Gaps in the HIV diagnosis and care cascade for migrants in Australia, 2013-2017: A cross-sectional study.

BACKGROUND: Globally, few studies compare progress toward the Joint United Nations Program on HIV/AIDS (UNAIDS) Fast-Track targets among migrant populations. Fast-Track targets are aligned to the HIV diagnosis and care cascade and entail achieving 90-90-90 (90% of people living with HIV [PLHIV] diagnosed, 90% of those diagnosed on treatment, and 90% of those on treatment with viral suppression [VS]) by 2020 and 95-95-95 by 2030. We compared cascades between migrant and nonmigrant populations in Australia. METHODS AND
FINDINGS: We conducted a serial cross-sectional survey for HIV diagnosis and care cascades using modelling estimates for proportions diagnosed combined with a clinical database for proportions on treatment and VS between 2013-2017. We estimated the number of PLHIV and number diagnosed using New South Wales (NSW) and Victorian (VIC) data from the Australian National HIV Registry. Cascades were stratified by migration status, sex, HIV exposure, and eligibility for subsidised healthcare in Australia (reciprocal healthcare agreement [RHCA]). We found that in 2017, 17,760 PLHIV were estimated in NSW and VIC, and 90% of them were males. In total, 90% of estimated PLHIV were diagnosed. Of the 9,391 who were diagnosed and retained in care, most (85%; n = 8,015) were males. We excluded 38% of PLHIV with missing data for country of birth, and 41% (n = 2,408) of eligible retained PLHIV were migrants. Most migrants were from Southeast Asia (SEA; 28%), northern Europe (12%), and eastern Asia (11%). Most of the migrants and nonmigrants were males (72% and 83%, respectively). We found that among those retained in care, 90% were on antiretroviral therapy (ART), and 95% of those on ART had VS (i.e., 90-90-95). Migrants had larger gaps in their HIV diagnosis and care cascade (85-85-93) compared with nonmigrants (94-90-96). Similarly, there were larger gaps among migrants reporting male-to-male HIV exposure (84-83-93) compared with nonmigrants reporting male-to-male HIV exposure (96-92-96). Large gaps were also found among migrants from SEA (72-87-93) and sub-Saharan Africa (SSA; 89-93-91). Migrants from countries ineligible for RHCA had lower cascade estimates (83-85-92) than RHCA-eligible migrants (96-86-95). Trends in the HIV diagnosis and care cascades improved over time (2013 and 2017). However, there was no significant increase in ART coverage among migrant females (incidence rate ratio [IRR]: 1.03; 95% CI 0.99-1.08; p = 0.154), nonmigrant females (IRR: 1.01; 95% CI 0.95-1.07; p = 0.71), and migrants from SEA (IRR: 1.03; 95% CI 0.99-1.07; p = 0.06) and SSA (IRR: 1.03; 95% CI 0.99-1.08; p = 0.11). Additionally, there was no significant increase in VS among migrants reporting male-to-male HIV exposure (IRR: 1.02; 95% CI 0.99-1.04; p = 0.08). The major limitation of our study was a high proportion of individuals missing data for country of birth, thereby limiting migrant status categorisation. Additionally, we used a cross-sectional instead of a longitudinal study design to develop the cascades and used the number retained as opposed to using all individuals diagnosed to calculate the proportions on ART.
CONCLUSIONS: HIV diagnosis and care cascades improved overall between 2013 and 2017 in NSW and VIC. Cascades for migrants had larger gaps compared with nonmigrants, particularly among key migrant populations. Tracking subpopulation cascades enables gaps to be identified and addressed early to facilitate achievement of Fast-Track targets.

Introduction

Reaching the end of the AIDS epidemic by achieving the Joint United Nations Program on HIV/AIDS (UNAIDS) Fast-Track targets set for 2020 and 2030 [1,2] requires tracking progress across all populations at risk and living with HIV. Fast-Track targets entail 90% of people living with HIV (PLHIV) are diagnosed, 90% of those diagnosed receive sustained antiretroviral therapy (ART), and 90% of those on ART achieve viral suppression (VS) (i.e., 90-90-90) by 2020 and scaling up to 95-95-95 by 2030. Reaching the Fast-Track targets by 2030 is projected to lead to at least 90% reduction of HIV incidence compared with 2010 [2].

Although progress is being made globally, significant gaps still exist as we work toward the UNAIDS target; importantly, new HIV infections are not decreasing fast enough [3]. The incidence reduction goal is more likely to be achieved if HIV prevention strategies are strengthened [4] and if the potential pools of high HIV viral loads are eliminated. In 2017, 75% of PLHIV globally were diagnosed, 79% of those diagnosed were on ART, and 81% of those were on ART with VS (i.e., 59% on ART and 48% virally suppressed of all PLHIV). However, there were 1.8 million new infections, which was higher than anticipated [5]. It is thought the higher-than-expected incidence may be due to gaps in key populations receiving care, including adolescent girls, young women, and migrants [5].

Migrants comprise a considerable proportion of PLHIV globally, but there are limited data on diagnosis and treatment rates in this important population. Although the UNAIDS Global AIDS update report provides HIV diagnosis and care cascade data at global, regional, and country levels, the onus is upon countries to track subpopulation cascades. In Australia, 29% of the population was born overseas, and 33% of the 27,545 estimated to be living with HIV in 2017 were born overseas—mainly Southeast Asia (SEA), sub-Saharan Africa (SSA), and the Americas [6,7]. In 2017, 89% of PLHIV in Australia were aware of their HIV status, 87% were on ART, and of those, 95% were virally suppressed [6]. Although the number of new infections decreased in Australia, particularly among men who have sex with men (MSM), new HIV infections among migrants increased [6]. Migrants were also more likely to present with a late HIV diagnosis than citizens [6, 8,9]. These differences raise concerns that migrants are being ‘left behind’, highlighting the need to focus on this group if Australia is to achieve the Fast-Track targets.

