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Interpreting measures of tuberculosis transmission: a case study on the Portuguese population
- Joao Sollari Lopes^{1}Email author,
- Paula Rodrigues^{2},
- Suani TR Pinho^{3},
- Roberto FS Andrade^{3},
- Raquel Duarte^{4, 5, 6} and
- M Gabriela M Gomes^{1}
https://doi.org/10.1186/1471-2334-14-340
© Lopes et al.; licensee BioMed Central Ltd. 2014
Received: 9 December 2013
Accepted: 9 June 2014
Published: 18 June 2014
Abstract
Background
Tuberculosis remains a high burden for Human society despite considerable investments in its control. Unique features in the history of infection and transmission dynamics of tuberculosis pose serious limitations on the direct interpretation of surveillance data and call for models that incorporate latent processes and simulate specific interventions.
Methods
A transmission model was adjusted to the dataset of active tuberculosis cases reported in Portugal between 2002 and 2009. We estimated key transmission parameters from the data (i.e. time to diagnosis, treatment length, default proportion, proportion of pulmonary TB cases). Using the adjusted model to the Portuguese case, we estimated the total burden of tuberculosis in Portugal. We further performed sensitivity analysis to heterogeneities in susceptibility to infection and exposure intensity.
Results
We calculated a mean time to diagnose of 2.81 months and treatment length of 8.80 months in Portugal. The proportion defaulting treatment was calculated as 0.04 and the proportion of pulmonary cases as 0.75. Using these values, we estimated a TB burden of 1.6 million infected persons, corresponding to more than 15% of the Portuguese population. We further described the sensitivity of these estimates to heterogeneity.
Conclusions
We showed that the model reproduces well the observed dynamics of the Portuguese data, thus demonstrating its adequacy for devising control strategies for TB and predicting the effects of interventions.
Keywords
Heterogeneity Sojourn times Transmission dynamics Tuberculosis epidemiologyBackground
Twenty years after considered to be a major global public health problem, tuberculosis (TB) remains a high burden for human society. In fact, WHO places TB as the second leading cause of death from an infectious disease, with almost two million deaths per year [1]. Moreover, according to this organization, one third of the world’s population is infected with TB and nearly 10 million people progress to an active state of the disease every year. Some aspects of TB transmission are slower than others in responding to changes in biologic, demographic, and socioeconomic conditions, resulting in a superposition of time scales that poses major challenges to the interpretation of transmission measures. A fraction of infected people progresses to active disease within two years of infection, while others may maintain a latent infection for decades with a reduced risk of disease progression. Complicating things further, those who have had the disease and have been successfully cured also maintain a risk of recurrent disease [2]. Furthermore, host susceptibility and rates of activation and reactivation of the disease appear to be related to co-morbidities (e.g. malnutrition, alcoholism, drug-addiction, diabetes, or HIV infection and other immunosuppressive diseases), high-risk of exposure to infection (e.g. health workers, prisoners), ageing and socioeconomic factors [3, 4]. A challenge for mathematical modeling is to capture those aspects that are essential to the questions being addressed while maintaining a stylized structure that enables interpretation without compromising accuracy of the conclusions [5–8]. In this respect, the selection of the model is intimately connected to the posed question [7], but also to specificities of the settings being studied.
In this work we applied a mathematical model of TB transmission to Portuguese surveillance data. Recorded data on TB incidence in Portugal from ECDC [9] show a consistent decrease from 1999 to 2011 of 4.34% per year, resulting in a total decline in TB incidence of 52.13%. Nevertheless, Portugal registers the highest incidence among Western European countries (over 20/100,000, while a great majority is below 10/100,000), which reflects a considerable rate of TB transmission. Recent immigrants comprise only about 3% of overall yearly cases, and the 50% most common ages of TB patients vary vastly from 25 to 65 years old (note also that TB cases in children with less than 15 years old comprises an uncommonly significant proportion of almost 3%) [10]. Other common risk factors have some weight on the overall yearly TB cases in Portugal, for example being a prison inmate (about 2%), HIV-positive (about 14%) or drug-user (about 15%) [10]. Although not modeled explicitly, these factors are generally contemplated by our analysis of sensitivity to risk heterogeneity.
