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National estimate of HIV prevalence in the Netherlands: comparison and applicability of different estimation tools

van Veen, Maaike Ga; Presanis, Anne Mb; Conti, Stefanoc; Xiridou, Mariaa; Stengaard, Annemarie Rd; Donoghoe, Martin Cd; van Sighem, Ard Ie; van der Sande, Marianne Aa,f; De Angelis, Danielab,c

doi: 10.1097/QAD.0b013e32834171bc
Epidemiology and Social

Objectives: To determine limitations and strengths of three methodologies developed to estimate HIV prevalence and the number of people living with HIV/AIDS (PLWHA).

Methods: The UNAIDS/WHO Workbook method; the Multiparameter Evidence Synthesis (MPES) adopted by the Health Protection Agency; and the UNAIDS/WHO Estimation and Projection Package (EPP) and Spectrum method were used and their applicability and feasibility were assessed. All methods estimate the number infected in mutually exclusive risk groups among 15–70-year-olds.

Results: Using data from the Netherlands, the Workbook method estimated 23 969 PLWHA as of January 2008. MPES estimated 21 444 PLWHA, with a 95% credible interval (CrI) of 17 204–28 694. Adult HIV prevalence was estimated at 0.2% (95% CrI 0.15–0.24%) and 40% (95% CrI 25–55%) were undiagnosed. Spectrum applied gender-specific mortality, resulting in a projected estimate of 19 115 PLWHA.

Conclusion: Although outcomes differed between the methods, they broadly concurred. An advantage of MPES is that the proportion diagnosed can be estimated by risk group, which is important for policy guidance. However, before MPES can be used on a larger scale, it should be made more easily applicable. If the aim is not only to obtain annual estimates, but also short-term projections, then EPP and Spectrum are more suitable. Research into developing and refining analytical tools, which make use of all available information, is recommended, especially HIV diagnosed cases, as this information is becoming routinely collected in most countries with concentrated HIV epidemics.

aCentre for Infectious Diseases Control, National Institute of Public Health and the Environment, Bilthoven, The Netherlands

bMedical Research Council, Biostatistics Unit, Cambridge, UK

cHealth Protection Agency, London, UK

dWorld Health Organization, Regional Office for Europe

eStichting HIV Monitoring, Amsterdam, The Netherlands

fJulius Centre, University Medical Center Utrecht, Utrecht, The Netherlands.

Received 4 May, 2010

Revised 15 September, 2010

Accepted 16 September, 2010

Correspondence to Maaike van Veen, National Institute of Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands. Tel: +31 30 2743561; fax: +31 30 2744409; e-mail:

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Estimates of disease burden, based on sound and transparent methods, should be central to decisions about public health interventions [1]. National estimates of the number of people living with HIV/AIDS (PLWHA) are needed to focus planning of HIV prevention activities and provision of treatment services. Moreover, if available, estimates of the number of undiagnosed infections would offer an invaluable insight into the population unaware of their HIV infection status. Persons living with undiagnosed HIV infection are likely to miss opportunities for timely access to treatment and support, therefore suffering greater morbidity and mortality than those diagnosed and treated early. In addition, they also contribute to sustaining the epidemic as they may unknowingly transmit the infection to their sexual or injecting partners [2,3]. Marks et al. [4] found that 54–70% of new HIV infections in the United States in 2003 arose from those undiagnosed. It has also been estimated that, in the European Union, around a third of individuals infected were unaware of their serostatus [5], whereas in some countries in eastern Europe and central Asia, over 60% of persons living with HIV were undiagnosed [6]. Understanding the extent to which specific populations are undiagnosed is crucial to inform public health strategies [4]. Of particular relevance in this regard are efforts currently placed into developing strategies to encourage greater uptake of testing and counseling.

