Impact of viral suppression among persons with HIV upon estimated HIV incidence between 2010 and 2015 in the United States.

BACKGROUND: The suppression of viremia among persons with HIV (PWH) using antiretroviral therapy has been hypothesized to reduce HIV incidence at the population level. We investigated the impact of state level viral suppression among PWH in the United States on estimated HIV incidence between 2010 and 2015.
METHODS: Viral suppression data and HIV incidence estimates from the National HIV Surveillance System were available from 29 states and the District of Columbia. We assumed a one year delay for viral suppression to impact incidence. Poisson regression models were used to calculate the estimated annual percent change (EAPC) in incidence rate. We employed a multivariable mixed-effects Poisson regression model to assess the effects of state level race/ethnicity, socioeconomic status, percent men who have sex with men (MSM) and hepatitis C virus prevalence as a proxy for injection drug use on HIV incidence.
FINDINGS: Fitted HIV incidence for 30 jurisdictions declined from 11.5 in 2010 to 10.0 per 100,000 population by 2015 corresponding with an EAPC of -2.67 (95% confidence interval [95%CI] -2.95, -2.38). Southern states experienced the highest estimated incidence by far throughout this period but upon adjustment for viral suppression and demographics there was a 36% lower incidence rate than Northeast states (adjusted rate ratio [aRR] 0.64; 95%CI 0.42, 0.99). For every 10 percentage point (pp) increase in viral suppression there was an adjusted 4% decline in HIV incidence rate in the subsequent year (aRR 0.96; 95%CI 0.93, 0.99). While controlling for viral suppression, HIV incidence rate increased by 42% (aRR 1.42 95%CI 1.31, 1.54) for every 5 pp increase in percent Black race and by 27% (aRR 1.27 95%CI 1.10, 1.48) for every 1 pp increase in percent MSM in states.
INTERPRETATION: A decline in estimated HIV incidence from 2010 to 2015 was associated with increasing viral suppression in the United States. Race and sexual orientation were important HIV acquisition risk factors.

Introduction

Highly active antiretroviral therapy–subsequently referred to as antiretroviral therapy (ART)–was introduced in the United States in 1996. Shortly thereafter the United States Centers for Disease Control and Prevention (CDC) reported the first substantial decline in AIDS deaths [1]. Health economists determined that by 2003 at least three million life years had been saved in the United States as a direct result of ART [2].

Treatment of HIV-infected persons to prevent the onward transmission of the virus to uninfected persons has been the recommendation of public health officials in the United States since 2012. Viewed from the perspective of public health prevention, this strategy is sometimes referred to as ‘treatment as prevention’ and has contributed to the announcement of a new initiative by the United States government namely ‘Ending the HIV Epidemic’ [3]. A treatment as prevention approach was supported by the early observation that in the island of Taiwan, after ART was provided freely to a population of approximately 4,300 persons with HIV (PWH), the transmission rate of HIV declined [4]. Ample proof of the transmission-blocking potential of ART between sero-discordant couples was later published in 2011 as the chief outcome of a multi-country randomized clinical trial [5]. Subsequent analysis of this trial showed that almost all transmissions to uninfected partners in this trial occurred from HIV-infected participants whose last index viral load before the estimated date of infection of their uninfected partners exceeded 40,000 copies/mL [6].

Similar associations between reduced viral load and reduced HIV incidence were observed in British Columbia, Canada, and in the context of a community trial in India. In British Columbia, a population-level longitudinal analysis of province-wide registries showed a 1% decline in HIV incidence for every 1% increase in viral suppression [7]. The authors concluded that ART expansion between 1996 and 2012 was associated with “a sustained and profound population-level decrease in morbidity, mortality and HIV transmission.” In India, a cluster-randomized trial was conducted among more than 26,000 injection drug users and men who have sex with men (MSM) from 22 communities. These researchers concluded that to reduce HIV incidence by 1%, the viremia in the population would have to be reduced by 4.3% and ART use by PWH would have to increase by 19.5% [8].

In the United States, a Poisson model using “community viral load” in the city of San Francisco showed that an increase in viral suppression from 45% to 78% was associated with a 46% decline in diagnoses of HIV between 2004 and 2008 [9]. However, the impact of viral suppression on incident HIV has not been previously reported for the United States at the state, regional or national levels.

Over the past decade, jurisdictions have increasingly provided reports of viral load data from persons living with diagnosed HIV and CD4+ lymphocyte counts of newly diagnosed cases to the CDC. The latter has allowed CDC to estimate incident HIV by jurisdiction. In this paper, we examined the impact of viral suppression on estimated incident HIV by jurisdiction in the six-year period from 2010 to 2015.