Initially, we applied a mathematical model for TB transmission under previously specified homogeneity assumptions [11]. For the analysis, we used data records from 2002 to 2009 provided by National Directorate of Health (Additional file 1). Using these data, we estimated parameter values of the model (i.e., detection time, treatment length, proportion of unsuccessful treatment, and proportion of pulmonary TB cases). Subsequently, we assessed the fit of the model by contrasting dynamic trajectories described by the parameterized model with real dynamics described by the data. The model was used to calculate the total burden of TB in Portugal considering the different stages of infection (i.e., primary infection, active infection not yet diagnosed, latent infection, and active infection under treatment). We further consider the existence of differential susceptibility to infection by applying an extended heterogeneous model based on previous work [8].
Methods
Analysis of treatment length and outcome in Portugal from 2002 to 2009
Surveillance data on TB for the period of 2002–2009 was provided by the Portuguese National Directorate of Health. These data, collected by medical practitioners, consisted of date of first symptoms, date of diagnosis and treatment starting, date of treatment ending, clinical form (i.e. pulmonary or extra-pulmonary), and treatment outcome [i.e. treatment completed (with or without laboratory confirmation); treatment failure; treatment default (i.e. lost to follow up); death (from tuberculosis or other causes)]. These data had been previously anonymized and, thus, no ethical approval was needed for the analysis presented here. From the dates of first symptoms and treatment starting and ending, we calculated time to detection and treatment length for each reported TB case - while the latter calculation is clearly objective, the former can raise some questions since retrospective self-report of symptoms is only a rough approximation for infection time. The proportion of treatment incompleteness was calculated by pooling together cases of default and treatment failure. The time to detection (1/τ yrs), treatment length (1/δ _{ T } yrs), proportion of incomplete treatments (ϕ _{ T }) and proportion of pulmonary TB cases (ν) were estimated directly from the Portuguese dataset. To accommodate possible outliers from reporting/clerical errors or atypical cases, we discarded values of treatment length and detection time longer than 3 yrs. Following parameter estimations from raw data, cumulative data sets were created. Goodness-of-fit between the cumulative data and the theoretical expectation was assessed using two-sample Kolmogorov-Smirnov tests.
Basic TB transmission model
Parameters of tuberculosis transmission models
Symbol | Definition | Value |
---|---|---|
β | Transmission coefficient | variable (yrs^{−1}) |
ν | Proportion of pulmonary TB cases | 0.75 |
μ | Death and Birth rate | 1/80 yrs^{−1} |
δ | Rate at which individuals leave P compartment | 2 yrs^{−1} |
ϕ | Fraction of infected population developing active TB | 0.05 |
σ | Reinfection factor | 0.5 |
ω | Rate of endogenous reactivation | 0.0003 yrs^{−1} |
τ | Inverse of time to detection | 4.26 yrs^{−1} |
κ | Proportion of detected cases in a year | 0.87 |
δ _{ T } | Inverse of treatment length | 1.36 yrs^{−1} |
ϕ _{ T } | Fraction of treatment default and failure | 0.04 |
α ^{a} | Low-risk factor | 0.15 or variable |
γ ^{a} | Proportion of low-risk group | 0.98 or variable |
System (1) has two equilibria: a disease-free equilibrium E _{1} = (1,0,0,0,0) stable when R _{0} < 1; and an endemic equilibrium E _{2} = (S ^{*},P ^{*},I ^{*},L ^{*},T ^{*}), where all coordinates are nonzero, stable when R _{0} > 1. Details on the model and its equilibrium points are in Additional file 2.
TB model assuming heterogeneous infection risk
where λ _{ i } = α _{ i } βνI, i = {1,2} and α and γ can take values from the interval (0,1). For reference, [8] estimated α = 0.15 and γ = 0.98 using a world-wide dataset.
The basic reproduction number ${\mathit{R}}_{0}^{\text{'}}$ for system (3) is also expressed by equation (2). This can be derived by noting that in the heterogeneous model the average number of infections contributed by a single infectious individual is the sum ${\mathit{R}}_{0}^{\text{'}}={\mathit{\gamma}}_{1}{\mathit{\alpha}}_{1}{\mathit{R}}_{0}+{\mathit{\gamma}}_{2}{\mathit{\alpha}}_{2}{\mathit{R}}_{0}$, where the first and second terms account for transmission to the low- and high-risk groups, respectively. Considering that γ _{1} + γ _{2} = 1 and γ _{1} α _{1} + γ _{2} α _{2} = 1, and that γ _{1} ≡ γ and α _{1} ≡ α, we obtain ${\mathit{R}}_{0}^{\text{'}}=\mathit{\gamma \alpha}{\mathit{R}}_{0}+\left(1-\mathit{\alpha \gamma}\right){\mathit{R}}_{0}$, or simply ${\mathit{R}}_{0}^{\text{'}}={\mathit{R}}_{0}$. From now on the basic reproduction number is represented by R _{0} irrespective of whether we refer to the homogeneous or heterogeneous model.
