Download Factors associated with late HIV diagnosis in the North

Factors associated with late HIV diagnosis in North-East Scotland: a six-year retrospective study
Noble G1, Okpo E 1, 2, Tonna I 3, Fielding S 1
1
Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, Scotland, UK,
2
NHS Grampian, Public Health Department, Summerfield House, 2 Eday Road, Aberdeen, AB15 6RE
3
Aberdeen Royal Infirmary, Infectious Diseases Unit, Emergency Care Centre, Aberdeen, UK
Keywords: HIV; late diagnosis; missed diagnosis; CD4 cell count
Corresponding author: Dr Emmanuel Okpo, Consultant in Public Health Medicine, NHS Grampian,
Public Health Department, Summerfield House, 2 Eday Road, Aberdeen, AB15 6RE
Email: [email protected], Telephone: 01224 558429
1
Abstract
Objectives
Late HIV diagnosis is associated with increased morbidity and mortality, increased risk of transmission,
impaired response to antiretroviral therapy and increased healthcare costs. The aim of this study was
to determine the factors associated with late HIV diagnosis in Grampian, North-East Scotland.
Study design
A population based retrospective database analysis.
Methods
All newly diagnosed HIV positive individuals in Grampian, North – East Scotland between 2009 and
2014 were included in the study. Participants were classified as having a late diagnosis if the CD4 cell
count at presentation was less than 350 cells/mm3. Socio-economic and demographic factors were
investigated in relation to outcome (late diagnosis) using Chi-squared and Mann-Whitney Tests.
Results
CD cell count results were available for 111 (89.5%) of the 124 newly diagnosed individuals during the
study Period. The prevalence of late diagnosis was 53.2% (n=59). Those infected via heterosexual
mode of transmission had a 2.83 times higher odds of late diagnosis (OR 2.83 (95% CI; 1.10, 7.32))
than men who have sex with men (MSM) and those with no previous HIV testing had a 5.46 increased
odds of late diagnosis (OR 5.46 (95% CI; 1.89, 15.81)) compared to those who had previously been
tested. Missed opportunities for HIV diagnosis were identified in 16.3% (n=15) of participants.
Conclusion
Heterosexual individuals and those with no previous HIV testing were more likely to be diagnosed late.
Targeted initiatives to increase perception of HIV risk and uptake of testing in these risk groups are
recommended.
2
Introduction
Human immunodeficiency virus (HIV) infection is now a treatable medical condition with the
introduction of effective antiretroviral therapy (ART) which has improved clinical outcomes for people
living with the infection. Despite this, there continues to be avoidable morbidity and mortality
associated with HIV in the United Kingdom (UK) [1]. Undiagnosed, untreated and advanced infection
facilitates onward HIV transmission and impinges upon both individual and wider population health.
In 2013, around 107,800 people were estimated to be living with HIV in the UK and it is estimated that
about a quarter (24%) of these people are unaware of their infection status [2]. All persons living with
HIV are at some risk of transmitting HIV to others; those that are unaware of their infection are
particularly important because they unknowing could infect others without realising [2].
Late HIV diagnosis has been associated with increased morbidity [3] and mortality [4], blunted
response to ART [5] and increased healthcare costs [6, 7]. Late diagnosis has been identified as a major
risk factor for early intensive care unit admission in HIV infected patients [8] and is the largest
remediable factor for HIV-associated deaths in the UK [4]. A national audit of deaths amongst HIVpositive adults determined that 24% of deaths were directly attributable to late diagnosis of HIV [4].
The UK Collaborative HIV Cohort (UK CHIC) study found that commencing ART at a CD4 cell count of
less than 350 cells/mm3 resulted in up to 15 years loss of life [9]. Healthcare costs in the year preceding
diagnosis have been found to be 200% higher for those who present late [6]. Furthermore, knowledge
of HIV status has been linked to a reduction in risk-taking behaviour and thus it has been suggested
that the HIV/AIDS epidemic could be reduced substantially by increasing the number of HIV-positive
individuals who are aware of their status [10].
Current literature shows that many ‘late presenters’ were seen in the recent past by healthcare
professionals but failed to be diagnosed [11] and a national audit conducted in 2010 by the British HIV
Association identified that a quarter of patients had a missed opportunity for diagnosis [12].
