# Estimates of the HIV undiagnosed population in Belgium reveals higher prevalence for MSM with foreign nationality and for geographic areas hosting big cities

Lise Marty, Dominique van Beckhoven, Cloë Ost, Jessika Deblonde, Dominique Costagliola, André Sasse, Virginie Supervie

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## To cite this version:

Lise Marty, Dominique van Beckhoven, Cloë Ost, Jessika Deblonde, Dominique Costagliola, et al. "Estimates of the HIV undiagnosed population in Belgium reveals higher prevalence for MSM with foreign nationality and for geographic areas hosting big cities." Journal of the International AIDS Society, 2019, 22 (8), pp.e25371. 10.1002/jia2.25371. hal-02291918

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SHORT REPORT

# Estimates of the HIV undiagnosed population in Belgium reveals higher prevalence for MSM with foreign nationality and for geographic areas hosting big cities

Lise Marty¹§ ID, Dominique Van Beckhoven², Cloë Ost², Jessika Deblonde², Dominique Costagliola¹, André Sasse², Virginie Supervie¹§ ID and the HERMETIC Study Group*

§Corresponding authors: Lise Marty and Virginie Supervie, INSERM, UMRS 1136, 27 rue de Chaligny, 75012 Paris. Tel: +33(0)185347256. (lise.marty@iplesp.upmc.fr; virginie.supervie@inserm.fr)

*A complete list of HERMETIC Study Group authors can be found in Appendix at the end of the manuscript.

## Abstract

**Introduction:** Increasing our knowledge on geographic areas and key populations most affected by HIV is essential to improve prevention and care and to ensure a more focused HIV response. Here, we estimated the prevalence of undiagnosed HIV infections in Belgium and its distribution across geographic areas and exposure groups.

**Methods:** We used surveillance data on newly diagnosed HIV cases and a previously developed back-calculation model to estimate number and prevalence rates (per 10000) of undiagnosed HIV infections by exposure group at national and subnational levels. Belgium consists of three regions: Flanders, Brussels-Capital Region and Wallonia. We produced estimates for Brussels-Capital Region and Wallonia. For Flanders, we produced estimates for two sub-regional areas: the province of Antwerp and the other provinces, because Antwerp is the second largest city after Brussels. Population sizes were determined using data from the Belgian Statistical Office and surveys on sexual behaviour and drug use.

**Results:** In Belgium, in 2015, an estimated 2818 (95% confidence interval: 2494 to 3208) individuals were living with undiagnosed HIV, that is, 15% of individuals living with HIV. The Brussels-Capital Region and the province of Antwerp, which host the two biggest cities, accounted for ~60% of the undiagnosed infections, and had the highest undiagnosed prevalence rates per 10000: 12.0 (9.4 to 15.3) and 7.4 (5.6 to 9.8) respectively. Individuals with foreign nationality accounted for 56% of the total number of undiagnosed infections, and were the most affected populations in all areas in terms of undiagnosed prevalence rates. Specifically, men who have sex with men (MSM) with non-European nationality were the most affected population in the province of Antwerp (853.4 (408.2 to 1641.9) undiagnosed infections per 10000), the Brussels-Capital Region (543.9 (289.1 to 1019.1)), and the other provinces of Flanders (691.7 (235.5 to 1442.2)), while in Wallonia, it was heterosexual women with Sub-Saharan African nationality (132.2 (90.6 to 178.5)).

**Conclusions:** Geographic areas hosting the biggest cities in Belgium accounted for the vast majority of undiagnosed HIV infections and individuals with foreign nationality were the most affected, especially MSM with non-European nationality. This should be accounted for when tailoring prevention and testing programs. Furthermore, MSM with foreign nationality require more attention in Belgium, and certainly more generally in Europe.

**Keywords:** modelling; undiagnosed HIV infections; subnational estimates; exposure group; men who have sex with men; foreign nationality

Additional information may be found under the Supporting Information tab for this article.