Different methods and tools have been used for HIV prevalence estimation such as extensions of the original back-calculation method [7,8]; variations of a direct method approach [9–12]; and simulations [13]. Joint United Nations Programme on HIV/AIDS (UNAIDS)/World Health Organization (WHO) tools [Workbook, Estimation and Projection Package (EPP), Spectrum] came under scrutiny after significant changes in global HIV and AIDS estimates were published at the end of 2007 [1,14]. These tools were consequently revised and improved to produce more reliable estimates [15–17]. It is imperative to understand the limitations and strengths of the methodologies that have been developed over time for the estimation of HIV prevalence and the number of PLWHA.

Data from the Netherlands, a western European country with a concentrated HIV epidemic (i.e. with overall HIV prevalence estimated below 1%), were used to assess the merits of different methods. Since the previous national estimate of 18 500 PLWHA derived from the Workbook method dates 2005 [18], an update on the number of PLWHA, the HIV prevalence and the proportion of HIV-positive individuals living with an undiagnosed infection is needed to understand and monitor the ongoing HIV epidemic in the Netherlands. The aim of this project was to estimate the number of PLWHA in the Netherlands, by exploring the following three methods: the UNAIDS/WHO Workbook method [10,19]; the Multiparameter Evidence Synthesis (MPES) approach [12,20]; and the UNAIDS/WHO EPP [16,21,22] and Spectrum [17].

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Materials and methods

The three adopted methods were chosen as they are specifically designed for HIV prevalence estimation. The Workbook method has already been applied in the Netherlands and is also widely established in other countries with concentrated epidemics [18]. MPES is routinely employed by the Health Protection Agency in the United Kingdom [12,23]. EPP and Spectrum are two complementary packages (here considered jointly), originally developed for generalized epidemics, but currently recommended by UNAIDS/WHO also for concentrated epidemics. Other methods (R. Lodwick, A. Alioum, C. Archibald, P. Birrell, D. Commenges, D. Costagliola, et al. Methods and data requirements for the estimation of the number of people with HIV. in preparation) considered in the development stage of this project were not used as they were not routinely implemented or were not originally conceived for HIV prevalence estimation. The methods were compared in terms of resulting outcomes; data requirements; applicability and the need for statistical and epidemiological expertise; and accuracy, biases and limitations.

Individuals were classified into risk groups defined to be mutually exclusive and graded by decreasing risk of infection. For example, a female sex worker (FSW) injecting drugs belongs to the risk group of injecting drug users (IDUs). The following groups were used:

1. MSM who attended a sexually transmitted infection (STI) clinic over the previous year

2. MSM not attending a STI clinic

3. IDUs

4. FSWs

5. STI clinic attendees (excluding those falling into the above higher risk groups 1–4)

6. Sub-Saharan African (SSA) migrants

7. Caribbean (CRB) migrants

8. Individuals at low risk of infection (not in any of the previous groups).

The three estimation methods require different inputs (see Table 1), with at least information on subgroup sizes and HIV prevalence for all eight risk groups. The target population was restricted to the age group 15–70 years, as the desired outcome was an estimate of the adult, sexually active, PLWHA in the Netherlands. Furthermore, groups 3 and 5–8 were classified by gender. Depending on the method, additional information – for instance, number of cases in specialized HIV care or data on antiretroviral therapy (ART) uptake – was also required.

As the HIV epidemic in the Netherlands, like in most European countries, exhibits a strong geographical component [24,25], three regions were considered: Amsterdam, Rotterdam and the rest of the Netherlands, which account for 33, 10 and 57% of HIV cases registered in care, respectively. In the Netherlands, all diagnosed HIV-infected individuals are referred to specialized care and recorded by the HIV Monitoring Foundation in a observational longitudinal database.