3
In 2014 UNAIDS established a new global 90-90-90 treatment target for HIV; by 2020, 90% of all people
living with HIV will know their HIV status, 90% of all people with diagnosed HIV infection will receive
sustained ART and 90% of all people receiving ART will be suppressed [13]. Given recent reports that
suggest that around a quarter of people living with HIV in the UK are currently unaware of their
infection status [2], it is clear that late diagnosis of HIV is an important healthcare issue requiring
address in order to meet the 90-90-90 target and improve health outcomes for people living with HIV.
Whilst several studies have been conducted worldwide to investigate late or missed diagnosis of HIV,
limited studies have been conducted within the UK. An observational study of 1,536 newly diagnosed
HIV patients in Brighton found that older adults, aged 50 years or older, were more likely to present
late and had a higher mortality. The authors additionally identified African origin and being a male
heterosexual as factors associated with late presentation [14]. Within North-East Scotland only one
relevant study has been published to date [15]. However, this study focused primarily on missed HIV
diagnoses and compared the diagnostic pathways of two earlier cohorts of patients; 1995 to 2000 and
2004 to 2009.
A recent search of PubMed did not reveal any study in the North-East of Scotland that explored factors
associated with late HIV diagnosis. Thus, this study aims to examine the factors associated with late
diagnosis of HIV in Grampian with the aim to provide research evidence and basis for the development
of targeted interventions and educational programmes for earlier diagnosis of HIV in North-East
Scotland.
4
Methods
Study design and setting
The study was a population based retrospective database analysis. It involved the analysis of routinely
collected HIV surveillance data from the Health Protection Scotland (HPS) HIV database and the review
of case notes of all newly diagnosed HIV patients’ during the study period.
The HPS HIV database was set up in the early 1980s to collate data on all newly diagnosed HIV antibody
positive individuals and AIDS cases to monitor trends in diagnosed HIV infection and AIDS cases among
the Scottish population and to provide timely and useful information for the targeting of health
promotion, the evaluation of preventive measures, and the planning of medical and social services for
those affected by HIV. Information on newly diagnosed HIV/AIDS infection come from reporting of all
newly diagnosed HIV infections by virology laboratories in Scotland and AIDS diagnoses by clinicians.
In addition, the General Register Office for Scotland (GROS) reports all deaths that record AIDS or HIV
among the causes of death to HPS. Records of HIV diagnosis, AIDS and death, which are regarded as
relating to the same individual, are merged to create one record in the database [16].
Setting
The setting of the study was Grampian, North-East Scotland. In 2015, the population of Grampian was
estimated to be 587, 820. Around 10% of the workforce is employed directly by the oil and gas
industry. The oil and gas workforce is very mobile with frequent contacts and activities in countries
where the prevalence of HIV is high. In addition, Aberdeen has two prestigious universities that offer
postgraduate degree programmes in oil and gas engineering and law that are popular among students
from countries where the prevalence of HIV is high [17].
5
HIV care within the National Health Service (NHS) Grampian Health Board is shared between the
Genitourinary Medicine (GUM) and Infectious Diseases (ID) departments; inpatient care is provided in
Aberdeen Royal Infirmary’s regional ID unit and outpatient care is delivered by both GUM and ID.
Definitions
The primary outcome of interest was late HIV diagnosis which, is defined by The European Late
Presenter Consensus Working Group as; persons diagnosed or presenting for HIV care with a CD4
count below 350 cells/mm3 (within 3 months of diagnosis) or presenting with an AIDS-defining event,
regardless of CD4 count [18]. Secondary outcomes were missed opportunities for diagnosis of HIV.
This was defined as failure to diagnose HIV within three months of the patient presenting with a clinical
indicator disease or AIDS-defining illness. Definitions of clinical indicator disease and AIDS-defining
illness were adopted from the British HIV Association (BHIVA) UK National Guidelines for HIV testing
[1].
Definitions for ethnicity were adopted from the Scottish Census [19]. Patient postcodes were utilised
to ascertain the appropriate 2012 Scottish Index of Multiple Deprivation (SIMD) quintile for each
patient [20] SIMD quintile was used as a proxy for socioeconomic status in the study with SIMD 1
representing the most deprived and SIMD 5 representing the least deprived areas.
Study population and data collection
All HIV positive patients aged 16 years and over, newly diagnosed in Grampian between January 2009
and December 2014, were included in the study. Patients were identified through the HPS HIV
database. Patients were excluded if they were aged less than 16 years. One HIV positive individual in
the custody of Her Majesty’s Prison Service was excluded on ethical grounds.