Migrants face barriers to healthcare access because of their migration status, sociocultural influence, financial constraints, and stigma [5,10]. Limited access to healthcare for migrants has been highlighted in many European countries, including Spain, Portugal, and Ireland, with the need to consider equity and equality [11,12]. In Asia, migrant workers had limited access to HIV testing, especially when undocumented [13]. In Australia, citizens and permanent residents have subsidised general healthcare access, including HIV care services, through the national health insurance scheme (Medicare). Additionally, temporary residents from countries with a reciprocal healthcare agreement (RHCA) with Australia (i.e., Belgium, Finland, Italy, Malta, Netherlands, New Zealand, Norway, Ireland, Slovenia, Sweden, and United Kingdom) are eligible for subsidised access through Medicare. However, migrants outside of these categories are not eligible for Medicare, although some may access HIV care and treatment through compassionate access programmes or private health insurance [6,8]. When reporting HIV diagnosis and care cascades, it is important to provide subpopulation cascades to track progress toward Fast-Track targets and ensure no one is left behind.

In this study, we developed the HIV diagnosis and care cascades for migrants (stratified according to migrant populations) and compared them to the cascade of nonmigrants (2013–2017) to explore gaps and opportunities to inform the national and state HIV response strategies.

Methods

We developed HIV diagnosis and care cascades using modelling estimates for proportions diagnosed combined with a clinical database for proportions on treatment and virally suppressed between 2013–2017 following a prespecified analysis plan (S1 Text) and the STROBE checklist for observational studies (S1 STROBE Checklist).

Study population and data sources

To estimate the number of people living with diagnosed HIV (PLDHIV) we primarily used New South Wales (NSW) and Victorian (VIC) data from the Australian National HIV Registry. Additional data from the Australian Bureau of Statistics (ABS) and follow-up studies of people recently diagnosed with HIV were used to inform population movement estimates [14]. The number of undiagnosed PLHIV was estimated using back-projection modelling from CD4+ count at diagnosis [15].

In Australia, all new HIV diagnoses must be reported by laboratories and/or doctors to state and territory health departments, who submit these data to the National HIV Registry [6,16]. State and territory authorities use this information to analyse and report surveillance data to the National HIV Registry, which is maintained by the Kirby Institute at the University of New South Wales (UNSW Sydney), on behalf of the Australian Government Department of Health [17]. HIV diagnoses among immigrants to Australia are added to the national HIV registry if they have lived in Australia for at least 3 months and intend to reside in the notifying state/territory, including among people previously diagnosed overseas [6]. HIV notifications in the registry are accompanied by a range of information with varying levels of completeness.

Data from the Australian Collaboration for Coordinated Enhanced Sentinel Surveillance of Sexually Transmissible Infections and Blood Borne Viruses (ACCESS) database were used to determine the number retained in care and calculate proportions on treatment and VS [18]. ACCESS is a national sexual health surveillance network that collects and collates routine deidentified data from over 120 sexual health clinics, general practice clinics, community health services, hospitals, and pathology laboratories across Australia, and records are anonymously linked between sites [18]. The ACCESS network collects demographic, testing, diagnosis, and treatment data to monitor the sexual health of high‑risk populations. Compared with other states, NSW and VIC have the highest coverage in the ACCESS database, and they comprise the highest proportion of PLHIV and migrants in Australia [6,7].

ACCESS data have been used previously to report on HIV treatment outcomes mostly in NSW and VIC [14,19]. The ACCESS database has a high coverage of treatment and viral load data for PLHIV in NSW and VIC. A total of 7,998 and 9,727 PLHIV were captured in the ACCESS network in 2016 and 2017, respectively, across seven general practice clinics, two hospitals, and 33 sexual health clinics and linked. ACCESS captures 53%–57% of the estimated PLHIV (diagnosed and nondiagnosed) and 59%–63% of the estimated diagnosed PLHIV in NSW and VIC [14,20].

Data collection

Data collected in ACCESS include date of visit, country of birth, area of residence, sex, self-reported HIV transmission category, ART status, and HIV viral load results. Country of birth is self-reported to the diagnosing clinician; we used this variable to classify individuals as migrants (born outside Australia) or nonmigrants (born in Australia). Based on country of birth, individuals were also grouped into region of birth based on the ABS classification [7]. Country of birth was also used to determine whether the individual was from a country eligible for RHCA with Australia [21]. Individuals missing country of birth and sex were compared separately. We excluded trans and gender-diverse persons because of their small numbers (6 in 2013 and 18 in 2017).

Cascade variables

We produced estimates for the HIV diagnosis and care cascade for the overall population and by migration status, sex, male-to-male HIV exposure, migrants from SEA and SSA, and migrants from countries with RHCA with Australia.

The first two steps of the cascades (the number of PLHIV and the number of diagnosed PLHIV) were estimated using previously developed methods applied to data from Australia's National HIV Registry, with numbers rounded to the nearest 10. Full details of the registry and calculations are available from the HIV, viral hepatitis, and sexually transmissible infections in Australia Annual Surveillance Report, 2018 [6]; previous publications [14,22]; and an online repository containing all the code used in the calculations [23].