To estimate the number of PLWHA, data were collected for each risk group in each geographical area (Tables 2 and 3). For instance, a national database on consultations at STI clinics provided information about diagnosed prevalence among MSM and heterosexual STI clinic attendees [26]. Furthermore, unlinked anonymous HIV surveys provided information on HIV prevalence among IDU, FSW and migrant populations [27]. An overview of all data sources used is outlined in Supplement 1,

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Workbook method

The UNAIDS/WHO Workbook method [19] is based on the identification of groups at different risk of HIV infection in each region of interest. The method assumes that pregnant women attending antenatal clinics are representative of the low-risk female population, whereas no low-risk men are envisaged. For each group, estimates of lower and upper bounds on size and HIV prevalence are required as input. After multiplying each combination of lower and upper bounds of group size and prevalence, the average of the four resulting figures is taken as the estimate of the number of subgroup-specific infections. The method, which is implemented in Excel, allows estimation of both point prevalence and prevalence trends through mathematical interpolation; however, here only point estimates were obtained. The national estimate is obtained by summing regional estimates [10]. For each risk group, most recently available data on population sizes and HIV prevalence (Tables 2 and 3) were acquired.

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Multiparameter evidence synthesis method

The MPES approach was introduced in the United Kingdom in response to the need to adopt an estimation method that would use all available data, notably on diagnosed infections, and provide a realistic representation of the uncertainty inherent in the estimates [12]. In MPES, the population is subdivided into a number of mutually exclusive groups (g) (Tables 2 and 3 and Supplement 1,, residing in different geographical regions (r). Heterosexual STI clinic attendees were further classified by ethnicity as SSA migrants, CRB migrants and nonmigrants. The method combines all available information to estimate three basic parameters for each subgroup and region combination: ρg,r, the proportion of the population in subgroup g in region r; πg,r, the corresponding HIV prevalence; and δg,r, the corresponding proportion of infections that are diagnosed.

From these estimates, using census information on the size of the total population, an estimated total number of infections, subdivided into diagnosed and undiagnosed infections, can be derived. Although in some cases, direct information is available on ρg,r, πg,r and δg,r, evidence is mostly available indirectly, that is, on functions of these basic parameters. MPES allows effective use of all information, both direct and indirect, in a unified Bayesian model. The available data are, within this model, combined with initial beliefs or expert opinion on the basic parameters, expressed in terms of probability distributions (priors), to produce a ‘posterior’ distribution for the basic parameters and any functions of these. This posterior distribution conveys our final in-and out-of-sample knowledge on the quantities of interest. The uncertainty in the estimation of ρg,r, πg,r and δg,r is then propagated through the posterior distributions of the number of people living with diagnosed or undiagnosed HIV infection. This is usually summarized via medians and 95% credible intervals (CrIs), respectively providing a point estimate and a measure of its accuracy (A 95% credibility interval indicates the range within which the parameter of interest falls with probability 95%. It differs from a 95% confidence interval in that the latter is expected to include the parameter in 95% of hypothetical replications of the experiment, which led to the actual sample. Credibility and confidence intervals are expression of different statistical paradigms, that is, the Bayesian and frequentist approaches.). The use of multiple data sources can lead to conflicting evidence. Discrepant items of data were reconciled in MPES through feedback from epidemiologists and data suppliers. Additionally, a number of assumptions were introduced as detailed in Supplement 3,

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Estimation and Projection Package and Spectrum methods

EPP was developed by UNAIDS/WHO, initially for generalized epidemics, to provide short-term projections of HIV prevalence and incidence [22]. EPP fits a transmission model to surveillance time-series of prevalence data, to produce a national estimate of the HIV prevalence. This transmission model is described by a set of three differential equations governing the dynamics of the sizes of three mutually exclusive population compartments: a group not at risk of HIV infection; a group at risk of infection; and a group infected with HIV [28]. These differential equations are parameterized by the rate r of growth of the epidemic; the fraction f0 of the population at risk of infection at the start of the epidemic; the start year t0 of the epidemic; and a behavioral response parameter Φ, describing how the epidemic levels off after reaching its peak [16]. In concentrated epidemics, a fifth parameter (d) is additionally included in the model to account for the turnover of people from the higher risk population subgroups (for example, sex workers ‘returning’ to the low-risk group after a median 3 years of stay in the ‘FSWs’ risk group). Estimates of the above system parameters are obtained via a Bayesian approach, and the uncertainty surrounding them is then propagated through the model via Monte Carlo simulation, thereby yielding a sample of HIV prevalence curves. The median and 2.5–97.5% percentiles of the resulting set of curves at any given year are conventionally reported as final summaries. The latest (2009) EPP release allows incorporation of data on (combination) ART. Retrospective prevalence data from the beginning of the epidemic (1985 in the Netherlands) are required. Ideally, for each subgroup of interest, the model requires at least three corresponding prevalence data points over time (Supplement 2,