Received 13 December 2018; Accepted 21 July 2019

Copyright © 2019 The Authors. Journal of the International AIDS Society published by John Wiley & Sons Ltd on behalf of the International AIDS Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

## 1 | INTRODUCTION

Early initiation of antiretroviral therapy (ART) during the course of HIV infection reduces HIV-related morbidity and mortality and is thus critical for HIV-positive individuals [1].

Furthermore, ART enables viral load suppression, which prevents HIV transmission to seronegative partners [2]. Large access to ART could thus have a considerable impact on HIV incidence, provided there is no delay between HIV infection and treatment initiation, followed by good ART compliance [3].
In many settings, however, ART initiation is delayed because
many HIV-infected individuals remain undiagnosed or/and are
diagnosed late, hindering engagement in care [4-6]. The undi-
agnosed HIV population contributes disproportionally to HIV
transmission, and the proportion of new infections attributed
to people with undiagnosed infection may be increasing in
some settings [e.g. 7], thus tackling this issue is a major chal-
lenge in the fight against HIV.

To enable early diagnosis of HIV infection, it is key to quan-
tify and characterize the HIV-positive population that remains
undiagnosed, and to determine geographic areas and key pop-
ulations with highest prevalence of undiagnosed infections. In
this study, we focus on Belgium. Previous works showed that,
in Belgium, men who have sex with men (MSM) and individu-
als originating from Sub-Saharan African countries repre-
sented a high proportion of newly diagnosed HIV cases [8]. In
addition, originating from Sub-Saharan African countries was
associated with delayed HIV diagnosis [9]. The undiagnosed
population has been shown to be the weakest stage of the
HIV care continuum in Belgium [8,9]. However, estimates of
the undiagnosed population by exposure group are still lack-
ing, at national and subnational levels. Here, we used national
surveillance data on newly diagnosed HIV cases, stratified by
clinical stage at HIV diagnosis, and a previously developed sta-
tistical model [10-12] to estimate prevalence of undiagnosed
HIV infections in Belgium and assess its heterogeneity across
populations and geographical areas.

2 | METHODS

## 2.1 | Data sources

To obtain the numbers of new HIV diagnoses, over time,
stratified by clinical stage at diagnosis, two data sources were
combined: the national HIV registry database, containing
exhaustive reports of the newly diagnosed HIV cases by the
seven Belgian AIDS Reference Laboratories (data available
from 1994 to 2015), and the Belgian HIV Cohort [9], collect-
ing data from all the AIDS Reference Laboratories and HIV
Reference Centres (data available from 2006 to 2015), both
managed by Sciensano, which is the Belgian Scientific Institute
of Public Health, legally entitled for surveillance activities,
including the infectious diseases surveillance. From these data
sources, we obtained for each newly diagnosed HIV case, its
sex, year of birth and date of HIV diagnosis, and the following
variables at the time of diagnosis: HIV exposure group
(heterosexual, MSM, injecting drug user), nationality, geo-
graphic area of residence and CD4 cell count. We also
obtained information on the clinical stage at HIV diagnosis,
and specifically on the presence/absence of a recent infection
(<6 months) or AIDS. Recent infection is reported by the diag-
nosing physician based on clinical symptoms of acute infection,
a recent negative test, or a recent history of risk behaviours
with a known HIV-positive partner. AIDS-defining events
reported by the HIV Reference Centres within three months
of HIV diagnosis were taken into account (only available from
2006 onwards). From this information, it was then possible to
determine the clinical stage at HIV diagnosis: recent infection,
AIDS, or neither AIDS nor recent infection. The annual num-
ber of new HIV diagnoses and the proportions of recent infec-
tion and AIDS at diagnosis are displayed on Figure S1 of the

supplementary information. Note that HIV surveillance data
collection was approved by the Privacy Commission, indepen-
dent administrative authority protecting privacy and personal
data [13]. A strict attention to confidentiality is present at
every stage of data collection, analysis and storage.