To estimate the number of diagnosed PLHIV for each stratification, complete notification data for several key variables were required. To address the missing data in the HIV Registry and to obtain the complete notification data required for our analysis, we used predictive mean matching univariate imputation using known notification data to the end of 2017 from the six notification variables: year of diagnosis (100% complete), age at diagnosis (99.4% complete), sex and gender (99.5% complete), country and region of birth (69.6% complete), and exposure category/likely route of HIV transmission (86.6% complete) for each jurisdiction (100% complete). From this imputation process, we produced 10 sets of notifications with complete information for each of the six variables to account for statistical uncertainty. This imputation was conducted using the MICE software package in the R statistical software program [24,25]. Using the 10 imputed notifications sets, we estimated the number of PLHIV and the number diagnosed in NSW and VIC to 31 December 2017 for each region of birth, with an estimated upper and lower bound for each, as described in the following two subsections.

PLDHIV (first 90)

From each imputed set of notifications, we determined the annual number of PLDHIV for each subpopulation in NSW and VIC separately by summing all notifications meeting the appropriate criteria and then subtracting duplicates, estimated deaths, overseas emigrants, and net interstate departures. The range for PLDHIV for each set was estimated by multiplying the appropriate lower and upper bounds for the number of deaths (estimated using the 95% CI of the death rate), emigrants, and interstate arrivals and departures. Our overall estimate for PLDHIV was then obtained by summing the mean NSW and VIC estimates for each population from the 10 imputed notification sets with a range between the minimum and maximum values from the sets.

Number of undiagnosed PLHIV

To estimate the proportion of PLHIV who were undiagnosed for each population, we applied the European Centre for Disease Prevention and Control (ECDC) HIV Modelling Tool and added this to the estimated number of PLDHIV [15]. The ECDC tool is a multistate back-calculation model using HIV notification data and estimates for the rate of CD4+ T-cell decline to fit diagnosis rates over time, which produces an estimate and range of the undiagnosed population and the percentage undiagnosed. The range in PLHIV was obtained by applying the lower estimate for proportion undiagnosed to the lower limit for PLDHIV and, conversely, for the upper bound.

Number of PLDHIV retained in care

We used the ACCESS database to identify the number retained in care, defined as PLDHIV with a consult/HIV viral load test (VL)/on ART in that year. Using the ACCESS database for those retained in care, we calculated the proportions on ART and virally suppressed to complete the HIV diagnosis and care cascade.

Proportion of PLDHIV retained in care on ART (second 90)

We used ART status as captured in the ACCESS database for PLDHIV receiving a three-drug regimen for treatment in that year. We queried the database for all HIV-positive individuals with ART documentation per year. We used this proportion to estimate the second 90 instead of the traditional proportion of diagnosed who are on ART.

Proportion of PLDHIV retained in care on ART with VS (third 90)

To calculate the number of PLDHIV retained in care on ART with VS, we used the last VL in the year for patients on ART recorded in the ACCESS database irrespective of ART duration. VS was defined as VL < 200 copies per millilitre.

Statistical methods

To build the HIV testing and treatment cascade for each year, proportions were calculated for the first 90, second 90, and third 90 based on the UNAIDS 90-90-90 targets (as described previously). The cascades were developed using a cross-sectional approach applied annually.

We developed the overall cascade and subpopulation cascades by sex, migration status, male-to-male HIV exposure, migrants born in SEA, migrants born in SSA, and RHCA status.

We estimated the overall ART coverage (first 90 * second 90) and VS (first 90 * second 90 * third 90) among all estimated PLHIV and compared it with targets of 81% and 73% by 2020, respectively, by calculating the appropriate proportion and the 95% CI for the proportions [2].

Individuals missing country of birth (could not be classified as migrants or nonmigrants) or sex in the ACCESS database were tracked separately for ART coverage and VS.

Poisson regression was used to describe annual trends of cascade proportions over time (2013–2017) using incidence rate ratio (IRR), 95% CI, and a p-value. Data modelled in the Poisson regression were checked for overdispersion. Data analysis was conducted using Stata IC 14 (Stata Corp, College Station, TX), with the significance level set at p < 0.05. We present the cascades for 2017 in detail and used all cascades (2013–2017) to explore trends.

The study was approved by The Alfred Ethics Committee (Project No. 248/17) and Monash University Human Ethics Committee (Project No. 11221).

Results

HIV diagnosis and care cascade in 2017

In 2017, there were an estimated 17,760 PLHIV in NSW and VIC; 90% were males, and 90% (95% CI 90%–91%) had been diagnosed (PLDHIV). Of the 9,391 retained in care in the ACCESS database in 2017, most (85%, n = 8,015) were males. Migration status could not be determined for 38% of PLHIV because of missing country of birth. Of those with a country of birth entry (n = 5,817), 41% (n = 2,408) were migrants. Most migrants were from SEA (28%), northern Europe (12%), and eastern Asia (11%). Most of the migrants and nonmigrants were males (72% and 83%, respectively). Of the retained migrants, 90% (95% CI 90%–91%) were on ART, and 95% (95% CI 95%–96%) of those on ART had VS. Overall, we estimated that 81% and 77% of all estimated PLHIV were on ART and virally suppressed, respectively (Table 1). Of the 38% of PLHIV missing country of birth in the ACCESS database, 94% (95% CI 94%–95%) were on ART, and 96% (95% CI 95%–97%) on ART had VS. Of those missing sex (9%), 96% (95% CI 95%–98%) were on ART, and 96% (95% CI 95%–97%) on ART had VS. The rest of the data used to develop the HIV care cascades and the estimated numerators and denominators for the cascade calculations are included in the Supporting information (S1 and S2 Tables).

