproblem. The system determines the impact of tuberculosis control programmes withdifferent coverage and cure rates on the epidemiology of tuberculosis and death rates.The findings indicate that programmes that effectively manage drug-sensitive or multiple-

drug-resistant tuberculosis (MDRTB) alone have less impact on death rates thanprogrammes which combine both but with high coverage levels allied with high cure ratesfor drug-sensitive tuberculosis (DSTB). The study, implemented by a multidisciplinaryteam, addresses two key policy questions that are important for resource poor settingswhich need to prioritize between interventions: first, whether to invest in MDRTB controland second, how different levels of cure rates for DSTB and MDRTB affect the epidemicand death rates.

Key words: System Dynamics, Decision Support Systems, Tuberculosis, HIV, Russia,Priority Setting
mailto:[email protected]:[email protected]:[email protected]


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Introduction

In this paper, we describe the application of system dynamics to an important globalpublic health problem, namely, the control of tuberculosis (TB) in the Russian Federation.We use Samara region, which is located in the south west of the Russian Federation andepidemiologically broadly typical of other Russian regions for TB as an illustrative case[Floyd et al, 2006].

The rise in the rates of TB and MDRTB pose a societal challenge to the RussianFederation, post-Soviet countries and to the European Union (EU): eight of the ten newMember States are former communist countries from central and eastern Europe (withrates of TB and MDRTB higher than their western neighbours) and given that theexpansion of the EU has shifted its borders eastward to abut Ukraine and Belarus, andgreatly lengthen its existing border with Russia [Coker et al, 2004].

Since 1995, with support from international and bilateral agencies, demonstration

projects implementing the WHO-approved TB control strategy, DOTS, have beeninitiated by the Russian Government, with the hope that the expansion of this model ofcontrol would halt the rise in the incidence of TB. However, to date, despite evidence ofgood clinical outcomes achieved in demonstration projects, expansion of this goodpractice in Russia has been limited. Although new regulations have recently beenadopted to support implementation of standardised international practices in TBtreatment [Ministry of Health, 2003], by 2003 access to DOTS was limited to 27% of thepopulation of Russia, compared with an average of 61% for the 22 high-burdencountries. TB case detection rates under DOTS in Russia remain low, at 6% in 2002,against a WHO case detection target of 70% [Atun et al, 2005a; World HealthOrganization, 2004]

The main goal of our research project was to develop a computer simulation basedDecision Support System (DSS) to inform policy makers with regard to policies to controlthe spread of TB in the Samara region in the Russian Federation, where in recent yearsTB had became a serious public health threat due to the alarming increase in the numberof new cases but also because of a high number of individuals developing multi-drugresistant tuberculosis (MDRTB)a strain of TB which cannot be cured by the standardfirst line drugs and which requires lengthy period of treatment with a considerably morecostly regimen of drugs.

In this paper, we describe the System Dynamics simulation model, which simulate theinfection and transmission of TB including emergence and spread of MDRTB. Thismodel addresses the following research questions: (i) What are the driving forces for the

epidemics of TB and MDRTB?; (ii) How many deaths could be averted by improving thetreatment outcome of TB including MDRTB?

Choice of modelling approach

We selected System Dynamics (SD) [Morecroft, 2007;Sterman, 2000] to model thetransmission dynamics of TB and to quantify the implications of some proposed policieson epidemiological trajectories and health consequences, in terms of the number offuture deaths. SD is an appropriate model for our task as it enables modelling complexsystems and focuses on patient states rather than modelling patients at the individuallevel. In SD, the population can be divided into large homogenous groups, in which allthe patients are in the same disease state, rather than modelling the flow of eachindividual patient within the population [Brailsford et al, 2004].


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The SD process includes two phases: (i) The first phase is qualitative in which thesystems elements are determined and possible cause-effect links are mapped in theform of interconnected feedback loops, and (ii) the second phase, which involves thetranslation of the qualitative structure into a quantitative simulation model, in which thedifferent stocks and flows are identified and relationships among them formallyquantified. The simulation model can be then used for what-if scenarios to investigatepossible outcomes of different policy interventions [Morecroft, 2007; Sterman 2000]

The SD simulation model built in this research focused on understanding the processesof infection, progress to disease, treatment of drug sensitive TB), and emergence andtreatment of MDRTB and how these processes interact to drive the transmissiondynamics of TB in the population, but also in elucidating the impact of possible policieson the TB epidemic.