2.2 | The statistical model

To derive estimates of the undiagnosed HIV population, first,
we fitted surveillance data on newly diagnosed HIV cases,
stratified by clinical stage at HIV diagnosis, using a back-calcu-
lation model [10-12] to estimate annual numbers of new HIV
infections and distributions of time from HIV infection to diag-
nosis. Full details on the model have been previously pub-
lished [10] and are also provided in the supplementary
information. Briefly, the back-calculation model uses the
annual numbers of new HIV diagnoses stratified by clinical
stage at diagnosis to simultaneously estimate the trend in HIV
incidence and the distribution of times from infection to diag-
nosis. The distributions of times from infection to diagnosis
for individuals diagnosed with AIDS and for those diagnosed
during recent infection are assumed to be known, with median
value of ten years and three months respectively. The distribu-
tion of times from infection to diagnosis for individuals diag-
nosed without AIDS nor recent infection is assumed to be
unknown and depends on two parameters, representing
uptake of routine testing and onset of pre-AIDS-related symp-
toms. Values for these two parameters are estimated together
with HIV incidence by fitting the back-calculation model to
the surveillance data. Second, we projected forward the esti-
mated annual numbers of new HIV infections according to the
estimated distributions of time from infection to diagnosis to
estimate the number of undiagnosed infections.

## 2.3 | Estimating numbers and rates of undiagnosed infections

Using surveillance data on newly diagnosed HIV cases and the
back-calculation model, we produced estimates of the number
of undiagnosed HIV infections by sex, HIV exposure group
and nationality, at national and subnational levels; estimates
were obtained independently for each population and geo-
graphic area. We considered four geographic areas, based on
the administrative division of Belgium. Belgium consists of
three regions: Flanders, Brussels-Capital Region and Wallonia.
Flanders and Wallonia are further divided into provinces. We
produced estimates by population at the regional level for
Brussels-Capital Region and Wallonia. For Flanders, we pro-
duced estimates for two sub-regional areas: the province of
Antwerp and the other provinces, because Antwerp is the sec-
ond largest city of Belgium after Brussels.

Rates of undiagnosed infections were calculated by dividing
the numbers of undiagnosed infections by the corresponding
population size. Population sizes at national and subnational
levels were determined from three data sources: Statbel, the
Belgian Statistical Office, for the numbers of inhabitants aged
18 to 64 by areas, sex, and nationality, and two surveys on
sexual behaviour [14] and drug use [15], for the proportions
of MSM (defined as men homosexually active [14]) and the
number of persons injecting drugs (defined as individuals who
injected drugs at least once over their lifetime).
Finally, using our estimates of the total number of undiag-
nosed infections and an estimate of the number of individuals
living with diagnosed HIV obtained previously [9], we calcu-
lated the overall number of individuals living with HIV and the
percentage of individuals unaware of their positive HIV status.

2.4 | Statistical analysis

At the time of analysis, surveillance data up to 2015 were
available. Multiple imputation by chained equation [16] was
used to impute missing values. We used a two-step proce-
dure to impute data to account for the fact that some vari-
ables (e.g. clinical stage) had more missing values before
2006. We first imputed missing data for the period 2006
to 2015 and generated ten imputed databases. Then each
of the ten imputed databases was merged with the raw
data for the period 1994 to 2005. Next, we imputed miss-
ing data over the 1994 to 2005 period and generated a
total of 50 imputed databases, that is, five new imputed
databases for each merged database. We then generated a
total of 1500 new datasets, by creating several bootstrap
datasets from each of the 50 imputed databases, and we
ran the model on each of the 1500 datasets. Mean esti-
mates and 95% confidence intervals (CI), using the per-
centiles method, were obtained from the 1500 runs.
Statistical comparisons of rates were performed using
Mann-Whitney tests in R3.2.4 [17].