Table 1

HIV diagnosis and care cascades in New South Wales and Victoria (2017).

Variable	First 90, % (95% CI)	Second 90, % (95% CI)	Third 90, % (95% CI)	Estimated on ART of all PLHIV*	Estimated viral suppression of all PLHIV**
All PLHIV	90 (90–91)¥	90 (90–91)	95 (95–96) ¥	81£	77£
Male	90 (90–91) ¥	91 (90–91)	95 (95–96) ¥	82£	79£
Female	89 (87–90)	78 (74–81)	91 (87–94)	69	63
Migrants	85 (85–86)	85 (84–87)	93 (92–94) ¥	73	67
Male	85 (84–86)	84 (83–86)	92 (91–94) ¥	71	66
Female	87 (85–89)	79 (74–84)	90 (84–94)	69	62
Nonmigrant	94 (93–94) ¥	90 (89–91)	96 (94–96) ¥	85£	81£
Male	93 (93–94) ¥	90 (88–91)	96 (95–97) ¥	84£	80£
Female	92 (90–94) ¥	70 (63–77)	88 (80–94)	64	57
Male-to-male HIV exposure	91 (90–91) ¥	90 (89–91)	96 (95–96) ¥	82£	79£
Migrant	84 (83–85)	87 (85–88)	93 (91–94) ¥	73	68
Nonmigrant	96 (95–96) ¥	92 (90–93)	96 (95–97) ¥	88£	85£
Migrants born in SEA	72 (70–74)	87 (84–90)	93 (90–95) ¥	63	58
Migrants born in SSA	89 (87–91)	93 (89–95)	91 (87–95)	83£	75£
Migrants eligible for RHCA	96 (95–97)	86 (82–88)	95 (92–97) ¥	83£	78£
Migrants not eligible for RHCA	83 (82–84)	85 (84–87)	92 (91–94) ¥	71	65
Missing country of birth	n/a	94 (94–95)	96 (95–97)	n/a	n/a
Missing sex	n/a	96 (95–98)	96 (95–97)	n/a	n/a

*The target for estimated proportion on ART of all PLHIV in 2020 is 81%, and estimates were calculated per subpopulation living with HIV.

**The target for estimated proportion with viral suppression of all PLHIV is 73% in 2020, and estimates were calculated per subpopulation living with HIV.

¥90-90-90 target met.

£Overall target met.

Abbreviations: ART, antiretroviral therapy; PLHIV, people living with HIV; RHCA, reciprocal healthcare agreement; SEA, Southeast Asia; SSA, sub-Saharan Africa

Migrant subpopulations had larger gaps in the HIV diagnosis and care cascade estimates compared with nonmigrants, as shown in Table 1. Among migrants estimated to be living with HIV (37%; n = 6,570), 85% (95% CI 85%–86%) were diagnosed, and of those retained in care, 85% (95% CI 84%–87%) were on ART, and 93% (95% CI 92%–94%) of those on ART had VS. Among nonmigrants estimated to be living with HIV, 94% (95% CI 93%–94%) were diagnosed, and of those retained in care, 90% (95% CI 89%–91%) were on ART, and 96% (95% CI 95%–97%) had VS.

The estimated overall ART coverage was the highest among nonmigrants (88%), nonmigrant males (84%), migrants born in SSA (83%), and those eligible for RHCA (83%) and lowest among nonmigrant females (63%). The estimated overall VS was >73% in migrants born in SSA (75%), migrants with RHCA (74%), nonmigrant males (76%), and nonmigrants reporting male-to-male HIV exposure (80%) (Table 1).

Migrants reporting male-to-male HIV exposure had lower cascade estimates (84-87-93) compared with nonmigrants reporting similar exposure (96-92-96). Migrants born in SEA also had lower cascade estimates (72-87-93) compared with those born in SSA (89-93-91). Similarly, migrants from countries not eligible for RHCA had lower cascade estimates (83-85-92) compared with those eligible (96-86-95). Females had lower cascade estimates irrespective of whether they were migrants (87-79-90) or nonmigrants (92-70-88) (Table 1).

HIV diagnosis and care cascade trends (2013–2017)

Between 2013–2017, the trend in the overall annual proportion diagnosed (first 90) in NSW and VIC was stable (IRR: 1.00; 95% CI 0.99–1.01; p = 0.281), including among migrants (IRR: 1.01; 95% CI 0.99–1.02; p = 0.142), nonmigrants (IRR: 1.00; 95% CI 0.99–1.01; p = 0.71), and other subpopulations (Table 2).

Table 2

HIV diagnosis and care cascade trends for New South Wales and Victoria (2013–2017).