The output produced by EPP can then be imported into the complementary Spectrum package to produce a richer array of demographic and epidemiological HIV/AIDS descriptors (such as number of new HIV infections or number needing ART) [29] (Brown T, Stover J; 2009, personal communication). The EPP and Spectrum packages are designed to complement each other (Brown T, Stover J; 2009, personal communication). For the purpose of producing a national HIV estimate, two Spectrum modules are used. The first is demography, a program computing population projections based on the current population size and fertility, mortality and migration rates. The second module used is the AIDS Impact Model, a program predicting the consequences of the AIDS epidemic, including the number of PLWHA, given an assumed adult HIV prevalence [30]. The demographic projection is modified by the AIDS Impact Model through information on AIDS-related deaths and the impact of HIV infections on fertility.

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Workbook method

Using the Workbook method, it was estimated that 23 969 PLWHA aged 15–70 years were living in the Netherlands as of 1 January 2008 (Table 4). Compared to the observed number of 12 649 HIV cases that were registered in care and alive on 1 January 2008 [25], this implies that 53% were in care. Compared to the 2005 estimate of 18 500, the estimated number of PLWHA has increased by almost 30%. The method itself does not estimate proportions of infections undiagnosed nor provides a formal measure of the uncertainty surrounding the estimates.

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Multiparameter Evidence Synthesis approach

MPES estimated that 21 444 (95% CrI 17 204–28 694) people aged between 15 and 70 years were living with HIV/AIDS in the Netherlands as of January 2008 (Table 4). The adult HIV prevalence was estimated at 0.2% (95% CrI 0.15–0.24%). Figure 1 illustrates subgroup-specific and region-specific estimates of the proportions diagnosed with HIV. Overall, it was estimated that 40% (95% CrI 25–55%) of infected individuals lived with an undiagnosed infection. Notably, large differences in the proportion undiagnosed were found between the regions, especially among migrants not attending a STI clinic.

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Estimation and Projection Package/Spectrum method

EPP/Spectrum produced an estimated HIV prevalence in the Netherlands for 2008 of 0.2%, with the number of PLWHA aged 15 years or more estimated at 19 115 (95% CrI 15 902–22 577) in 2008 (Table 4). For 2013, it is projected that 21 287 (95% CrI 15 884–26 093) people aged 15 years or more will live with HIV in the Netherlands (Fig. 2). Spectrum provided a 95% credibility interval around the aggregate (but not subgroup-specific) PLWHA.

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On the basis of results from MPES, we estimate that there were 21 444 (95% CrI 17 204–28 694) PLWHA in the Netherlands as of January 2008, aged between 15 and 70 years. Overall, an estimated 40% of the HIV-infected population remained undiagnosed, with a particularly high proportion outside Amsterdam and Rotterdam. The HIV prevalence is estimated at 0.2% based on EPP/Spectrum implying a projected number of 19 115 PLWHA in the Netherlands. Workbook estimated a national figure of 23 969 PLWHA.

An important implication of this work is the confirmation that even in the Netherlands – a western European country with reasonably good access to testing and counseling – a substantial proportion of HIV infections were estimated as undiagnosed. Although high compared to the UK and the US where 28 and 21%, respectively, were unaware of their infection [31,32], this estimate is plausible as the Netherlands introduced an active testing policy rather late. Especially among migrant populations, it is known that access to testing is limited [33]. This is reflected in the proportion of HIV-infected patients who present late (CD4 cell count <200) in care. Among heterosexuals (usually originating from HIV endemic countries), this was almost 40% in 2009 [34]. It stresses the need for further efforts in developing strategies to achieve greater uptake of testing, counseling and referral into care.