6
Ethical approval
The study has been carried out in compliance with the Declaration of Helsinki [21]. Ethical approval
was granted by the North of Scotland Research Ethics Committee (REC reference 15/NS/0056).
Statistical analysis
Chi-squared and Mann-Whitney tests were used to identify associations between possible predictor
variables and late HIV diagnosis. These included gender, age at diagnosis, SIMD, ethnicity, migrant
status, probable mode of transmission, region of exposure, registration with GP, contact with
healthcare professionals in the year preceding HIV diagnosis, previous HIV testing, clinical indicator
diseases in the five years preceding HIV diagnosis, hepatitis B/C co-infection and missed opportunity
for diagnosis. Multivariate logistic regression was then used to ascertain which of the identified
variables in combination were predictive for late HIV diagnosis. Variables were entered into a
multivariate model if p<0.05 in univariate analysis. There were a small number of missing data in this
study but to check the robustness of results we undertook a couple of sensitivity analyses which
assumed extremes for some variables and for others we made an informed assumption. For example
in sensitivity analysis 1, for missing ethnicity data, one person was assumed to be Black-African using
knowledge of their migrant status, and the remainder assumed to be white British. For SIMD, in both
sensitivity analyses we assumed that all missing data were SIMD 3. The number of missing data and
assumptions made in both analyses are listed in table 1.
All statistical analyses were carried out using IBM SPSS Statistics 22.0 [22].
Table 1: Assumptions used for sensitivity analyses
7
Results
Study Population
Of the 124 patients with a diagnosis of HIV during the study period, 111 (89.5%) had a valid CD4 count
recorded within 3 months of diagnosis and were included in the analysis. Of these; 68.5% (n=85) were
male, 25.0% (n=31) were aged 16-29 years, 25.0% (n=31) belonged to the least deprived socioeconomic group, 51.6% (n=64) were of White-British ethnicity, 51.6% (n=64) were UK Nationals, 51.6%
(n=64) had a heterosexual probable mode of transmission and 42.7% (n=53) were exposed in the UK
and Ireland. Characteristics of included patients are presented in Table 2.
Table 2: Socio-demographic characteristics of included participant at baseline and comparison of
late versus early diagnosis
Prevalence and factors associated with late HIV diagnosis
Overall, the prevalence of late diagnosis was 53.2% (n=59). In the univariate analysis, late diagnosis
of HIV was significantly associated with female gender (p=0.036), Black-African ethnicity (p=0.014),
being an immigrant (p=0.014), heterosexual mode of transmission (p=0.002), exposure in sub-Saharan
Africa (p=0.015), having undergone no previous HIV testing (p<0.001), having a number of clinical
indicator diseases (p=0.034) and co-infection with hepatitis B and/or C (p=0.013) ( Table 2).
In multivariate logistic regression, individuals with no previous HIV testing were more likely to be
diagnosed late compared to those who had previously been tested (adjusted OR 5.46 (95% CI; 1.89,
15.81)). Moreover, heterosexuals were 2.83 times more likely to be diagnosed late compared to MSM
(adjusted OR 2.83 (95% CI; 1.10, 7.32). Unadjusted and adjusted odds ratios and 95% confidence
intervals are presented in Table 3. The findings from the sensitivity analyses were consistent and
similar to the results of the original univariate and multivariate analyses.
8
Clinical indicator disease
Late diagnosis was observed in 59% of individuals in whom clinical indicator disease in the 5 years
preceding diagnosis was present (n=61) compared to 45.2% in those with no clinical indicator disease
in the 5 years preceding diagnosis (p=0.298).
Missed opportunity for HIV diagnosis
Missed opportunities for diagnosis were identified in 16.3% (n=15) of included participants. There
were more people diagnosed late and who had missed opportunities (73.3%) than those without
missed opportunities (50.6%); however this was not statistically significant (p=0.183).
Table 3: Factors associated with late diagnosis
9
Discussion
Our study showed that over half (53.2%) of the newly diagnosed HIV positive individuals in the 5-year
period between January 2009 and December 2014 had a CD4 cell count of less than 350 cells/mm3 at
the time of diagnosis or within 3 months of diagnosis. This proportion of late diagnosis appears to be
consistent with those found in other UK studies [12, 14-15].
