To control the HIV epidemic, it is necessary to develop, plan and implement innovative strategies to find undiagnosed persons and engage them in care and treatment. A preliminary step to reach this objective is to regularly monitor the size and characteristics of the undiagnosed population and properly address interventions, i.e. according to age, sex and sexual behavior.
Broadly, three approaches have been used to estimate the number of people with undiagnosed HIV: prevalence surveys , extended back-calculation on HIV and AIDS data [2,3], and other statistical models based on the synthesis of various data sources [4,5]. All methods are quite complex and demand surveillance systems that have been in place for a long time, along with population-based or community-based surveys.
In Italy, national surveillance of new HIV diagnoses was only established in 2008, and prevalence surveys are not routinely carried out; thus, given the available data, it is necessary to consider alternative methods to estimate the undiagnosed population.
The objective of this study was to provide an estimate of the undiagnosed HIV population in Italy in 2012, also stratified by sex, HIV exposure category, age group, and country of birth, using a simple approach of combining routine surveillance data with estimates of progression to AIDS before HAART introduction. An estimate of the overall prevalence of persons living with HIV in Italy in 2012 was also provided.
The method is a modification of the ‘London method 2’ [6–8] and it does not require surveillance data from the beginning of the HIV epidemic. Very briefly, the undiagnosed HIV population is estimated as the ratio between the estimated annual number of simultaneous HIV/clinical AIDS diagnoses and the expected annual progression rate to AIDS in the undiagnosed population. This progression rate was estimated considering the annual AIDS incidence by CD4+ cell count in untreated patients before the introduction of HAART (as derived from by cohort studies, ) and the CD4+ cell count distribution of newly diagnosed asymptomatic HIV reported to surveillance.
Thus, the annual number of cases with undiagnosed HIV, n, can be estimated by:
where S is the estimate of simultaneous HIV/clinical AIDS diagnoses, pi is the proportion of patients in CD4+-stratum i among asymptomatic, ri is the annual AIDS rate in CD4+-stratum i, k is the number of CD4+ cell count classes considered (see http://links.lww.com/QAD/A872).
We considered new HIV diagnoses in 2012, reported by June 2014, to the Italian HIV surveillance system . Available information were date of birth, sex, nationality, date of diagnosis, reasons for testing, HIV exposure category, CD4+ cell count and clinical stage at HIV diagnosis (i.e. asymptomatic stage, symptomatic non-AIDS stage, AIDS stage).
Also, different scenarios about under-reporting/ascertainment of HIV/AIDS were considered to estimate the undiagnosed population.
We estimated the overall HIV prevalence in Italy in 2012 through several steps: the number of people diagnosed and in care in 2012 was retrieved from a national survey . The total number of people diagnosed was obtained considering the estimated percentage of people not retained in care according to a survey performed in three Italian infectious disease clinics . Finally, the number of people living with HIV was obtained by adding the estimates of undiagnosed HIV cases to the estimates of diagnosed.
Analyses were performed using R software 3.1.0 ; multiple imputation was implemented through mi R-package .
There were 4082 new HIV diagnoses in adults (≥15 years of age) reported to the Italian National Surveillance System by July 2014 with an HIV test date in the year 2012. In total 13.8% of the cases had an unknown HIV exposure category, 21.6 and 28.4% had a missing CD4+ cell count and clinical stage respectively (Supplementary Table 1, http://links.lww.com/QAD/A872).
Table 1 shows the distribution of new HIV diagnosis by clinical stage and CD4+ cell count at diagnosis and estimated annual rate to clinical AIDS in people with undiagnosed HIV (0.076 and 0.078 for raw and imputed data, respectively). This means that 7.6–7.8% of undiagnosed HIV positive cases would progress to AIDS, unless they were diagnosed with HIV before the end of the year.
Table 2 shows the estimates of simultaneous HIV/clinical AIDS diagnoses, the number expected to progress to AIDS in that year unless they were diagnosed before, and the number of undiagnosed HIV cases in Italy in 2012 considering three different scenarios about under-reporting. Estimates are stratified according to HIV exposure category, sex, nationality and age group.
The median estimates of the number of undiagnosed cases vary between 13 729 [95% confidence interval (CI): 12 152–15 592] and 16 475 (95%CI: 14 581–18 710) under the hypothesis of no under-reporting and under-reporting of 20%, respectively; estimates stratified by HIV exposure category evidenced that MSM had the highest estimate of undiagnosed cases whereas the injection drug users (IDU) was the category with the lowest estimate; regarding sex, estimates of undiagnosed HIV+ men were around 4.5 times more than those for women; three times higher for people born in Italy than for those born abroad; regarding age, the highest estimate of undiagnosed cases was for the 35–44 years old group.