Methods

The National HIV Surveillance System (NHSS) serves as the source for both HIV diagnosis and viral suppression data. CDC estimates and publishes HIV incidence using the reported diagnosis data following the methodology of Song et al. using a CD4 depletion model to estimate time between infection and diagnosis [10, 11]. The estimation method also makes use of first reported CD4 cell count after HIV diagnosis, assuming that persons are ART naïve at the time. Reporting of first CD4 test result after diagnosis is a required element in NHSS and significant progress has been made in reporting of all levels and percentages of CD4 test results [12]. For this analysis, we used CDC’s published HIV incidence estimates among persons 13 years or older (adults and adolescents) for the years 2010 through 2015 [13].

Relative standard errors (RSEs) were previously calculated for estimates of HIV incidence per state. Estimates with an RSE > 50% are considered statistically unreliable and ordinarily not published, and were not included in this analysis. To understand the potential impact of this source of missing data, a sensitivity analysis was done by including these suppressed incidence estimates in the final statistical model to evaluate the change in fitted incidence rates.

We included 2010–2015 state-level demographic variables in the analysis, including percent less than high school graduate, percent Black/African American, percent Hispanic, percent uninsured, and percent below poverty level. These demographic data were extracted from the American Community Survey and the Current Population Survey [14, 15]. Approximately 60% of PWH in the United States are gay, bisexual, or other MSM. We included published estimates of percentage of MSM among adult men for each state [16]. Recent increases in injection drug use in the United States has resulted in growing hepatitis C (HCV) and HIV infections in some rural areas. In this study, we also included prevalence of HCV infection by state in the analysis as a proxy for the prevalence of injection drug use using published estimates based on National Health and Nutrition Examination Survey 2009–2013 [17].

Viral suppression among persons living with HIV (diagnosed and undiagnosed) was extracted from various supplemental surveillance reports published by CDC and is freely available online. The percent virally suppressed was calculated using the following sets of numbers. The numerator was the number of persons with suppressed viral load test results reported to CDC from jurisdictions with complete laboratory reporting of CD4 and viral load test results. Standard surveillance definition of <200 copies/mL at the most recent test result in each year, was used to define viral suppression in a given calendar year. The denominator consisted of estimated total prevalence of HIV for the same year, count of persons living with diagnosed HIV plus estimate of persons living with undiagnosed HIV. The NHSS data is updated regularly. The most recent numerator and denominator data can be accessed from either the HIV surveillance supplemental reports [12, 13]. or the National Center for HIV/AIDS Viral Hepatitis STD and Tuberculosis Prevention’s AtlasPlus, available at https://www.cdc.gov/nchhstp/atlas/index.htm. To assess the impact of viral suppression on estimated HIV incidence between 2011 and 2015, a one-year lag in viral suppression was used thereby restricting the viral suppression data in our analysis to the 2010–2014 time frame. Although viral suppression data were available for 39 jurisdictions for at least one year, we restricted the analysis to 30 jurisdictions (29 states and the District of Columbia) with data available for 60% (3 years or more) of the time frame to ensure evaluation of changes over time.

Means and standard deviations were used to summarize percent viral suppression and state level demographic variables by year. Quartiles for estimated HIV incidence rate and percent viral suppression were calculated across the entire time frame, and used to evaluate changes in these measures over time. Estimated annual percent changes (EAPC) in HIV incidence with corresponding fitted yearly rates were determined using Poisson regression models that included a fixed effect for year for each jurisdiction and for all 30 jurisdictions combined. Fitted yearly incidence rates were also determined by region (Northeast, Midwest, South, and West). Unadjusted and adjusted rate ratios associated with differences in percent viral suppression and demographic characteristics were calculated using multivariable mixed-effects Poisson regression models that included a fixed effect for year, and a random intercept to account for within-state correlations. Percent viral suppression and state level demographics were treated as continuous covariates and adjusted models included 2010 estimated HIV incidence rate as a covariate. All analyses were conducted using SAS version 9.4 and R version 3.5.0.

Results

Of the 30 United States jurisdictions included in the analysis, one third were located in the Midwest and one third were in the South (Table 1). State specific fitted annual HIV incidence rates showed that 17 of 30 jurisdictions experienced negative estimated annual percentage change (EAPC) between 2010 and 2015 including three of the most populous states: California, New York and Texas (Table 2). In 2010, Washington DC had the highest fitted incidence of any jurisdiction during this period at 149.7 per 100,000 population but also the greatest estimated annual percent decline, -12.7 (95% confidence intervals [CI] -15.0, -10.3). Seven states had positive EAPC with Indiana and Hawaii experiencing the largest increases, 9.1 (95% CI 6.0, 12.3) and 14.0 (95% CI 6.7, 21.8) respectively.

Table 1

Estimated HIV incidence rate, state level viral suppression and demographics by year for 29 states and the District of Columbia*.