In this study, we found that heterosexual mode of transmission and no previous HIV testing were
independently associated with late HIV diagnosis. Several studies [23-28] have shown that
heterosexual mode of transmission is associated with late HIV diagnosis. In contrast; only one existing
study identified previous HIV testing as an important factor in late HIV diagnosis. This study, carried
out in China, found that late HIV diagnosis was more common in individuals with no prior
consideration of HIV testing [29].
Interestingly in the univariate analysis, female gender was associated with late diagnosis contrary to
existing literature which suggests that men are more likely to be diagnosed late.
Only one other
existing study from Belgium identified female gender as a demographic feature of late diagnosis in
univariate analysis [30].This finding could be explained by the notion that late diagnosis is generally
more prevalent in individuals who are not perceived or do not perceive themselves to be at high risk
of HIV acquisition [31]. Furthermore, the fear of stigma and discrimination which still remains a
significant issue particularly among minority ethnic groups could be a barrier to testing [32]. In
addition inadequate partner notification may contribute to late diagnosis in females observed in this
study. A service evaluation conducted in NHS Grampian as part of an HIV Needs Assessment found
that some individuals reported the person from whom they most likely contracted HIV from had been
aware they were HIV positive but had not disclosed this information [33]. Although partner
10
notification has long been recognised as an important HIV prevention intervention there are a number
of challenges preventing its effectiveness [34].
Unlike other recent studies, this study found no evidence that injecting drug use was associated with
late diagnosis. This could be due to a well established needle exchange programme in Grampian thus
resulting in little or no HIV transmission in that risk group.
The finding of this study suggests that migrant populations, particularly those originating from subSaharan Africa (SSA), are an important demographic group to consider when addressing late HIV
diagnosis. This is consistent with surveillance data which indicates that migrants from HIV endemic
countries, particularly individuals from SSA, account for a disproportionate and rising number of HIV
infections in Western Europe [35] and majority have acquired their HIV infection in their country of
origin [36].
In this study, 16.3% of patients had a missed opportunity for diagnosis and late diagnosis was
associated with a higher number of clinical indicator diseases and co-existing hepatitis B/C infection.
Current evidence suggests that testing for HIV following diagnosis of an indicator disease significantly
decreases the risk of late HIV diagnosis [37]. Similar numerous missed previous opportunities for
testing in patients diagnosed late were reported in an audit carried out in Central Scotland [38] and a
review published in 2014 [39] reported non adherence to the 2008 UK national HIV testing guidelines
recommending a test for all patients with HIV indicator disease. Thus the finding of our study calls for
a renewed effort to raise awareness among clinicians regarding HIV indicator disease and late
diagnosis.
In existing literature, age at diagnosis has repeatedly been identified as a factor associated with late
diagnosis; with older individuals more likely to be diagnosed late [14, 23-25, 27-29, 40-41]. Notably in
the current study, age at diagnosis was not found to be predictive of late HIV diagnosis either at
11
univariate or multivariate analysis. Proportions of late diagnosis were relatively similar across the age
groups with the lowest proportion of late diagnoses (45.5%) observed in the 40-44 year age group and
the highest proportion (62.5%) observed in the 35-39 year age group. It is possible that this finding is
a result of the sample size and if more patients were included in the analysis greater disparities
between the age groups may be detected.
To address some the findings in this study, a number of interventions have already been implemented
in Grampian including a late diagnosis multidisciplinary subgroup [HIV clinical team, Public Health,
Primary care] to review all new cases of late diagnoses to identify any missed opportunity and
feedback to the service where the missed opportunity occurred, raising awareness of HIV clinical
indicators in primary care through educational programmes, opportunistic testing in a university
setting during ‘fresher’s week’ and offering of HIV test during new patient registration in primary care.
This study has some limitations. Firstly, it was a retrospective study with an aspect of case note review
and interrogation of several patient electronic management systems with incomplete and missing
data which could affect the result. Secondly, the sample size was small which limits comparability.
Conclusion
In conclusion, a good proportion of HIV positive patients continue to be diagnosed late. Heterosexual
route of transmission and having no previous HIV test were independently associated with late HIV
diagnosis. There is need to highlight the importance of early diagnosis among the public and
healthcare workers. Offering of testing in non-NHS (community-based) settings such as in universities
and faith-based organisations settings is recommended to increase uptake of HIV testing in hard to
reach groups and in those who have never had a test.
Acknowledgements
12
The authors would like to thank Dr’s Steve Baguley, Ambreen Butt, and Daniela Brawley for their help
with access to patient notes in GUM.