Variable	2013 (%)	2014 (%)	2015 (%)	2016 (%)	2017 (%)	IRR (95% CI)	p-Value
Diagnosed (first 90)	89	90	90	90	90	1.00 (0.99–1.01)	0.281
Male	89	90	90	90	90	1.00 (0.99–1.01)	0.444
Female	87	87	88	88	89	1.01 (0.99–1.02)	0.482
Migrants	83	84	85	85	85	1.01 (0.99–1.02)	0.142
Male	83	84	84	84	85	1.01 (0.99–1.01)	0.286
Female	85	85	86	86	87	1.01 (0.98–1.03)	0.569
Nonmigrant	93	93	94	94	94	1.00 (0.99–1.01)	0.710
Male	93	93	93	93	93	1.00 (0.99–1.01)	0.878
Female	94	93	93	93	92	0.99 (0.97–1.02)	0.798
Male-to-male HIV exposure	90	90	91	91	91	1.00 (0.99–1.01)	0.393
Migrant	83	84	84	84	84	1.00 (0.99–1.01)	0.691
Nonmigrant	95	95	95	95	96	1.00 (0.99–1.01)	0.483
Born in SEA	70	70	71	72	72	1.01 (0.99–1.03)	0.369
Born in SSA	83	85	87	88	89	1.02 (0.99–1.04)	0.137
RHCA eligible	94	95	95	96	96	1.01 (0.99–1.02)	0.534
RHCA not eligible	80	81	81	82	83	1.01 (0.99–1.02)	0.099
On ART (second 90)	72	74	77	86	90	1.06 (1.05–1.06)	<0.001
Male	70	73	76	86	91	1.06 (1.05–1.07)	<0.001
Female	66	67	71	76	78	1.04 (1.01–1.08)	0.012
Missing sex	89	90	93	95	96	1.02 (0.99–1.04)	0.122
Missing country of birth	70	73	75	91	94	1.08 (1.07–1.09)	<0.001
Migrant	71	72	78	81	85	1.04 (1.03–1.06)	<0.001
Male	67	69	75	79	84	1.05 (1.03–1.07)	<0.001
Female	69	69	75	77	79	1.03 (0.99–1.08)	0.154
Nonmigrant	74	76	79	84	90	1.04 (1.03–1.05)	<0.001
Male	72	74	77	83	90	1.04 (1.03–1.05)	<0.001
Female	67	68	71	71	70	1.01 (0.95–1.07)	0.707
Male-to-male HIV exposure	69	72	76	87	90	1.07 (1.06–1.08)	<0.001
Migrant	67	70	77	81	87	1.06 (1.04–1.08)	<0.001
Nonmigrant	73	75	79	85	92	1.05 (1.04–1.06)	<0.001
Born in SEA	76	77	83	83	87	1.03 (0.99–1.07)	0.055
Born in SSA	80	83	88	90	93	1.03 (0.99–1.08)	0.108
RHCA eligible	70	73	77	80	86	1.04 (1.01–1.07)	0.016
RHCA not eligible	71	72	78	81	85	1.04 (1.02–1.06)	<0.001
Viral suppression (third 90)	89	91	92	95	95	1.02 (1.01–1.03)	<0.001
Male	89	91	92	95	95	1.02 (1.01–1.03)	<0.001
Female	84	87	87	91	91	1.02 (0.97–1.06)	0.351
Missing sex	92	92	95	95	97	1.01 (0.99–1.04)	0.331
Missing country of birth	91	94	95	96	97	1.01 (0.99–1.03)	0.068
Migrants	87	89	90	93	93	1.02 (1.00–1.04)	0.046
Male	85	88	88	93	92	1.02 (1.00–1.04)	0.044
Female	85	88	86	91	90	1.02 (0.96–1.08)	0.584
Nonmigrant	87	89	92	94	96	1.02 (1.01–1.04)	0.001
Male	87	89	92	94	96	1.03 (1.01–1.04)	0.001
Female	83	85	86	92	88	1.02 (0.95–1.09)	0.609
Male-to-male HIV exposure	89	92	92	95	96	1.02 (1.01–1.03)	<0.001
Migrant	87	88	88	93	93	1.02 (0.99–1.04)	0.076
Nonmigrant	87	90	92	95	96	1.02 (1.01–1.04)	0.002
Migrants born in SEA	83	90	87	92	93	1.03 (0.99–1.06)	0.178
Migrants born in SSA	88	91	92	92	91	1.01 (0.96–1.06)	0.794
Migrants eligible for RHCA	90	91	91	96	95	1.02 (0.98–1.05)	0.369
Migrants not eligible for RHCA	85	88	89	93	92	1.02 (0.99–1.04)	0.064

Abbreviations: ART, antiretroviral therapy; IRR, incidence rate ratio; RHCA, reciprocal healthcare agreement; SEA, Southeast Asia; SSA, sub-Saharan Africa

Fig 1 highlights the cascade trends of migrants compared with nonmigrants, and Fig 2 compares male-to-male HIV exposures. The trend in the overall annual proportion on ART (second 90) increased (IRR: 1.06; 95% CI 1.05–1.06; p < 0.001), including in migrants (IRR: 1.04; 95% CI 1.03–1.06; p < 0.001) and nonmigrants (IRR: 1.04; 95% CI 1.03–1.06; p < 0.001) (Fig 1). However, there was no increasing trend among migrant females (IRR: 1.03; 95% CI 0.99–1.08; p = 0.154), nonmigrant females (IRR: 1.01; 95% CI 0.95–1.07; p = 0.707), migrants from SEA (IRR: 1.03; 95% CI 0.99–1.07; p = 0.055), and migrants from SSA (IRR: 1.03; 95% CI 0.99–1.08; p = 0.108) (Table 2).

Fig 1

HIV diagnosis and care cascades for all PLHIV, migrants, and nonmigrants in NSW and VIC (2013–2017).

ART, antiretroviral therapy; NSW, New South Wales; PLHIV, people living with HIV; VIC, Victoria; VS, viral suppression.

Fig 2

HIV diagnosis and care cascades for all male-to-male HIV exposure, migrants, and nonmigrants in NSW and VIC (2013–2017).

ART, antiretroviral therapy; NSW, New South Wales; VIC, Victoria; VS, viral suppression.