System (3) has also two equilibria, a disease-free equilibrium ${\mathit{E}}_{1}^{\text{'}}$ = (γ,1 - γ,0,0,0,0,0,0,0,0) stable when R _{0} < 1, and an endemic equilibrium ${\mathit{E}}_{2}^{\text{'}}$ = (${\mathit{S}}_{1}^{*}$, ${\mathit{S}}_{2}^{*}$, ${\mathit{P}}_{1}^{*}$, ${\mathit{P}}_{2}^{*}$, ${\mathit{I}}_{1}^{*}$, ${\mathit{I}}_{2}^{*}$, ${\mathit{L}}_{1}^{*}$, ${\mathit{L}}_{2}^{*}$, ${\mathit{T}}_{1}^{*}$, ${\mathit{T}}_{2}^{*}$), whose coordinates are all nonzero for the considered range of parameter values, which is stable when R _{0} > 1.
Results
Parameter estimation from TB data in Portugal
where T _{0} = 17,477. Figure 2b shows a superposition of expression (5) with δ _{ T } = 1.36 yrs^{−1}, and the cumulative frequency of treatment length of the selected cases. These results also show some agreement between the observations and the exponential curve (D = 0.4595, p-value = 6.746 × 10^{−4}).
Outcome of tuberculosis treatment in Portugal, 2002–2009
Outcome | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 |
---|---|---|---|---|---|---|---|---|
Death | 126 | 126 | 145 | 142 | 129 | 130 | 122 | 100 |
Default | 110 | 93 | 92 | 98 | 102 | 61 | 65 | 46 |
Failure | 1 | 6 | 3 | 3 | 4 | 4 | 0 | 0 |
Completed | 2,109 | 2,074 | 2,162 | 2,091 | 2,146 | 2,045 | 1,971 | 1,198 |
Unassigned | 44 | 39 | 50 | 26 | 42 | 91 | 125 | 865 |
where T _{0} = 17,477. Figure 2c shows the superposition of expression (6), where δ _{ T } = 1.36 yrs^{−1} and ϕ _{ T } = 0.04, and the cumulative frequency of treatment incompleteness for the selected cases. Again, there is an impressive agreement between the observations and the theoretical curve, showing that sojourn times in treatment T are also approximately exponential (D = 0.3226, p-value = 0.079. Ties on observed data were discarded for a correct calculation of the p-value).
Clinical form under tuberculosis treatment in Portugal, 2002–2009
Clinical form | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 |
---|---|---|---|---|---|---|---|---|
Pulmonary | 1,748 | 1,717 | 1,844 | 1,752 | 1,850 | 1,749 | 1,745 | 1,643 |
Extra-pulmonary | 643 | 621 | 610 | 608 | 573 | 582 | 538 | 566 |
Epidemiology of TB in Portugal
ECDC recorded data show a consistent decline from 1999 to 2011 on TB incidence in Portugal [9], suggesting a non-steady state of TB transmission. Nevertheless, as an initial approximation, this study assumes equilibrium conditions for TB transmission, with an average number of reported TB cases of 2348.25 cases per year (Table 2). According to the Portuguese National Institute of Statistics, the population size of Portugal during this period was approximately 10.56 million people, resulting in a rate of 22.24 treatments initiated per 100,000 person-years.
Estimates of active disease prevalence and basic reproduction number for tuberculosis transmission models (1) and (3)
System^{a} | S | P (10^{−3}) | I (10^{−5}) | L | T (10^{−4}) | R _{0} |
---|---|---|---|---|---|---|
1 | 0.844 | 1.059 | 4.261^{b} | 0.155 | 1.148 | 1.116 |
3 | 0.930 | 1.316 | 4.261^{b} | 0.069 | 1.148 | 2.237 |
3 with varying α and γ | pr: 0.844 | pr: 1.059 | 4.261^{b} | pr: 0.059 | 1.148^{c} | pr: 1.116 |
Pr: 0.940 | Pr: 1.348 | Pr: 0.155 | Pr: 2.421 |
Considering the Portuguese population size, the estimated values correspond approximately to 11,000 people with primary TB infection, 1,600,000 with latent infection, 1,200 patients under treatment and 500 undiagnosed actively infected persons.