3 | RESULTS

**3.1 | Overall estimates and population**
**heterogeneity at national level**

An estimated 2818 (95% CI: 2494 to 3208) individuals were
living with undiagnosed HIV infection in Belgium in 2015
(70% of men, 30% of women), corresponding to an undiag-
nosed prevalence rate in the general population (aged 18 to
64) of 4.1 (95% CI: 3.6 to 4.6) per 10,000 (Table 1). Given
that, at the end of 2015, it was estimated that 15,885 individ-
uals were living with diagnosed HIV in Belgium [9], we
deduced that, overall, 18,703 individuals were living with HIV
at the end of 2015 and 15% of them were unaware of their
HIV status.

Individuals with foreign nationality accounted for more than
half of the total number of undiagnosed HIV infections
(Table 1); MSM with foreign nationality accounted for ~20%
of the total number (MSM with non-European nationality for
~10%) and heterosexuals with foreign nationality for ~40%
(heterosexuals with Sub-Saharan African (SSA) nationality for
~25%). They also had the highest undiagnosed prevalence
rates (per 10,000): 264.0 for MSM with foreign nationality
versus 55.6 for Belgian MSM (p < 0.05), 14.5 for heterosexual
women with foreign nationality versus 0.7 for Belgian hetero-
sexual women (p < 0.05), 9.9 for heterosexual men with for-
eign nationality versus 1.0 for Belgian heterosexual men
(p < 0.05).

Among individuals with foreign nationality, some groups had
even higher rates. Among MSM, those with non-European
nationality had the highest rate of undiagnosed prevalence,
534.3 per 10,000, that is, almost ten-fold the rate for Belgian
MSM (p < 0.05). MSM with European nationality had the

second highest rate, 151.3 per 10,000, that is, almost three-
fold the rate for Belgian MSM (p < 0.05). Among heterosexu-
als with foreign nationality, most undiagnosed infections
occurred among people with SSA nationality: 76% of women
and 57% of men. Estimated rates were respectively 173.0 and
93.1 per 10,000 for women and men with SSA nationality.

3.2 | Overall estimates and population heterogeneity at subnational level

Subnational estimates revealed that around 60% of the undi-
agnosed individuals were living in two areas: the Brussels-
Capital Region (~30%) and the province of Antwerp (~30%).
These areas were also the most affected in terms of rates,
with respectively 12.0 and 7.4 persons living with undiagnosed
HIV infection per 10,000 inhabitants. These rates were signifi-
cantly higher than the national level rate (p < 0.05). In con-
trast, rates for Wallonia and the other provinces of the
Flanders region were significantly lower than the national rate,
2.3 and 2.7 respectively (p < 0.05).

In all considered areas, MSM and heterosexuals with for-
eign nationality were the most affected populations, but their
ranking varied from one area to another. In Brussels-Capital
Region and the province of Antwerp, the most affected popu-
lations in terms of rates were as follows (p < 0.05 for all two-
by-two comparisons): MSM with non-European nationality
(with respective rates of 543.9 and 853.4), MSM with Euro-
pean nationality (233.9 and 348.8), heterosexual women with
SSA nationality (147.4 and 331.8), heterosexual men with SSA
nationality (103.5 and 117.6) and Belgian MSM (145.9 and
89.0). For the other provinces of Flanders, it was MSM with
non-European nationality (691.7), heterosexual women with
SSA nationality (200.5), MSM with European nationality
(130.3), heterosexual men with SSA nationality (120.2) and
Belgian MSM (41.3). In Wallonia, it was heterosexual women
with SSA nationality (132.2), MSM with foreign nationality
(65.1), heterosexual men with SSA nationality (56.6) and Bel-
gian MSM (41.3).

4 | DISCUSSION

This study is the first to provide estimates for the undiag-
nosed HIV population, by exposure group, at national and sub-
national level in Belgium as well as an estimate for the first
step of the cascade of care.