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Accuracy and biases

The contrasted methods produced three different outcomes, though within a relatively similar range. Moreover, the results from both UNAIDS tools fit well within the credibility interval of the MPES method (17 204–28 694). The MPES method produced a cross-sectional estimate of 21 444 PLWHA that is consistent with all information, as it relies on all data available, including HIV diagnosis data for registered HIV cases in care. EPP/Spectrum produced an estimate of 19 115 PLWHA relying on a projection of HIV incidence curves from retrospective prevalence data spanning several years. Demographic assumptions in Spectrum contributed to reducing the EPP estimate. In comparison with MPES and EPP/Spectrum, the somewhat higher Workbook outcome of 23 696, despite the assumption of no low-risk men being infected with HIV, relied on fewer data. Referring to the Workbook outcome, the number of PLWHA has increased by 30% compared to the previous estimate in 2005, which is much steeper (unlikely realistic) increase than the 18% increase in registered cases in care [26]. Furthermore, Workbook only provides a single point estimate, with a minimum-maximum range, but with no formal uncertainty measure attached. Both MPES and EPP/Spectrum are likely to provide more accurate prevalence estimates than Workbook as they employ a richer collection of data and rely on a more refined set of assumptions. However, only MPES provides estimates that are based on, and hence consistent with, all available information, fully reflecting the uncertainty in the data and estimation process. By allowing detection and modeling of biased or conflicting evidence, MPES encourages a critical re-appraisal of the whole body of evidence. For these reasons, although a formal comparison between methods relying on different data, assumptions and structure remains difficult, we suggest that the most reliable estimate of PLWHA is provided by MPES. In addition, MPES provides subgroup-specific and region-specific estimates, allowing to identify risk groups and regions with the highest proportions undiagnosed and, therefore, most in need of intervention measures. This information is especially useful in guiding public health strategies.

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Data requirements

Data on population size and HIV prevalence for each subpopulation and region are needed to both Workbook and MPES for the target year (Table 1). On the other hand, EPP/Spectrum requires retrospective prevalence data from the beginning of the epidemic and data on ART use. MPES uses data on proportions diagnosed (derived from anonymous unlinked HIV prevalence surveys) and numbers of HIV cases registered in care. The latter is an advantage of MPES, because data on diagnosed HIV cases are now routinely collected in most high-income and many low-income and middle-income countries [35], whereas the other methods are not designed to use this information.

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Applicability and expertise needed

Of the three tools, Workbook is the easiest to use: it is very user-friendly and requires no statistical expertise to be understood and used. Once data on input parameters are collected, Workbook takes only a short time to perform. Although less straightforward than Workbook, EPP/Spectrum is also user-friendly and the accompanying user guide illustrates the estimation process comprehensively. No statistical or mathematical knowledge is required to implement EPP/Spectrum. In contrast to Workbook and EPP/Spectrum, MPES is less user-friendly and more difficult to apply. It is implemented in the freely available WinBUGS software [36] (code available from the authors on request); however, the code is bespoke and needs to be adapted to the case-study at hand, depending on the available data. Hence, both statistical and epidemiological knowledge are needed to implement a MPES approach. Biases and contradictions in the data must be discussed and well understood prior to being modeled. The method is, therefore, more challenging than the others. To increase the applicability of MPES, a more user-friendly and specifically designed programming environment would be useful.