Finally, we estimated that the undiagnosed HIV population in Italy was around 11–13% for an overall prevalence of about 125 000–130 000 cases; regarding the exposure category, it is of note that the estimated percentage of undiagnosed HIV among IDU was around 3% whereas the estimated percentage among MSM accounted for about 16–19%; regarding sex, the estimated percentage was about 7–8% in women compared with 13–15% in men; with respect to the country of birth, the estimated percentage of undiagnosed cases was higher in those born abroad (16–19%) than those born in Italy (10–12%); regarding age groups, the percentage of undiagnosed cases was particularly high among those aged below 25 years old (25–28%) whereas the lowest percentage was among those aged 50–59 years old (Supplementary Table 2, http://links.lww.com/QAD/A872).
The magnitude and characteristics of the undiagnosed HIV population is important for focusing intervention strategies and prevention initiatives. Reducing the prevalence of undiagnosed HIV infection and increasing the proportion of HIV-infected individuals who are aware of their status are important for HIV prevention, as the transmission rate from the unaware group was estimated to be 3.5 times higher than the aware group  and that persons who are undiagnosed accounted for almost one-third of transmissions . Also, it was estimated that about eight transmissions would be averted per 100 persons newly aware of their infection .
Thus, increasing the number of HIV-infected persons who are diagnosed and linked with effective care and prevention programs may have the potential to significantly reduce new HIV infections over time. In addition, diagnosed persons can benefit from clinical treatments to prevent immune system damage and opportunistic infections.
Using routine surveillance data, we estimated that there were about 12 000–18 000 people in Italy with undiagnosed HIV, corresponding to 11–13% of the overall prevalent population with HIV. This estimate is lower than that obtained (19%) from routine HIV testing among persons attending clinics for sexually transmitted infections (STI), in 2013 . This estimate could be over-biased because of possible nondisclosure of HIV status due to convenience and discomfort in disclosing risky sexual behavior to healthcare professionals [19,20].
The percentage of undiagnosed cases was lower for IDU (around 3.0%) and higher for MSM (16–19%); the low percentage among IDU is likely attributable to routine HIV testing for all people attending drug treatment services since the 1980s, and to the decreasing incidence of new HIV infections in this group . Stratified estimates also evidenced a higher percentage of undiagnosed men compared with women, in people born abroad compared with those born in Italy, and in people below 25 years old compared with the other age groups.
The approach we used is simpler than other statistical methods requiring historical data about new HIV/AIDS diagnoses; in particular, only annual routine surveillance data for new HIV diagnoses such as CD4+ cell count, presence/absence of HIV/AIDS-related symptoms are needed to implement the method. Thus, it could be used for example to evaluate if a particular strategy (e.g. expanded HIV testing in men older than 45, opt-out testing in specific health facilities, rapid-test testing among young people) leads to a reduction of the undiagnosed population from one year to another. A similar approach, (although with different assumptions) the so-called ‘London 1 method’, was recently proposed  to estimate the number of undiagnosed in the lower CD4+ cell count range (i.e. <200 or <350 cells/μl).
The estimated overall prevalence of undiagnosed HIV-infected people in Italy are in line with those recently obtained in other countries using statistical/mathematical models: 16–18% in 2009–2010 in United States [23,24], 20% in 2010 in France , 17% in United Kingdom in 2014 , 9.4% in Australia in 2013 . For MSM these percentage were 15% in 2011 in the Netherlands , 13% in Switzerland in 2010 , 22% in England and Wales in 2010 .
Some issues should be considered. The method mainly relies on the assumption that the CD4+ cell count distribution in the undiagnosed population is similar to that of newly diagnosed asymptomatic people. This assumption cannot be verified, although the distribution of CD4+ cell count in asymptomatic people we used was not particularly different from the CD4+ cell count distribution estimated in undiagnosed [26,28], whereas it appeared to be different from that reported in  (Supplementary Table 3, http://links.lww.com/QAD/A872). To evaluate the impact of the assumption on the estimates we performed some simulations assuming that the undiagnosed HIV people had ± 5%, ± 10% and ± 15% CD4+ cell counts, compared with the new asymptomatic HIV diagnoses; the estimates varied up to ± 2500 cases, corresponding up to ± 2% on the overall prevalence.
Another issue is also the possible under-reporting/ascertainment of both AIDS and HIV diagnoses. Assuming that under-reporting/diagnosis was not different by clinical stage, CD4+ at diagnosis and the other characteristics considered, a 10% (20%) increase of under-reporting/diagnosis results in about 1% (2%) increase in prevalence of undiagnosed infections (Supplementary Table 2, http://links.lww.com/QAD/A872).