We found that individuals with foreign nationality, whether
MSM, heterosexual women or men, accounted for more than
half of the undiagnosed population and were the most
affected by undiagnosed HIV. Our findings for heterosexuals,
showing that individuals with Sub-Saharan African nationality
are at high risk of undiagnosed HIV infection, are in line with
other European studies [10]. The importance of the HIV bur-
den among MSM with foreign nationality has been less inves-
tigated in Europe. However, our findings for this population
corroborate two recent studies: one in France showing that
born-abroad MSM had the highest undiagnosed prevalence
rates [10] and another one in Catalonia, Spain, showing that
the proportion of HIV-infected individuals living with undiag-
nosed HIV was greater for migrant MSM than for local MSM
(32% vs. 22%) [18].
<table><caption>Table 1. National and subnational level estimates of undiagnosed HIV prevalence (and 95% confidence intervals) in 2015 in Belgium</caption><thead><tr><th rowspan="2"></th><th colspan="3">Belgium</th><th colspan="3">Brussels-Capital Region</th><th colspan="3">Flanders</th><th colspan="3">Wallonia</th></tr><tr><th>Undiagnosed<br>infections</th><th>Population size<br>(18 to 64 years)</th><th>Rate per<br>10,000</th><th>Undiagnosed<br>infections</th><th>Rate per<br>10,000</th><th>Undiagnosed<br>infections</th><th>Rate per<br>10,000</th><th>Undiagnosed<br>infections</th><th>Rate per<br>10,000</th><th>Undiagnosed<br>infections</th><th>Rate per<br>10,000</th><th>Undiagnosed<br>infections</th><th>Rate per<br>10,000</th></tr></thead><tbody><tr><td>Global</td><td>2818 (2494<br>to 3208)</td><td>6,901,298</td><td>4.1 (3.6 to 4.6)</td><td>906 (708 to 1152)</td><td>12.0 (9.4<br>to 15.3)</td><td>824 (618<br>to 1090)</td><td>7.4 (5.6 to 9.8)</td><td>752 (613<br>to 963)</td><td>2.7 (2.2 to 3.4)</td><td>497 (404 to 612)</td><td>2.3 (1.8 to 2.8)</td></tr><tr><td>Men</td><td>1967 (1702<br>to 2307)</td><td>3,462,994</td><td>5.7 (4.9 to 6.7)</td><td>660 (476 to 899)</td><td>17.7 (12.7<br>to 24.1)</td><td>542 (393 to 755)</td><td>9.7 (7.0<br>to 13.4)</td><td>510 (404<br>to 662)</td><td>3.6 (2.8 to 4.6)</td><td>325 (242<br>to 429)</td><td>3.0 (2.2 to 3.9)</td></tr><tr><td>Women</td><td>851 (688<br>to 1058)</td><td>3,438,304</td><td>2.5 (2.0 to 3.1)</td><td>245 (170 to 368)</td><td>6.5 (4.5 to 9.7)</td><td>282 (170 to 484)</td><td>5.1 (3.1 to 8.8)</td><td>242 (169<br>to 395)</td><td>1.7 (1.2 to 2.8)</td><td>172 (130<br>to 223)</td><td>1.6 (1.2 to 2.0)</td></tr><tr><td>MSM</td><td>1207 (1006<br>to 1501)</td><td>144,753</td><td>83.4 (69.5<br>to 103.7)</td><td>346 (236 to 502)</td><td>221.5 (151.1<br>to 321.4)</td><td>340 (231 to 481)</td><td>144.8 (98.4<br>to 204.9)</td><td>366 (257<br>to 519)</td><td>61.3 (43.0<br>to 86.9)</td><td>203 (139<br>to 324)</td><td>44.2 (30.3<br>to 70.5)</td></tr><tr><td>Belgian MSM</td><td>698 (563<br>to 904)</td><td>125,472</td><td>55.6 (44.9<br>to 72.0)</td><td>137 (73 to 250)</td><td>145.9 (77.7<br>to 266.2)</td><td>183 (120 to 283)</td><td>89.0 (58.4<br>to 137.6)</td><td>227 (156<br>to 339)</td><td>41.3 (28.4<br>to 61.7)</td><td>168 (109<br>to 282)</td><td>41.3 (26.7<br>to 69.3)</td></tr><tr><td>MSM with foreign<br>nationality</td><td>509 (381<br>to 726)</td><td>19,281</td><td>264.0 (197.6<br>to 376.5)</td><td>209 (131 to 327)</td><td>335.5 (210.3<br>to 524.9)</td><td>156 (81 to 264)</td><td>535.5 (278.1<br>to 906.3)</td><td>139 (66<br>to 249)</td><td>288.9 (137.2<br>to 517.5)</td><td>35 (16 to 63)</td><td>65.1 (28.9<br>to 118.0)</td></tr><tr><td>MSM with European<br>nationality</td><td>206 (142<br>to 304)</td><td>13,611</td><td>151.3 (104.3<br>to 223.3)</td><td>98 (53 to 185)</td><td>233.9 (126.5<br>to 441.6)</td><td>64 (21 to 130)</td><td>348.8 (114.4<br>to 708.4)</td><td>45 (13<br>to 104)</td><td>130.3 (37.6<br>to 301.2)</td><td>x</td><td>x</td></tr><tr><td>MSM with non-<br>European nationality</td><td>303 (199<br>to 490)</td><td>5671</td><td>534.3 (350.9<br>to 864.0)</td><td>111 (59 to 208)</td><td>543.9 (289.1<br>to 1019.1)</td><td>92 (44 to 177)</td><td>853.4 (408.2<br>to 1641.9)</td><td>94 (32<br>to 196)</td><td>691.7 (235.5<br>to 1442.2)</td><td>x</td><td>x</td></tr><tr><td>Heterosexual women</td><td>842 (678<br>to 1049)</td><td>3,433,067</td><td>2.5 (2.0 to 3.1)</td><td>x</td><td>x</td><td>282 (168 to 488)</td><td>5.1 (3.1 to 8.9)</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Heterosexual women<br>with foreign<br>nationality</td><td>641 (512<br>to 786)</td><td>442,352</td><td>14.5 (11.6<br>to 17.8)</td><td>189 (127 to 285)</td><td>12.7 (8.5<br>to 19.1)</td><td>174 (100 to 329)</td><td>27.0 (15.5<br>to 51.0)</td><td>16.9 (115<br>to 269)</td><td>15.5 (10.5<br>to 24.6)</td><td>120 (85 to 162)</td><td>10.0 (7.1<br>to 13.5)</td></tr><tr><td>Heterosexual women<br>with SSA nationality</td><td>490 (385<br>to 643)</td><td>28,330</td><td>173.0 (135.9<br>to 227.0)</td><td>146 (97 to 222)</td><td>147.4 (97.9<br>to 224.1)</td><td>135 (65 to 306)</td><td>331.8 (159.7<br>to 752.0)</td><td>123 (78<br>to 210)</td><td>200.5 (127.1<br>to 342.3)</td><td>109 (75 to 147)</td><td>132.2 (90.6<br>to 178.5)</td></tr><tr><td>Belgian heterosexual<br>women</td><td>201 (115<br>to 373)</td><td>2,990,715</td><td>0.7 (0.4 to 1.2)</td><td>x</td><td>x</td><td>108 (37 to 265)</td><td>2.2 (0.8 to 5.5)</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Heterosexual men</td><td>723 (557<br>to 956)</td><td>3,297,805</td><td>2.2 (1.7 to 2.9)</td><td>x</td><td>x</td><td>161 (99 to 269)</td><td>3.0 (1.9 to 5.0)</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Heterosexual men with<br>foreign nationality</td><td>437 (325<br>to 641)</td><td>439,275</td><td>9.9 (7.4 to 14.6)</td><td>195 (112 to 334)</td><td>13.7 (7.8<br>to 23.5)</td><td>102 (59 to 198)</td><td>15.4 (8.9<br>to 29.8)</td><td>95 (51<br>to 161)</td><td>8.7 (4.7<br>to 16.5)</td><td>61 (32 to 127)</td><td>5.0 (2.6<br>to 10.4)</td></tr><tr><td>Heterosexual men with<br>SSA nationality</td><td>250 (173<br>to 387)</td><td>26,843</td><td>93.1 (64.4<br>to 144.2)</td><td>105 (53 to 192)</td><td>103.5 (52.2<br>to 189.2)</td><td>46 (20 to 101)</td><td>117.6 (51.1<br>to 258.1)</td><td>69 (33<br>to 143)</td><td>120.2 (57.5<br>to 249.0)</td><td>40 (21 to 82)</td><td>56.6 (28.7<br>to 116.4)</td></tr><tr><td>Belgian heterosexual<br>men</td><td>285 (195<br>to 413)</td><td>2,858,530</td><td>1.0 (0.6 to 1.4)</td><td>x</td><td>x</td><td>59 (25 to 118)</td><td>1.3 (0.5 to 2.5)</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Persons who inject<br>drugs</td><td>47 (24<br>to 84)</td><td>25,673</td><td>18.3 (9.3 to 32.7)</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td></tr></tbody></table>