VS proportions were generally high over the years and were ≥90% in 2017 in all subpopulations except nonmigrant females. There was an increasing trend in VS among all PLHIV (IRR: 1.02; 95% CI 1.01–1.03; p < 0.001). However, there was no increasing VS trend in migrants reporting male-to-male HIV exposure (IRR: 1.02; 95% CI 0.99–1,04; p = 0.076) (Fig 2), female migrants (IRR: 1.02; 95% CI 0.96–1.08; p = 0.584), migrants born from SEA (IRR: 1.03; 95% CI 0.99–1.06; p = 0.178), and migrants born from SSA (IRR: 1.01; 95% CI 0.96–1.06; p = 0.794) (Table 2).

Discussion

Our results suggest that although gaps in the overall HIV diagnosis and care cascade in NSW and VIC decreased between 2013 and 2017, there were notable and sustained gaps in migrant subpopulations. Overall, migrants had lower HIV diagnosis and retention across the HIV care cascade than nonmigrants, especially the proportion diagnosed and on ART (first and second 90). Migrants reporting male-to-male HIV exposure, born in SEA and SSA, ineligible for subsidised healthcare through RHCA, and who were female had lower HIV diagnosis and retention across the care cascade than nonmigrants. There was a particularly low proportion of PLHIV born in SEA who were diagnosed (72% in 2017). As we approach 2020, the first Fast-Track target year, we estimated that in 2017, 70% and 65% of migrants estimated to be living with HIV in NSW and VIC were on ART and virally suppressed, respectively. This was lower than 80% and 77%, respectively, in nonmigrants estimated to be living with HIV.

To the best of our knowledge, this is the first analysis that examined and compared HIV diagnosis and the care cascade in migrants in Australia. In a survey of 55 European and Central Asian countries, Brown and colleagues (2018) identified that only 41% produced cascades for key and vulnerable populations, but there was wide variation between countries and no subpopulation comparisons [26]. Our findings are consistent with other studies that identified that mobile populations often face barriers to HIV testing and treatment access and often have lower VS, suggesting that they have lower HIV diagnosis and retention in the care cascade than citizens [26,27]. Italian researchers reported that migrants living with HIV had barriers to initiating ART and a higher risk of virologic failure compared with nonmigrants [28]. In countries with concentrated epidemics, HIV diagnosis and care among MSM are often similar to overall national cascades, but migrants often have lower treatment coverage and VS [26]. A review by Tanser and colleagues (2015) also identified poorer HIV diagnosis and care cascades among migrants and found that existing health system structures failed to properly account for migrants who still present late and have poor retention [29]. In Australia, the incidence of HIV in newly arrived Asian-born MSM was found to be higher than other subpopulations [30]. Although Medicare ineligibility is a significant barrier, it is known that migrants are less likely to be offered or less likely to initiate early ART despite having access [31,32].

Migrants are often more likely to have higher HIV prevalence, higher frequency of late HIV diagnosis, and lower VS rates and to experience barriers to healthcare access than the general population [29,33,34]. Studies in Australia have previously identified that migrants faced barriers to HIV testing, including cost and eligibility for health services, low visibility or awareness of HIV in Australia, HIV-related stigma, sociocultural and religious influences, and missed opportunities for testing during general practitioner visits [10,35]. Our study found that migrants from countries that are not eligible for RHCA had lower HIV diagnosis and care cascades compared with those who are eligible. An additional layer of barriers includes legal status of migrants. A study that compared documented and undocumented migrants in Italy found that despite free access to ART, migrants still had difficulties in gaining optimal HIV care [34]. In contrast, other studies found that when healthcare cost barriers are removed for migrants living with HIV, VS increases, thereby reducing risk of transmission [36,37]. Removing barriers that migrants living with or at risk of HIV face is expected to improve health outcomes and progress toward the UNAIDS Fast-Track targets.

The poorer cascade for Australian-born females is of particular concern. However, our analysis did not explore differences between indigenous and nonindigenous Australians. Surveillance data have shown that between 2013 and 2016, the rate of HIV notifications increased by 41% in the indigenous population and remained 1.6 times higher compared with the nonindigenous population in 2016 [6]. According to the surveillance data, a greater proportion of HIV notifications in the indigenous population is attributed to heterosexual sex (21%) or injecting drug use (18%) compared with the Australian-born nonindigenous population (18% and 3%, respectively). Further comparisons between indigenous and nonindigenous Australian-born populations is warranted to compare whether there are differences in HIV testing, treatment coverage, and VS.

Previous work by our group suggests that reaching the Fast-Track targets may not be enough to achieve HIV incidence reduction, especially in countries like Australia with low HIV prevalence [4]. Scaling up other HIV prevention strategies, such as HIV testing, preexposure and postexposure prophylaxis, and condom use, would be required. Realising the full prevention and treatment benefits of the UNAIDS Fast-Track strategy will require reaching all key and vulnerable subpopulations, including migrants [29]. This is particularly important at this stage of the HIV epidemic response and with increasing levels of global migration [38]. Understanding the dynamics of HIV and migration can be complex because migrants can be at risk of HIV infection before, during, and after migration. HIV prevention and treatment programmes will need to take into consideration these dynamics with an inclusive approach.