We assumed that the progression rates depend only on CD4+ cell count and that the estimates before introduction of HAART could be similar to current AIDS progression rates among the undiagnosed. Our method did not consider age  as determinant of progression before introduction of HAART, although such a development could be incorporated to refine the estimates.
Finally, to estimate the overall prevalence of people living with HIV, we also considered the percentage of those not retained in care. This percentage was considered equal in all subgroups evaluated.
In conclusion, the estimated undiagnosed HIV population in Italy is in a plausible range of 12 000–18 000 cases, corresponding to 11–13% of the overall prevalence. The approach described can also be easily implemented in other countries where HIV surveillance systems have routinely been collecting data on CD4+ cell count and clinical stage at diagnosis.
We thank Nicoletta Orchi, Assunta Navarra, Claudio Angeletti, (‘L. Spallanzani’ National Institute for Infectious Diseases, IRCCS, Rome, Italy), Mariangela Raimondo (Istituto Superiore di Sanità, Rome, Italy) who provided comments in the preliminary discussion about this work.
We thank also Matthias An der Heiden (Germany), Andre Sasse (Belgium), Zheng Yin (United Kingdom), Stephane Le Vu (France), Anastasia Pharris (ECDC), who provided the CD4+ cell count distribution among newly diagnosed HIV-infected cases reported to the national surveillance system.
We are extremely grateful to Andrew Phillips and Rebecca Lodwick who provided their insight on the initial idea of the approach we used here; and Kholoud Porter who provided the AIDS rate by CD4+ cell count from CASCADE Collaboration.
Source of funding: The Ministry of Health, Progetto di Ricerca RF-IMI-2009-1302855 – Conv.ne I.S.S. n 40H78. A.M. is also the recipient of an unrestricted grant from Gilead (2014 Fellowship Program).
A.M., P.P., and E.G. conceived the initial idea and the study design; A.M. and P.P. implemented the model and drafted the manuscript; V.R. and L.C. managed the database of National Surveillance System and extracted the data; B.S. coordinated the National Surveillance System and contributed to data interpretation; V.P. and G.I. contributed to data interpretation; A.M. and P.P. revised the manuscript. All authors read and approved the final manuscript.
Conflicts of interest
There are no conflicts of interest.
Preliminary results were presented in the abstract at the 7th Congress of the Italian Society of Medical Statistics and Clinical Epidemiology (SISMEC), 25–28 September 2013, Rome, Italy; abstract oral communication 18 at the 6th Italian Conference on AIDS & Retroviruses (ICAR), 25–27 May 2014, Rome, Italy.
1. McGarrigle CA, Cliffe S, Copas AJ, Mercer CH, De Angelis D, Fenton KA, et al. Estimating adult HIV prevalence in the UK in 2003: the direct method of estimation
. Sex Transm Infect
2. Campsmith ML, Rhodes PH, Hall HI, Green TA. Undiagnosed HIV prevalence among adults and adolescents in the United States at the end of 2006
. J Acquir Immune Defic Syndr
3. Wand H, Yan P, Wilson D, McDonald A, Middleton M, Kaldor J, et al. Increasing HIV transmission through male homosexual and heterosexual contact in Australia: results from an extended back-projection approach
. HIV Med
4. Goubar A, Ades AE, De Angelis D, McGarrigle C, Mercer C, Tookey P, et al. Estimates of HIV prevalence and proportion diagnosed based on Bayesian multiparameter synthesis of surveillance data
5. Presanis AM, Gill ON, Chadborn TR, Hill C, Hope V, Logan L, et al. Insights into the rise in HIV infections, 2001 to 2008: a Bayesian synthesis of prevalence evidence
6. Lodwick RK, Sabin CA, Phillips AN. Estimation of the number of undiagnosed HIV-positive people within a region based on surveillance of simultaneous HIV/AIDS diagnoses [PE18. 1/5]
. Presented at: 12th European AIDS Conference
7. Lodwick RK, Sabin CA, Phillips AN. A method to estimate the number of people in a country or region with HIV who are undiagnosed and in need of ART [P165]
. Presented at: 10th International Congress on Drug Therapy in HIV Infection
8. Working Group on Estimation of HIV Prevalence
in EuropeHIV in hiding: methods and data requirements for the estimation of the number of people living with undiagnosed HIV
9. Guiguet M, Porter K, Phillips A, Costagliola D, Babiker A. Clinical progression rates by CD4 cell category before and after the initiation of combination antiretroviral therapy (cART)
. Open AIDS J
10. Camoni L, Boros S, Regine V, Santaquilani M, Ferri M, Pugliese L, et al. Aggiornamento delle nuove diagnosi di infezione da HIV e dei casi di AIDS in Italia al 31 dicembre 2013
. Not Ist Super Sanità
2014; 27 (9 Suppl 1):3–48.