	2010	2011	2012	2013	2014	2015	Percent Change from 2010 to 2015
Estimated HIV Incidence Rate per 100,000	17.1	16.6	16.1	15.6	15.7	15.6	-8.7
	Mean (standard deviation)
Percent Viral Suppression	32.3 (6.6)	36.2 (5.9)	40.2 (6.4)	45.4 (7.6)	47.4 (7.6)	49.4 (7.1)	53.0
Percent Less Than High School Graduate	10.9 (3.4)	10.6 (3.3)	10.1 (3.2)	9.8 (3.1)	9.5 (3.0)	9.3 (3.0)	-15.0
Percent Black	11.9 (12.6)	11.8 (12.5)	11.9 (12.3)	11.9 (12.3)	12.0 (12.3)	11.9 (12.1)	0.3
Percent Hispanic	9.0 (8.8)	9.1 (8.9)	9.3 (8.9)	9.4 (8.9)	9.6 (9.0)	9.8 (9.0)	9.3
Percent Uninsured	13.8 (3.9)	13.6 (3.9)	13.2 (4.0)	13.0 (3.6)	10.7 (3.5)	8.8 (3.3)	-36.0
Percent Below Poverty Level	13.9 (3.5)	14.0 (3.4)	13.7 (3.3)	14.3 (4.0)	13.5 (3.8)	12.3 (2.9)	-11.5
	Mean (standard deviation)
Percent MSM 2009–13¶	3.5 (2.5)
Prevalence of HCV Infection 2013–16¶	0.9 (0.4)
Region†¶
Northeast	10.0% (3 states)
Midwest	33.3% (10 states)
South	33.3% (9 states + DC)
West	23.3% (7 states)

* States were included in the subset if viral suppression data was available for 60% of study years, 2010–14.

† States included in the subset by region: Northeast (Maine, New Hampshire, New York); Midwest (Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Nebraska, North Dakota, South Dakota, Wisconsin); South (Alabama, District of Columbia [DC], Georgia, Louisiana, Maryland, South Carolina, Tennessee, Texas, Virginia, West Virginia); West (Alaska, California, Hawaii, Oregon, Utah, Washington, Wyoming).

¶ Percent MSM and prevalence of HCV infection were estimated from the noted data sources over the entire timeframe indicated, not separately by year.

Note. Data sources include: National HIV Surveillance System (estimated HIV incidence and viral suppression for persons with HIV); American Community Survey 2010–15 (percent less than high school graduate, Black, Hispanic, uninsured); Current Population Survey 2010–15 (percent below poverty level); published estimates using data from the American Community Survey 2009–2013 (percent men who have sex with men [MSM]) [15]; National Health and Nutrition Examination Survey 2013–16 (prevalence of HCV infection).

Table 2

Estimated annual percent change (EAPC) in HIV incidence rate by state with corresponding fitted annual incidence rates per 100,000.

Fitted incidence rate	2010	2011	2012	2013	2014	2015	EAPC (95% CI)*
30 Jurisdictions	11.5	11.2	10.9	10.6	10.3	10.0	-2.67 (-2.95, -2.38)
Northeast:
Maine¶	-	-	-	-	-	-	-
New Hampshire¶	-	-	-	-	-	-	-
New York	26.1	24.7	23.4	22.1	20.9	19.8	-5.38 (-6.40, -4.35)
Midwest:
Illinois	14.2	14.0	13.7	13.4	13.1	12.9	-1.98 (-3.67, -0.25)
Indiana	7.8	8.6	9.3	10.2	11.1	12.1	9.13 (6.01, 12.33)
Iowa†	3.6	3.7	3.9	4.1	-	-	4.69 (-13.87, 27.24)
Michigan	8.9	8.8	8.8	8.7	8.7	8.6	-0.59 (-3.00, 1.89)
Minnesota†	7.5	7.2	6.9	6.6	6.3	6.1	-4.17 (-7.76, -0.43)
Missouri	10.7	10.1	9.5	8.9	8.4	7.9	-5.85 (-8.71, -2.89)
Nebraska†	6.5	5.7	5.1	-	-	-	-11.56 (-66.42, 132.89)
North Dakota¶	-	-	-	-	-	-	-
South Dakota¶	-	-	-	-	-	-	-
Wisconsin†	5.1	5.1	5.1	5.0	5.0	4.9	-0.81 (-4.96, 3.52)
South:
Alabama	15.7	15.1	14.6	14.1	13.6	13.1	-3.62 (-6.25, -0.92)
District of Columbia	149.7	130.7	114.1	99.6	87.0	75.9	-12.69 (-15.05, -10.27)
Georgia	30.9	30.0	29.0	28.1	27.3	26.4	-3.11 (-4.44, -1.77)
Louisiana	25.6	25.7	25.9	26.1	26.2	26.4	0.65 (-1.46, 2.79)
Maryland	30.0	28.3	26.7	25.1	23.7	22.3	-5.77 (-7.50, -4.00)
South Carolina	18.9	18.0	17.2	16.4	15.7	14.9	-4.55 (-6.97, -2.07)
Tennessee	15.9	15.5	15.0	14.6	14.2	13.7	-2.92 (-5.17, -0.61)
Texas	21.8	21.3	20.8	20.3	19.8	19.4	-2.28 (-3.26, -1.29)
Virginia	13.0	13.1	13.2	13.2	13.3	13.4	0.61 (-1.58, 2.86)
West Virginia†	4.2	4.4	4.7	5.0	5.3	-	6.26 (-5.94, 20.04)
West:
Alaska¶	-	-	-	-	-	-	-
California	16.6	16.4	16.2	16.1	15.9	15.7	-1.06 (-1.97, -0.13)
Hawaii†	6.2	7.0	8.0	9.2	10.4	11.9	13.99 (6.66, 21.83)
Oregon†	7.6	7.1	6.7	6.3	5.9	5.5	-6.10 (-10.26, -1.74)
Utah†	5.0	5.1	5.3	5.4	5.5	5.6	2.31 (-3.75, 8.75)
Washington	9.2	8.8	8.4	8.0	7.6	7.3	-4.74 (-7.60, -1.79)
Wyoming¶	-	-	-	-	-	-	-