Limitations of our study included a high proportion (38% in 2017) of individuals retained in ACCESS database missing a country of birth entry. This impacted our ability to categorise migrants or nonmigrants and migrant populations. However, we tracked ART coverage and VS among those missing country of birth and found similar or higher proportions compared with nonmigrants. We used a cross-sectional instead of a longitudinal approach to develop the cascades of care because it was not feasible to link individuals diagnosed in the National HIV Registry to the clinical database. Hence, we used the proportion of individuals retained in care in the clinical database who initiated ART to estimate the second 90 of the cascade. Another limitation was that all patients on ART, irrespective of ART duration, were counted in the analysis, including patients newly initiated on ART. This has the potential to overestimate the number not virally suppressed. The generalizability of our work is reduced because it only included two Australian states (NSW and VIC). However, these states have the highest proportion of PLHIV and migrants [7] and are likely to be reasonably representative of the national picture. Finally, the analysis considered country of birth as a proxy for migrant status and Medicare eligibility and may have misclassified people born in other countries who were Australian citizens and Medicare eligible. Additional data that can better classify migrants, including year of arrival, years lived in Australia, and visa category, are needed. Follow-up modelling studies are required to reveal the differences between migrants and nonmigrants in order to highlight additional sociodemographic characteristics that may be used to design public health programmes.

Conclusion

Gaps in the overall HIV diagnosis and retention in the care cascade in NSW and VIC decreased between 2013 and 2017. However, there were notable and sustained gaps in migrant subpopulations. In particular, retention of migrants in the care cascade was lower compared with nonmigrants. Notable low retention occurred in migrants reporting male-to-male HIV exposure, migrants from SEA and SSA, migrants not eligible for subsidised healthcare access in Australia, and females (migrants and nonmigrants). Migration is a global issue, with global migration increasing. If the HIV response is to meet the Fast-Track HIV incidence reduction target, it is important to monitor HIV diagnosis and the care cascades in all subpopulations to ensure that no one is being left behind.

(DOC)

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Concept note.

(DOCX)

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Estimated PLHIV diagnosed.

PLHIV, people living with HIV.

(DOCX)

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Retained PLHIV on ART.

ART, antiretroviral therapy; PLHIV, people living with HIV.

(DOCX)

Click here for additional data file.

15 Dec 2019

Dear Dr. Marukutira,

Thank you very much for submitting your manuscript "HIV diagnosis and care cascade for migrants in Australia : Identifying gaps and opportunities towards the last mile to HIV elimination" (PMEDICINE-D-19-03230) for consideration at PLOS Medicine for our upcoming special issue on refugee and migrant health.

Your paper was evaluated by the editors and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to invite you to submit a revised version that fully addresses the reviewers' and editors' comments. You will appreciate that we cannot make a decision about publication until we have seen the revised manuscript and your response, and we expect to seek re-review by one or more of the reviewers.

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Requests from the editors:

Noting referee 1's comments, PLOS Medicine policy is that significance tests should be performed and reported with 95% CI and p values where appropriate.

Please adapt the title so that the segment after the colon consists predominantly of a study descriptor, e.g., "HIV Diagnosis and Care Cascade for Migrants in Australia: a cross-sectional study", and include study date(s).

We ask you to combine the "methods" and "findings" subsections of your abstract. Please add a new final sentence to the new combined subsection to summarize the study's main limitations.

Please add summary demographic and country or region of origin details for study participants to abstract and main text.

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The first paragraph of the discussion should summarize the study's findings, with elements of discussion being restricted to subsequent paragraphs.

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Please correct "report" at line 410.

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Comments from the reviewers:

*** Reviewer #1:

This is an important study and shows important information and I think the authors have done a nice job with the study. There are a few places where I think the reporting is unclear and needs some revising and possibly the methods, but I think these changes are not overly major. Below are specific comments for the authors.

I do want to commend the authors on the reporting of precisely null results! This is important to know, but often gets confused with non-significance. I would agree your findings are null even though you are near significance! My only quibble would be to ask you not to use significance testing at all and remove reference to "with the significance level set at p<0·05" in the methods and remove the pvalues from the results.

In the methods, can you clarify this statement, "To estimate the number of people living with diagnosed HIV (PLDHIV) we used New South Wales (NSW) and Victorian (VIC) data from the Australian National HIV Registry, enhanced by surveillance among people recently diagnosed with HIV to improve migration estimates." What does it mean to be "enhanced"?

You note that "The ACCESS network collects demographic, testing, diagnosis and treatment data to monitor the sexual health of high‑ risk populations. Compared to other states, NSW and VIC have the highest coverage in the ACCESS database and they comprise the highest proportion of PLHIV and migrants in Australia [6, 17]." But it is important to state here how high the coverage is. Is this the 52% and 61% percent mentioned in the next few sentences? This wasn't clear to me.

I found the explanation of how the first two steps of the cascade were created to be unclear. I begins by talking about mean matching imputation but I am not clear on what is being imputed. Is this the number of people who are infected and if so, based on what data? Is it the registry data and if so, why does this need to be imputed? Or is the idea you are just imputing covariates within the registry data? It might help to start that paragraph within "In order to [state the objective]…" statement to make it clearer why the imputation was being done. And if this was based on notifications, wouldn't this be persons living with HIV who know their status and not all people with HIV?

I was confused about the second 90 which appears to be limited to those in care. This doesn't seem right as the second 90 is meant to be everyone on ART and the third 90 should be everyone who is virally suppressed. Limiting to just those in care would seem to inflate the numbers and also not carry through everyone in the original denominator of 100%. I could be misunderstanding this but that is the way it read to me.

The reason I think I could be wrong is the next section says "Proportion of PLDHIV retained in care on ART (2nd 90)" So if I'm wrong about the above, it isn't clear why this is the 2nd 90 as that would usually be the proportion on ART. Perhaps these are mathematically equivalent in which case I would just make that clearer.