11. Camoni L, Raimondo M, Dorrucci M, Regine V, Salfa MC, Suligoi B. Estimating minimum adult HIV prevalence: a cross-sectional study to assess the characteristics of people living with HIV in Italy
. AIDS Res Hum Retroviruses
12. Zona S, Lazzaretti C, Borghi V, Salsi E, Franceschini E, Bini T, et al. Characteristics of HIV-infected patients not retained in care of three cohorts in of Italy
. 7th Italian Conference on AIDS & Retroviruses (ICAR)
2015; Riccione, Italy, P140, p. 71.
13. R Core Team [computer program]. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2012. http://www.R-project.org/
[Accessed 7 June 2014].
14. Su Y, Gelman A, Hill J, Yajima M. Multiple imputation with diagnostics (mi) in R: opening windows into the black box
. J Stat Softw
15. Marks G, Crepaz N, Janssen RS. Estimating sexual transmission of HIV from persons aware and unaware that they are infected with the virus in the USA
16. Skarbinski J, Rosenberg E, Paz-Bailey G, Hall HI, Rose CE, Viall AH, et al. Human immunodeficiency virus transmission at each step of the care continuum in the United States
. JAMA Intern Med
17. Hall HI, Holtgrave DR, Maulsby C. HIV transmission rates from persons living with HIV who are aware and unaware of their infection
18. Salfa MC, Regine V, Ferri M, Suligoi S. Sentinel surveillance of sexually transmitted infections based on a network of clinical centres and Sentinel surveillance of sexually transmitted infections based on a network of microbiology laboratories
. Not Ist Super Sanità
19. Datta J, Hickson F, Reid D, Weatherburn P. STI testing without HIV disclosure by MSM with diagnosed HIV infection in England: cross-sectional results from an online panel survey
. Sex Transm Infect
20. Savage EJ, Lowndes CM, Sullivan AK, Back DJ, Else LJ, Murphy G, Gill ON. Effect of nondisclosure of HIV status in sexual health clinics on unlinked anonymous HIV prevalence estimates in England, 2005–2009
2015; [Epub ahead of print].
21. Mammone A, Pezzotti P, Angeletti A, Orchi N, Carboni A, Navarra A, et al. HIV incidence estimate combining HIV/AIDS surveillance, testing history information and HIV test to identify recent infections in Lazio, Italy
. BMC Infect Dis
22. Lodwick RK, Nakagawa F, van Sighem A, Sabin C, Phillips AN. Use of surveillance data on HIV diagnoses with HIV-related symptoms to estimate the number of people living with undiagnosed HIV in need of antiretroviral therapy
. PLoS One
23. Hall HI, Frazier EL, Rhodes P, Holtgrave DR, Furlow-Parmley C, Tang T, et al. Differences in human immunodeficiency virus care and treatment among subpopulations in the United States
. JAMA Intern Med
24. Hall HI, Song R, Szwarcwald CL, Green T. Brief report: Time from infection with the human immunodeficiency virus to diagnosis, United States
. J Acquir Immune Defic Syndr
25. Supervie V, Ndawinz JDA, Lodi S, Costagliola D. The undiagnosed HIV epidemic in France and its implications for HIV screening strategies
26. Skingsley A, Yin Z, Kirwan P, Croxford S, Chau C, Conti S, et al. HIV in the UK – Situation Report 2015: data to end 2014
. Public Health England, London
27. Jansson J, Kerr CC, Mallitt KA, Wu J, Gray RT, Wilson DP. Inferring HIV incidence from case surveillance with CD4+ cell counts
2015; [Epub ahead of print].
28. van Sighem A, Nakagawa F, De Angelis D, Quinten C, Bezemer D, de Coul EO, et al. Estimating HIV incidence, time to diagnosis, and the undiagnosed HIV epidemic using routine surveillance data
29. Birrell PJ, Gill ON, Delpech VC, Brown AE, Desai S, Chadborn TR, et al. HIV incidence in men who have sex with men in England and Wales 2001–10: a nationwide population study
. Lancet Infect Dis
30. van Sighem A, Vidondo B, Glass TR, Bucher HC, Vernazza P, Gebhardt M, et al. Swiss HIV Cohort Study. Resurgence of HIV infection among men who have sex with men in Switzerland: mathematical modelling study
. PLoS One
31. Phillips A, Pezzotti P. CASCADE CollaborationShort-term risk of AIDS according to current CD4 cell count and viral load in antiretroviral drug-naïve individuals and those treated in the monotherapy era