Funding
None
Author Contributions
This paper is an abridged version of a master’s thesis presented for the degree of Master of Science
(MSc) in Global Health and Management, University of Aberdeen by GN. EO, IT, SF supervised the
thesis. EO and IT developed the concept for the study. GN and SF conducted the statistical analysis.
All authors contributed significantly at every stage of the paper, read and approved the final
manuscript.
Conflict of interest
The authors have no competing interest to declare
13
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Table 1: Sensitivity analysis assumptions
Variable
Missing data
Gender
Age at diagnosis
SIMD
Sensitivity analysis 1
Sensitivity analysis 2
No missing data
-
-
No missing data
-
-
4
Missing data assumed to
be SIMD 3
Missing data assumed to be
SIMD 3
3
n=1 assumed to be BlackAfrican based on migrant
status.* Remaining missing
data assumed as WhiteBritish
n=1 assumed to be BlackAfrican based on migrant
status.* Remaining missing
data also assumed as BlackAfrican
3
n=1 assumed to be a
migrant based on
ethnicity.** Remaining
missing data assumed as
UK National
n=1 assumed to be a migrant
based on ethnicity.**
Remaining missing data
assumed as migrant
4
Missing data assumed to
be heterosexual
transmission
n=1 assumed to be
heterosexual as female.
Remaining missing data
assumed to be MSM
n=1 assumed to be
exposed in SSA based on
ethnicity.*** Remaining
missing data assumed as
UK and Ireland
n=2 assumed to be exposed
in the UK and Ireland and n=1
assumed to be exposed in
SSA based on ethnicity#
Remaining missing data
assumed as SSA
Missing data assumed as
not registered with GP
Missing data assumed as
registered with GP
Missing data assumed as
no contact with healthcare
professionals
Missing data assumed as
contact with healthcare
professionals
Missing data assumed to
be no previous HIV testing
Missing data assumed as
previous HIV testing
Ethnicity
Migrant status
Probable mode of
transmission
Region of exposure
5
Registration with GP
Contact with
healthcare
professionals in the
year preceding
diagnosis
Previous HIV testing
5
27
8
19
Clinical indicator
disease (present or
absent) in the five
years preceding
diagnosis
Co-existing hepatitis
B/C infection
Missed opportunity
for diagnosis
19
Missing data assumed as
no clinical indicator disease
Missing data assumed as
clinical indicator disease
present
8
Missing data assumed as
no hepatitis B/C coinfection
Missing data assumed as
hepatitis B/C co-infection
19
Missing data assumed to
be no missed opportunity
for diagnosis
Missing data assumed to be a
missed opportunity for
diagnosis
*n=1 assumed to be Black-African as cross tabulation of migrant status against ethnicity revealed that the
majority of migrants (69.8%) were of Black-African ethnicity.
**n=1 assumed to be a migrant based on results of ethnicity as cross tabulation of ethnicity against
migrant status showed that all (100.0%) patients of Black-African ethnicity were migrants.
***n=1 assumed to be exposed in SSA as cross tabulation of region of exposure and ethnicity revealed
the majority of Black-African patients (83.3%) were exposed in SSA.
#
n=2 assumed to be exposed in the UK and Ireland as cross tabulation of region of exposure and ethnicity
demonstrated that the majority of White-British patients (83.9%) were exposed in the UK and Ireland.