Men who have sex with men (MSM) population was defined as 4.18% of male population [14] of each geographical and nationality group. SSA: Sub-Saharan African; Persons who inject drugs were distributed among 79.6% of men and 20.4% of women in each geographical and nationality group [15]. x: values could not be estimated due to insufficient HIV cases; Over 2010 to 2015, newly diagnosed MSM with non-European nationality were originating from Latin America (35%), Sub-Saharan Africa (31%), Asia (21%), North Africa (8%) and North America (5%).
In Belgium, in particular in areas hosting the two biggest cities, MSM with foreign nationality, especially those with non-European nationality, was the most affected population. Over 2010 to 2015, newly diagnosed MSM with foreign nationality were originating from Europe (52%), Latin America (17%), Sub-Saharan Africa (15%), Asia (10%), North Africa (4%) and North America (2%). In the French aforementioned study, born-abroad MSM were originating from Haiti or the Americas (28%), Europe (26%), Sub-Saharan Africa (19%), North Africa, Asia or Oceania (26%) [10]. In Spain, the population of MSM born in Latin America was found to be particularly vulnerable to both HIV and other sexually transmitted infections [19]. Another study in Spain investigating the factors associated with late presentation of HIV infection for MSM found a significant interaction between the region of birth and the educational level: MSM born in Africa or Latin America with low educational level had higher odds of presenting late [20].