The validity of the second and third 90 really depends on how representative the ACCESS database is. Can you provide any information on that given it appears to only be 50% complete?

*** Reviewer #2:

I confine my remarks to statistical aspects of this paper. The basic approach is fine, but I have some corrections and suggestions before I can recommend publication.

First, I wonder why the authors didn't model the differences between groups that they seem interested in. It isn't *necessary* to do so - and the title of the paper is not about modeling - but they do seem interesting. Maybe that would be a different paper.

Line 24 Perhaps I am confused, but this seems to contradict the result reported in the results section.

Line 25-28 and similarly in the results section (line 249-254) - all of these rates did increase, just not significantly. Part of this can be corrected by adding the word "significantly" but the authors should be careful not to accept the null. And, if they want to comment on statistics about differences between groups, they should model them (e.g. with a regression).

Line 196-197 I don't understand how these plausible ranges were found or what they were used for.

Table 2 - I would get rid of the CIs in the cells for years. It's not that they are wrong, but they aren't needed and they make the table very hard to read.

Fig 1 and 2: I would use line graphs with time on the x axis and % on the y axis. I would have 5 graphs in each figure - one for each "90" - each with3 lines (one for all, one for migrants and one for non-migrants) (or maybe no line for "all")

Peter Flom

*** Reviewer #3:

A well written paper describing the differential treatment cascades within the Australian system, according to migrant status. Although it is not essential the authors might consider a few additions to help contextualize the paper, especially for overseas readers; Firstly, it should be noted that 28.5 % of the Australian general population is overseas born (according to the 2016 census) so the proportion of PLHIV is consistent with those figures. Secondly, the CD4 back projection method, to estimate the number of undiagnosed PLHIV may not be as accurate when looking at sub populations for countries over represented in late presentations (mainly due to cultural or religious stigma).

Additionally, although the focus of the paper is on the apparent inequality of health outcomes for migrants, there is no separation of indigenous vs non indigenous status in the Australian born group. The poorer cascade for Australian born females suggests this may be an important issue.

Finally, the assumption that overseas born PLHIV is a surrogate for inability to access care is a bit of an over simplification. Many overseas born people do have PR or residency and can access Medicare as well as anyone else. For those without Medicare, all sexual health clinics provide free care and can access free antiviral medication now. It would seem that the key barrier is reluctance of the migrant to access testing or care, rather than these services are not available to them. this is reflected in the increasing rates of on therapy and VS in all groups but not in any improvement in the proportion diagnosed.

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

9 Jan 2020

Submitted filename: Responses to reviewers_FINAL.docx

Click here for additional data file.

14 Jan 2020

Dear Dr. Marukutira,

Thank you very much for re-submitting your manuscript "Gaps in the HIV diagnosis and care cascade for migrants in Australia: A cross-sectional study (2013-2017)" (PMEDICINE-D-19-03230R1) for consideration at PLOS Medicine for our upcoming special issue on refugee and migrant health.

I have discussed the paper with editorial colleagues and the guest editors for the special issue, and it was also seen again by one reviewer. I am pleased to tell you that, provided the remaining editorial and production issues are dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

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Please let me know if you have any questions. Otherwise, we look forward to receiving the revised manuscript soon.

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Richard Turner, PhD

Senior Editor, PLOS Medicine

rturner@plos.org

------------------------------------------------------------

Requests from Editors:

Please adapt the title slightly: we suggest "Gaps in the HIV diagnosis and care cascade for migrants in Australia, 2013-2017: a cross-sectional study".

Please revise your abstract, aiming to improve readability of the "methods and findings" subsection.

At line 4 in your abstract, we suggest adding a few words to quote the UNAIDS targets.

At line 24 (and line 246 in the main text), please quote the number or proportion of males, say, in the total cohort.

To the final sentence of the "methods and findings" subsection of your abstract, please add 1-2 further limitations. We ask you to include the point made at line 379, i.e., that estimation of the "second 90" was done by assessing those in care.

Please restructure the "author summary" so that it consists of the existing three sections, but with each section comprising 3-4 bulleted points. The individual points should consist of one sentence, in general. Please consult one or two recent research papers published in PLOS Medicine to get a sense of the preferred style.

At line 254 and elsewhere in the ms, please substitute "(Table 1)". In general, square brackets should only be used inside parentheses.

At line 291, please remove "by 6%" (which is implicit in the point estimate) to leave "... increased (IRR 1.06 ... )".

Please add initial zeroes to the p values in table 2.

At line 358, should that be "surveillance data"?

Throughout the text, please adapt the formatting of reference callouts as follows: "... risk of transmission [36,37].".

In the reference list, please ensure that journal names are abbreviated as appropriate (e.g., reference 4).

Please remove ampersands from the reference list (e.g., reference 10).

Please correct the spelling of the journal name for reference 15.

To the attached STROBE checklist, please add paragraph numbers, where applicable, to the rightmost column.

Comments from Reviewers:

*** Reviewer #2:

The authors have addressed my concerns and I now recommend publication

Peter Flom

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

30 Jan 2020

Submitted filename: Responses to reviewers.docx

Click here for additional data file.

31 Jan 2020

Dear Dr. Marukutira,

On behalf of my colleagues and the academic editor, Dr. Paul Spiegel, I am delighted to inform you that your manuscript entitled "Gaps in the HIV diagnosis and care cascade for migrants in Australia, 2013-2017: A cross-sectional study" (PMEDICINE-D-19-03230R2) has been accepted for publication in PLOS Medicine.

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