Effect of considering a heterogeneous model
Following the same reasoning as used for system (1), we assumed for system (3) that TB transmission in Portugal from 2002 to 2009 is close to equilibrium and that the fraction of actively TB infected individuals (${\mathit{I}}_{1}^{*}$ + ${\mathit{I}}_{2}^{*}$) was 4.261 × 10^{−5}. By considering the parameter values of Table 1, we obtained estimates of the stationary values (Table 4) and R _{0} = 2.237 (β = 145.024 yrs^{−1}).
These estimates were obtained assuming risk heterogeneity governed by parameters α and γ previously estimated from global tuberculosis data [8]. Additionally, we analyzed the sensitivity of estimated proportions of individuals in each class and of R _{0} estimation to variation in these parameters (Additional file 3 and Table 4). To avoid very low (i.e., close to 0) and very high (i.e., close to 1) values of α and γ, which greatly affect estimations, we consider values between 0.005 and 0.995. This sensitivity analysis results in particularly large variations in susceptible and latently infected classes (Table 4). Considering the Portuguese population size, the estimated values indicate approximately 11,000 to 14,000 people with primary TB infection, 600,000 to 1,600,000 with latent infection, 1,200 patients under treatment and 500 undiagnosed actively infected persons. R _{0} varies from 1.116 to 2.421 (β varies from 72.358 to 156.98 yrs^{−1}).
Trends of TB prevalence with transmission coefficient and R _{0}
Discussion
With a TB prevalence of over 20 cases per 100,000 individuals, Portugal is one of the countries with the highest TB burden in the European Union (along with Bulgaria, Estonia, Latvia, Lithuania, Poland and Romania) [9]. Furthermore, there are worries that the current economic crisis may increase TB burden in this country due to a consequent increase of unemployment, wealth inequality and general deterioration of the living conditions [17, 18]. For this reason, developing a mathematical model that suits the Portuguese scenario is important as to better understand the dynamics of this transmission system, identify key parameters in the control of TB and to inform policies by predicting the impact of interventions.
Despite the clear need for a better understanding of TB in Portugal, this work is the first that considers the complexity of TB transmission to estimate the Portuguese TB burden. Its main limitations are: the calculation of TB detection time from retrospective self-diagnosed data; the simplifications of the model, particularly, not assuming age-structure nor emigration; and the assumption of a steady-state for TB transmission. As such, although the results provide a valuable indication of TB burden in each stage of TB infection, they should be interpreted with caution.
In the present work, we had access to Portuguese clinical and socio-demographic data and calculated directly the standard detection time for TB as of about 2.81 months and the typical treatment length as 8.80 months. These values are comfortably within the range recommended by WHO [1]. The default proportion in Portugal was calculated as 4%, a value that is somewhat lower than previously reported (6 to 30%) [19]. Using these calculated values along with parameters taken from the literature, we assessed our proposed model against the Portuguese data by comparing expected temporal dynamics with those recorded in the database (Figure 2). Agreement is impressive given the simple and unconstrained nature of the model, indicating its adequacy to the study of TB transmission in Portugal. For this reason, although more complex heterogeneous models have been proposed [20, 21], we are confident on the suitableness of the considered model.
The burden of TB in a country is difficult to measure due to the different stages of the disease and the impossibility to quantify directly from surveillance data the proportion of individuals in some of these stages. In the adopted model we considered four stages of TB infection: primary infection; active disease not yet diagnosed; active disease undergoing treatment; and latent infection. Surveillance data is limited to patients entering the treatment stage. However, by considering a specific mathematical model we were able to estimate the total burden of TB in Portugal as of about 1.6 million infected individuals (i.e., about 11,000 with primary TB infection, 1,600,000 with latent infection, 1,200 under-treatment and about 500 undiagnosed active infections), corresponding to an R _{0} of 1.116. This value for R _{0} is very much in line with previous studies [22, 23].