Our findings also show high prevalence of undiagnosed population in densely populated areas, like Brussels and the province of Antwerp. The data did not enable obtaining estimates for other Belgian cities. This calls for further examination of the link between prevalence of undiagnosed HIV infection and urbanization level in the future in order to fine-tune the testing strategies accordingly.

The accuracy of our estimates may have been limited by several sources of uncertainty. First, potential inaccurate adjustment for missing entries (e.g. 59% missing for AIDS status at diagnosis) might affect the results. Second, potential overestimation of the number of new HIV diagnoses, and in turn overestimation of the undiagnosed HIV prevalence, among individuals with foreign nationality, may occur because some individuals might have been diagnosed and treated before migrating to Belgium, especially among those from Western Europe [21,22]. Third, prevalence rate estimates are dependent of population size estimates, which remain uncertain for MSM and IDU, especially for those with foreign nationality. Fourth, changes in test-seeking behaviours over time are not fully accounted for in the model. They are accounted for whenever an increase in test-seeking rate (respectively a decrease) induces an increase (respectively a decrease) in the proportion of recent infection among newly diagnosed HIV cases (see full details in the supplementary material). Fifth, we neglected pre-HIV diagnosis mortality, which may have led to slightly underestimating the number of undiagnosed HIV infections, especially among persons who inject drugs whose risk of death from overdose, before being diagnosed with HIV, may not be negligible. However, the size of the population who inject drugs as well as the proportion of persons who inject drugs among newly diagnosed HIV cases are small (1.7%, i.e. 47 cases, in 2015), thus even if we underestimated the number of persons who inject drugs unaware of their HIV infection, this would not change the general pattern of the HIV undiagnosed population in Belgium. Sixth, we did not account for human mobility within Belgium before HIV diagnosis. However, data from the Belgian Statistical Office (Statbel, personal communication) show that this mobility is low.