Model Building

The model building process was divided into two stages. The first stage consisted on

building a model to represent tuberculosis transmission, including both the DSTB andMDRTB strains of the disease. The second stage of model development consisted onbuilding a model to represent the transmission mechanisms of HIV/AIDS. This modelfocused on the interaction between HIV/AIDS and tuberculosis and, in particular, on therole of HIV/AIDS in the processes of tuberculosis infection, transmission, and spread inthe population.

This model includes two main sub-models: the DSTB sub-model and the MDRTB sub-model. The DSTB sub-model describes the processes of DSTB infection, progression todisease, and clinical outcomes. The MDRTB sub-model describes the processes ofMDRTB infection, progression to disease, and clinical outcomes.

The simulation model was built using software called IThink (also known as Stella)The software is user friendly and allows the user to draw the different elements of thesimulation model (such as stocks and flows) on a computer interface. The model canalso be divided into sub-models (known as sectors in IThink) so that the modelcomplexity is reducedmaking it easier to understand its structure.

In the simulation model, different states of TB were represented by stocks and included,for example, Susceptible, Latently infected, Disease, Persistent, Cured andDeath. The number of individuals moving between these stocks per unit time isrepresented by flows. Flows include, for example, infection rate, breakdown to diseaserate, treatment rate, cure rate, and death rate.

The model was divided into five sectors: the first sector represents the natural history ofDSTB; the second represents the detection and treatment of DSTB; the third and fourthsectors represent the natural history and the detection and treatment of MDRTBrespectively; and the fifth sector represents the reinforcing process of TB infection. Onceindividuals with the DSTB disease are detected, they are admitted to a first treatmentphase from which they can move to the following stocks (states): Cured, Death,Persistent and MDRTB. A fraction of the individuals who become persistent aredetected again and enter a re-treatment phase from which they can move to one of thestocks of Cured, Death, or Persistent. The sector captures changes in the size of theinfectious population (individuals who can transmit the infection to susceptibleindividuals) as the epidemic progresses over time.

The model was calibrated using routine data, primary data collected from Samara region,and data from published literature. The data fed into the model included three categories:


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(i) first, data related to TB epidemiology, such as the average breakdown time frominfection to disease (incubation period); (ii) second, data related to the effectiveness oftreatment procedures in the region, such as the fraction of individuals cured; (iii) third,prevalence data for the model stocks at the beginning of the simulation period, such asthe number of individuals latently infected at the start of the simulation period. The modelis described in great details elsewhere [Atun et al, 2007a; Lebcir et al, In Press].

Model validation

The aim of model validation in SD is to build confidence in the model, such that it can beused to inform policy and decision making. System Dynamics model validation includestwo main phases (Barlas, 1996).. First, the validation of the qualitative model focusingon the set of variables included in the model and their relationships. Second, thevalidation of the quantitative simulation model focusing on the stock and flows diagrams,the equations, and the model output. We carried out both validation phases to test ourmodel. The qualitative model was built in association with the local clinicians and policymakers. The variables included in the model were drawn from the epidemiology

literature and from extensive interviews of key informants in Samara. The qualitativemodel was iteratively refined until key clinicians and policy makers involved in TB controlwere in agreement. The quantitative model was validated through comparison of themodel output and the observations from the real world. Given that public healthmanagers in the region are very concerned about the deaths from TB, we compared thebehaviour over time of the number of deaths observed in the region for the period 1999-2002 to the model output. The model replicated, with a high level of accuracy, real worldobservations [Lebcir et al, In Press]..

Scenario testing

The scenarios tested in the model were selected to respond to the concerns of public

health managers and policy makers in the region: namely, the impact of policies relatedto detection and treatment of TB on the number of death from TB and MDRTB and fromHIV associated TB and HIV associated MDRTB deaths. These scenarios would involvepolicies which would require substantial re-deployment, hence simulations were useful totest the potential impact of these policies, before any decisions were made.