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An important limitation of Workbook is that it only allows low-risk women in the exposure group. Furthermore, Workbook was initially developed to estimate HIV prevalence among the adult population aged 15–49 years. In many high-income countries, persons aged 50 and over account for a substantial proportion of PLWHA [5,25]. We recommend that when using the Workbook method, this should also include the population older than 50 years. For EPP/Spectrum, data on future ART uptake are required; this can be done only by extrapolating our current knowledge and expectations for the coming years. Furthermore, as Spectrum assumes that women live longer than men after contracting HIV, this will result in a somewhat heavier mortality in the case of mainly male-driven epidemics (T. Brown, J. Stover, 2009, personal communication). Thus, in epidemics that are mainly MSM-driven (such as in many western European countries and, to some extent, in the United States) or IDU-driven (as in eastern Europe), these assumptions may have an excessive impact on the projected number of PLWHA. Different methods are being used to estimate the size of risk groups, but there is currently no consensus on the best and most appropriate methods to use [15]. In addition, the data used in the models were not all from the same year: for instance, FSW estimates relied upon rather outdated statistics on population size.

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The choice of a method necessarily depends on the objectives, data availability and time constraints. For a realistic reflection of the uncertainty inherent in both the estimation process and the data, MPES could be most appropriate. Moreover, only MPES yields subgroup-specific and region-specific estimates of the proportions undiagnosed. On the other hand, if the aim is not just to obtain annual estimates, but also short-term projections, then EPP/Spectrum could be more suitable. Overall, we recommend MPES as the most statistically sound method, but EPP/Spectrum is the most practical package in terms of data and expertise needed to provide estimates. Future work should focus on developing methods that include data on HIV diagnoses and on methods for risk group size estimation.

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M.v.V. was involved in the design of the study, interpreted data and parameters and carried out Workbook and EPP method. M.v.V. was also involved in interpreting MPES and wrote the article. A.P. designed various aspects of the MPES model for Dutch data and was involved in critically reviewing and revising the article. S.C. programmed and implemented the MPES model, was involved in the statistical analysis and critically reviewed and edited the article, especially the Materials and Methods. M.X. was involved in the design of the study and critically reviewed and revised the article, especially the Discussion section of the text. A.R.S. was involved in the design of the study and reviewed the article. M.D. was involved in the design of the study and reviewed and edited the article. A.v.S. supplied data of HIV diagnosed cases and reviewed and edited the article. M.v.d.S. was involved in interpretation of the data and methods and reviewed and revised the article. D.D.A. was involved in the design of the study, interpreted data and results from the MPES model and reviewed and revised the article.

The authors would like to thank the WHO Regional Office for Europe for partially funding this project through the partnership program between WHO and the Netherlands Ministry of Health, Welfare and Sport. They would like to thank, in particular, Stine Nielsen (currently at the Robert Koch Institute, Berlin) for her assistance and suggestions. We are very grateful to Eline Op de Coul from the National Institute for Public Health and the Environment (RIVM) for her contribution to the estimation process. Roel Coutinho (RIVM) is acknowledged for his comments and criticism on the article. Mary Mahy (UNAIDS), John Stover (Futures Institute), and Tim Brown (East-West Centre) are acknowledged for their explanation of the UNAIDS/WHO packages EPP/Spectrum. The authors are grateful to Gijs Baaten, Marcel Buster, Anneke van den Hoek, Rik Koekenbier, Anneke Krol, Martijn van Rooijen, Ineke Stolte, and Daan Uitenbroek from the Amsterdam Health Service for providing data from the Amsterdam Health Monitor, the Amsterdam pregnancy screening, drug monitor, anonymous unlinked HIV survey among STI clinic attendees, laboratory results of, and data from the Amsterdam Cohort Studies among MSM and drug users. We thank Hannelore Götz and Bianca Stam (Municipal Health Service Rotterdam-Rijnmond) for providing data from Rotterdam pregnant screening and the Rotterdam Health Monitor and Nicole Dukers (South Limburg Public Health Service) for providing data from the Amsterdam Health Monitor. Femke Koedijk and Liesbeth Mollema from the RIVM are thanked for providing data from the STI centers and PIENTER study. Floor Bakker of Rutgers Nisso is acknowledged for providing data on the sexual health survey. Tobias Dorfler (Schorer) and Harm Hospers (University Maastricht) are acknowledged for providing data from the Schorer monitor.

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Estimation and Projection Package; estimation tools; HIV; Multiparameter Evidence Synthesis; prevalence; Spectrum; Workbook

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