5 | CONCLUSIONS

Our findings show that, in Belgium, heterogeneity exists in
how geographic areas and populations are affected by HIV

and emphasizes the need to tailor prevention programs and
develop a comprehensive HIV testing strategy. It shows that
the Sub-Saharan African population is at high risk of undiag-
nosed HIV infection, as observed in surrounding countries
[10], and particularly highlights that MSM with foreign
nationality are disproportionately affected by HIV. MSM with
foreign nationality may combine several HIV vulnerabilities
related to migration (e.g. hardship [23], barrier to HIV test-
ing) and being MSM (e.g. higher HIV transmission risk
through anal intercourse, high sexual activity), and should be
given more attention in Belgium, and more generally in
Europe.

AUTHORS’ AFFILIATIONS

¹Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, Paris, France; ²Sciensano (Scientific Institute of Public Health), Epidemiology of Infectious Diseases, Brussels, Belgium

COMPETING INTERESTS

VS has served on advisory boards for ViiV Healthcare (2016) and Gilead (2018) and reports lecture fees from MSD (2014), Gilead (2014, 2015, 2017), Abbvie (2018) and Janssen (2018), outside the submitted work. DC reports grants from Janssen-Cilag (2017 to 2018), Merck-Sharp & Dohme-Chibret (2015 to 2017), ViiV (2015), personal fees from Janssen-Cilag (2016, 2018) and Merck-Sharp & Dohme-Chibret (2015, 2017) for lectures, personal fees from ViiV (2015), for travel/accomodations/meeting expenses, personal fees from Gilead France from 2011 until December 2015 for French HIV board, personal fees from Innavirvax (2015 and 2016) and Merck Switzerland (2017) for consultancy, outside the submitted work. LM, DVB, CO, JD and AS declare no conflicts of interest.

AUTHORS’ CONTRIBUTIONS

LM and VS designed the research. LM performed the research. LM and VS drafted the manuscript. All authors analysed the data and critically revised the manuscript for important intellectual content.

ACKNOWLEDGEMENT

FUNDING

This work was supported by the ANRS (France) and by IWT (Belgium) and through the framework of HIVERA JTC 2014. The sponsor had no role in the study. LM is grateful for the financial support of the ANRS, in the form of a postdoctoral research fellowship.

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## SUPPORTING INFORMATION

Additional information may be found under the Supporting Information tab for this article.

**Table S1.** Percentage of missing values, types of variable and imputation methods used for each imputed variable

**Figure S1.** Mean annual number of new HIV diagnoses and proportions of recent infection and AIDS at diagnosis from 2006 to 2015 in Belgium, at the national level and in four geographic areas.

## APPENDIX

### HERMETIC STUDY GROUP

Hanne Apers (ITM, Belgium), Jessika Deblonde (Sciensano, Belgium), Anda Kıvıte (RSU, Latvia), Jasna Loos (ITM, Belgium), Lise Marty (INSERM U1136, France), Christiana Nöstlinger (ITM, Belgium), Daniela Rojas Castro (Coalition Plus, France), Virginie Supervie (INSERM U1136, France), Dominique Van Beckhoven (Sciensano, Belgium).