The model was simulated over a period of 10 years. The scenarios tested relate to theeffectiveness of TB treatment and the fraction of the population with the disease.Scenarios are represented by the following three factors:

DSTB cure rate: Three values are tested for the following cure rates: 70%(representing current situation), 80%, and 90% (representing improved situation.

MDRTB cure rate: Two values are tested for 5% (representing cure rates from

programmes without access to second line anti-TB drugs) and 80% cure rate(representing the potential that might be achievable with a well-resourced andwell-organised MDRTB control programme with access to second-line drugs)[Dye et al, 2002; Tahaouglou et al, 2001]

Fraction of the population with TB which are detected and included in treatmentprogrammes: Four values are tested for the following fractions: 50% (WorseSituation), 70% (Current situation), and 90% (Improved Situation).

Given that the aim of this study is to evaluate the impact of TB control policies onpreventing deaths, the scenarios are evaluated with respect to four outcome indicators:cumulative number of TB deaths, cumulative number TB associated HIV deraths,cumulative number of MDRTB deaths, and cumulative number of HIV associatedMDRTB deaths.Results


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The simulation results were analysed to evaluate how changes in the level of a singlefactor impact on the cumulative number of deaths. The effect of each factor is presentedin the following:

1/:DSTB cure rate: The simulation results show that DSTB cure rate has a positiveimpact on TB control, in terms of reducing the cumulative number of TB deaths, andcumulative HIV associated TB deaths. However, the scale of improvement is different forthe two indicators. The cumulative number of TB deaths is reduced by 22% from 7,668deaths, for a 70% DSTB cure rate, to 5,958 deaths for a 90% DSTB cure rate. Thereduction in the cumulative number of HIV associated TB deaths is not that sizeable as itwill decrease from 4414 deaths under 70% DSTB cure rate to 3931 desths under 90%DSTB cure ratecorresponding to a reduction of around 10% (See Figure 1).

2/ MDRTB cure rate: An increase in MDRTB cure rate is expected to prevent deaths fromTB and MDRTB. An increase of MDRTB cure rate will reduce the cumulative deaths fromTB from 6,313 deaths for 5% MDRTB cure rate to 4,474 deaths for 80% MDRTB cure

rate, a reduction of around 30%. The results are substantially different in terms ofcumulative MDRTB deaths. At the 5% MDRTB cure rate level, the cumulative number ofMDRTB deaths is estimated to reach 1973 deaths. However, at 80% MDRTB cure rate,the cumulative number of MDRTB deaths is reduced to 134 deaths, that is a 93%reduction compared to cure rates at the 5% level (See Figure 2).

0

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6000

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70% 80% 90%

DSTB Cure Rate

Deat

hs

TB Deaths TB Associated HIV Deaths

Figure 1: Tuberculosis and HIV associated tuberculosis deaths for different levels ofDSTB cure rate

3/ The fraction of the population with TB to be treated: This factor has a significant effecton transmission dynamics of TB. The model supported the concern of policy makers inthe region regarding the importance of identifying individuals with the disease andproviding them with appropriate treatment to reduce the spread of disease within thepopulation. The simulation results indicate that increasing the fraction of cases detectedreduces quite considerably the cumulative number of TB deaths as shown in Figure 3.The cumulative number of TB deaths could be reduced from 7,538 deaths for a 50%detection fraction to 5,562 for a 90% detection fraction, a reduction of approximately

28%. However, as far as cumulative MDRTB deaths are concerned, the impact is less


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pronounced. Cumulative MDRTB deaths are anticipated to fall by less than 1%, from1,967 to 1,960, for 50% and 90% detection fraction respectively..

0

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MDRTB Cure Rate

Deaths

TB Deaths MDRTB Deaths

Figure 2: Tuberculosis and MDRTB deaths for different levels of MDRTB cure rate

0

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4000

6000

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50% 70% 90%

Figure 3: Tuberculosis and HIV associated tuberculosis deaths for different levels oftuberculosis detection fraction

Discussion

We show that SD computer simulation modelling can provide useful insights for policymakers in a complex public health environment where competing priorities arechallenging decision-making. In a post-Soviet setting, where traditional public healthpractices remain entrenched, expanding international methods of TB control based onDOTS are likely to improve tuberculosis control and result in public health benefits.However, an expanded DOTS programme will need to be harnessed to an expansion of


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References-R.A.Atun, J.Baeza ,F.Drobniewski, V.Levicheva, R.J.Coker, Implementing WHO DOTSstrategy in the Russian Federation: stakeholder attitudes, Health Policy vol, 74 pp 122-132, 2005a.