Nonetheless, we recognize that, as evidenced by Murphy et al. [24] and Gomes et al. [8], individual variation in susceptibility to infection may change dramatically the transmission dynamics of TB leading to significant differences in the interpretation of population measures. Murphy and co-authors consider genetic factors as a partial cause for heterogeneity, but, as previously noted, there is much uncertainty associated to these factors [25]. Another possible cause for heterogeneity is whether individuals are vaccinated or not, however, given the inconsistent protection conferred by the Bacillus Calmette–Guérin vaccine, there is also considerable uncertainty related to the impact of vaccination [26]. Bacaer and co-authors [27] considered a heterogeneous model to study jointly TB and HIV epidemiology. This model divided the population in high and low risk of TB infection, depending if the persons were HIV positive or negative, and was developed to analyze data from South Africa, a country where about 12% of the population is infected with HIV [28]. Dowdy et al. [29] considered a heterogeneous transmission model to study TB data from Rio de Janeiro. In this study the high and low risk division was done by considering geographically distributed prevalence data, however, the existence of clearly defined “hotspots” of TB cases in this setting is mostly due to slumbers and other well-defined regions with a high impact of risk factors and social determinants. We argue, then, that heterogeneity in TB infection risk in Portugal is likely to result from a differential impact of biological and social determinants to different sections of the population [3, 17]. An often neglected caveat of introducing heterogeneity in TB transmission models is that the factors that drive heterogeneity may break assumptions of random mixing (e.g. being in prison or nursing-home, working on health-care, living in slumbers). In Portugal, however, the factors that have been implied with heterogeneity in TB risk are not markedly associated with closed-groups (e.g. HIV and drug-use [9], unemployment, wealth inequality and living conditions [17]). Given the difficulty to categorize the Portuguese population as having a high or low risk of infection so that the world-wide estimations from [8] could be directly verified and refined, we calculated the TB burden from a spectrum of profiles of heterogeneous susceptibility. Hence, we estimated a range of possible TB burden of between 0.6 and 1.6 million infected individuals (i.e., 11,000 to 14,000 with a primary TB infection, 600,000 to 1,600,000 latent-infected, and the same 1,200 under-treatment and 500 undiagnosed actively-infected as verified in the homogeneous scenario), corresponding to R _{0} from 1.116 to more than 2.421. The wide range of values for TB burden is mostly attributed to the wide range of estimates of the proportion of latently-infected individuals. Considering an increasing trend on the reactivation rate of latent infections [13], it is of great importance to characterize the heterogeneity of a population and, thus, to estimate with accuracy the proportion of latently-infected individuals in a population.
In the comparison between homogeneous and heterogeneous models, although we considered the same proportion of infectious individuals (i.e. 4.261 × 10^{-5}), we estimated a possible range of values for R _{0} and β in the heterogeneous case that can be considerable distinct from the ones estimated in the homogeneous case. This interesting dynamic is better explored in Figure 3, where we calculated the proportion of infected individuals for various values of β and R _{0} while considering systems 1 and 3 (with fixed values of α and γ). These results show that TB estimates of transmission coefficient β (and of R _{0}) may vary greatly depending on the model considered. Examining the effect of heterogeneity in R _{0} is especially important since, as noted before, its use in TB may be problematic if all the dynamics of the transmission system are not considered [30, 31].
Conclusions
We proposed a mathematical model for TB transmission, which fits well the dynamics described by records of the Portuguese National Directorate of Health, as a tool for intervention impact studies. Under homogeneity assumptions, we estimated an upper bound for the total burden of TB infection in Portugal of 1.6 million individuals (more than 15% of the Portuguese population). Accounting for heterogeneities in susceptibility and exposure to infection led to lower estimates of infection prevalence.
Declarations
Acknowledgements
We thank David Dowdy and Nico Nagelkerke for thoughtful comments, which improved the quality of the original manuscript. The authors are also grateful to Maurício Barreto for his input on the work, António Fonseca-Antunes for providing data on treatment of tuberculosis in Portugal from 2002–2009 and to Patrícia Soares for constructing and feeding these data to an SQL database. PR was supported by Fundação para a Ciência e a Tecnologia (FCT) [grant PEst-OE/MAT/UI0297/2011]. JSL and MGMG were supported by FCT and European Commission (EC) [grant EC-ICT-231807]. STRP and RFSA were partially supported by Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB) [grant PNX 0006/2009), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), FCT [grant AUX-PE-FCT 1171/2009], and INCT-Citecs (grant 57386/2008-9) and INCT-SC funded by the National Institutes of Science and Technology Programme (MCT-CNPq).
Authors’ Affiliations
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