20
Table 2: Socio-demographic characteristics of included participant
female
Male
N (%)
111 (100)
26 (23.4)
85 (76.6)
Early
Diagnosis
N (%)
52 (46.8)
7 (26.9)
45 (52 )
Late
Diagnosis
N (%)
59 (53.2)
19 (73.1)
40 (47.1)
16-29
30-34
35-39
40-44
45-49
31 (27.9)
22 (19.8)
16 (14.4)
11 (9.9)
14 (12.6)
16 (51.6)
9 (40.9)
6 (37.5)
6 (54.5)
7 (50.0)
15 (48.4)
13 (59.1)
10 (62.5)
5 (45.5)
7 (50.0)
≥50
17 (15.3)
8 (47.1)
9 (52.9)
Variable
CD4 Cell count
Gender (n=111)
Age (years) at
Diagnosis
(n=111)
SIMD Quintile
(n=97)
Ethnicity (n=108)
Migrant status
(n=108)
Probable mode
of transmission
(n=107)
Probable region
of exposure
(n=106)
Registration with
GP (n=106)
1 (Most
deprived)
2
3
4
5 (Least
deprived)
Black-African
White British
White Other
UK National
P-value
p= 0.036*
p= 0.984**
p= 0.263**
15 (15.5)
16 (16.5)
18 (18.6)
17 (17.5)
7 (46.7)
4 (25.0)
8 (44.4)
9 (52.9)
8 (53.3)
12 (75.0)
10 (55.6)
8 (47.1)
31 (32.0)
31 (28.7)
64 (59.3)
13 (12.0)
64 (59.3)
16 (51.6)
8 (25.8)
37 (57.8)
6 (46.2)
37 (57.8)
15 (48.4)
23 (74.2)
27 (42.2)
7 (53.8)
27 (42.2)
Migrant
44 (40.7)
14 (31.8)
30 (68.2)
Heterosexual
MSM
64 (59.8)
42 (39.3)
23 (35.9)
29 (69.0)
41 (64.1)
13 (31.0)
IDU
1 (0.9)
Excluded
Excluded
UK and Ireland
Sub-Saharan
Africa
53 (50.0)
32 (60.4)
21 (39.6)
26 (24.5)
7 (26.9)
19 (73.1)
All other regions
27 (25.5)
11 (40.7)
16 (59.3)
p = 0.014***
p = 0.014*
p= 0.002*
p= 0.015***
p= 0.335#
Registered
Unregistered
96 (90.6)
10 (9.4)
46 (47.9)
3 (30.0)
50 (52.1)
7 (70.0)
p= 1.000*
Contact with
healthcare
professional in
the year
preceding
diagnosis (n=84)
No contact
26 (31.0)
11 (42.3)
15 (57.7)
Contact
58 (69.0)
25 (43.1)
33 (56.9)
21
Number of
contact with
healthcare
professional in
the year
preceding
diagnosis
p= 0.508##
Median (IQR)
84
1 (0-1)
1 (0-2)
p= 0.298*
Clinical indicator
disease in the 5
years preceding
diagnosis (n=92)
Absent
31 (33.7)
17 (54.8)
14 (45.2)
Present
61 (66.3)
25 (41.0)
36 (59.0)
P= 0.034##
Number of
Clinical indicator
disease in the 5
years preceding
diagnosis
Previous HIV
testing (n=103)
Co-existing
Hepatitis B or C
infection (n=103)
Median (IQR)
No previous HIV
test
Previous HIV
test
92
1 (0-1)
1 (0-1.25)
p<0.001*
74 (71.8)
24 (32.4)
50 (67.6)
29 (28.2)
23 (79.3)
6 (20.7)
Co-infection
21 (20.4)
4 (19.0)
17 (81.0)
No co-infection
82 (79.6)
43 (52.4)
39 (47.6)
p=0.013*
p=0.183*
Missed
opportunity for
diagnosis (n=92)
Missed
opportunity
15 (16.3)
4 (26.7)
11 (73.3)
No missed
opportunity
77 (83.7)
38 (49.4)
39 (50.6)
*Continuity Correction, **Linear by Linear, ***Pearson Chi Square, #Fishers Exact test, ##Mann-Whitney
22
Table 3: Factors associated with Late diagnosis of HIV
Unadjusted
Factors
OR
95% CI
OR
Gender (n=111)
Female
1.00
Male
3.05 1.16, 8.02
Ethnicity (n=108)
White-British
1.00
Black-African
3.94 1.53, 10.14
White-Other
1.60 0.48, 5.30
Migrant status (n=108)
UK National
Migrant
1.00
2.94 1.31, 6.57
Probable mode of
transmission (n=106)
MSM
Heterosexual
1.00
3.98 1.73, 9.12
Region of exposure
(n=106)
UK and Ireland
Sub-Saharan Africa
All other regions
1.00
4.14 1.48, 11.55
2.22 0.86, 5.70
Previous HIV testing
(n=103)
Testing
No testing
1.00
7.99 2.87, 22.19
Number of clinical
indicator diseases in five
years preceding
diagnosis (n=92)
1.91 1.11, 3.30
Co-existing Hepatitis B/C
infection (n=103)
Co-infection-NO
Co-infection -YES
1.00
4.69 1.45, 15.13
Adjusted
95% CI
p
1.00
2.83 1.10, 7.32
p=0.031
1.00
5.46 1.89, 15.81
p=0.002
23