-R.A. Atun, R.M.Lebcir,F. Drobniewski, R.J.Coker RJ, Impact of an effective multidrug

resistant tuberculosis control programmes in the setting of an immature HIV epidemic:system dynamics simulation model, Intl J STD & AIDS, vol 16 pp 560-570, 2005b-R.A Atun, R.M.Lebcir, M.McKee, J.Habicht,R.J.Coker, Impact of joined-up HIV harmreduction and multidrug resistant tuberculosis control programmes in Estonia: Systemdynamic simulation model, Health Policy, vol 81 pp 207-217, 2007a.-R.A.Atun, R.M.Lebcir, F.Drobniewski, M.McKee, R.J.Coker, High coverage with HAARTis required to substantially reduce the number of deaths from tuberculosis: systemdynamics simulation, Intl J STD & AIDS,vol 18 pp 267-273, 2007b.-Y.Balabanova, M.Ruddy, J.Hubb, M.Yates, N.Malomanova, I.Fedorin, F.Drobniewski,Multidrug Resistant tuberculosis in Russia. Clinical Characteristics, analysis of secondline drug resistance and development of standardized therapy, Eur J Clin Micr Infct Dis,vol 24, pp 136-139, 2005.

-Y.Barlas, Formal aspects of model validity and validation in system dynamics, Sys DynRev, vol 12, pp 183-210, 1996.-S.C.Brailsford, V.A.Lattimer, P.Tamaras, J.C.Turnbull JC, Emergency and on demandhealth care: modelling a large complex system, J Ope Res Soc, vol 55, pp 34-42, 2004.-R.J.Coke, Should TB programmes invest in second-line treatments for multidrugresistant tuberculosis (MDR-TB)?.International, J Tub Lung Dis, vol 6, pp: 649-650,2002.-R.J.Coker, R.A.Atun, M.McKee, Health care system frailties and public health control ofcommunicable disease on the European Unions new eastern border, Lancet, vol 363,pp1389-1392, 2004.-F.Drobniewski, R.A.Atun, I.Fedorin, A.Bikov, R.J.Coker, The bear trap: the collidingepidemics of multi-drug resistant tuberculosis and HIV in Russia, Intl J STD & AIDS, vol

15, pp 641-646, 2004.-C.Dye, B.G.Williams, M.A.Espinal, M.C.Raviglione, Erasing the world slow strain;strategies to beat multi-drug resistant tuberculosis, Science, vol 295, pp 2042-2046,2002..-F.K.Floyd, R.Hutubessy, Y.Samyshkin, A.Korobitsyn, I.Fedorin, G.B.Volchenkov, B.Kazeonny, R.J.Coker, F.Drobniewski, W.Jakubowiak, M.Shilova, R.A.Atun,Healthsystems efficiency in the Russian Federation: the case of tuberculosis control, BultnWHO, vol 84, pp 43-51, 2006.-R.M.Lebcir, R.A.Atun, R.J.Coker, System dynamics simulation of treatment policies toaddress colliding epidemics of tuberculosis, drug resistant tuberculosis and injecting drugusers driven HIV in Russia,J Ope Res Soc, In Press.-Ministry of Health, On improving tuberculosis control in the Russian Federation. PrikazNo. 109. Moscow, Ministry of Health of the Russian Federation :Moscow, 2003.-J.Morecroft, Strategic Modelling and Business Dynamics,, Wiley, Chichester, UK,2007.-J.D.Sterman, Business dynamics. Systems thinking and modeling for a complex world,Mc-Graw Hill/Irwine: Singapore, 2000.-K.Tahaoglou, T.Torun, T. Sevim, T, The treatment of multidrug-resistant tuberculosisin Turkey, N Engl J Med, vol 345, pp 170-174, 2001.-World Health Organization., Global Tuberculosis Control: Surveillance, Planning,Financin, World Health Organization, Geneva, 2004.

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