* EAPC and fitted yearly HIV incidence rates for each state determined from separate Poisson regression models including a fixed effect for year.

† One or more years of HIV incidence estimates for these states had relative standard errors of 30%-50%.

¶ EAPC could not be estimated if a state had <3 years of available HIV incidence estimates.

Fitted HIV incidence rates determined from a mixed effects Poisson regression model showed that all four United States regions experienced declines in HIV incidence from 2010 to 2015 (Fig 1). The Southern states experienced the greatest decline but also retained–by far–the highest fitted annual rate at 18.65 per 100,000 by 2015.

Fig 1

Fitted HIV incidence rate by region, United States 2010–2015.

Fitted HIV incidence rate determined using a mixed-effects Poisson regression model including region, a fixed effect for year, and a random intercept.

Comparing 2015 with 2010, most states and DC either began reporting viral suppression data or had improvements in the proportion of PWH virally suppressed (Fig 2). Most states did not experience changes to a lower quartile of estimated HIV incidence rate, although one populous state (New York) did, as did two others (Minnesota and Oregon). Overall mean (±standard deviation) viral suppression among PWH steadily increased from 32.3% ± 6.6% in 2010 to 49.4% ± 7.1% in 2015 (Table 1) for the 30 jurisdictions.

Fig 2

Percentage viral suppression in persons with HIV and estimated HIV incidence in 2010 and 2015, United States.

The subset includes 30 jurisdictions (29 states and the District of Columbia) with viral suppression data available for 3 years or more. Estimated HIV incidence rate and percent viral suppression categories were determined by quartiles calculated across the entire time frame 2010–15.

After adjustment for proportions of persons with less than a high school education, Black race, Hispanic ethnicity, below the poverty line, uninsured, MSM, HCV prevalence and virally suppressed PWH, the multivariable model showed that Northeastern states had the highest fitted HIV incidence rate between 2011 and 2015 (Fig 3). The high adjusted incidence rates observed in the Northeast in this analysis contrast with unadjusted incidence rates observed in the South (Fig 1).

Fig 3

Fitted HIV incidence rate by viral suppression in previous year (%) and U.S. region, 2011–2015.

Fitted HIV incidence rate determined using a multivariable mixed-effects Poisson regression model including all variables in Table 3, fixed effects for year and 2010 HIV incidence rate and a random intercept.

Table 3

Unadjusted and adjusted associations of estimated HIV incidence rate with state level viral suppression and demographic variables.

	Unadjusted Rate Ratio*		Adjusted Rate Ratio†
	(95% CI)	p-value	(95% CI)	p-value
Viral Suppression (%) in previous year	1.02 (0.99, 1.05)	0.117	0.96 (0.93, 0.99)	0.005
(per increase of 10 pp)
Percent < High School Graduate	1.07 (0.95, 1.21)	0.243	1.20 (1.04, 1.38)	0.014
(per increase of 5 pp)
Percent Black	1.34 (1.27, 1.42)	< 0.001	1.42 (1.31, 1.54)	< 0.001
(per increase of 5 pp)
Percent Hispanic	1.04 (0.89, 1.21)	0.642	1.06 (0.99, 1.13)	0.091
(per increase of 5 pp)
Percent Uninsured	0.93 (0.91, 0.95)	< 0.001	0.95 (0.92, 0.97)	< 0.001
(per increase of 5 pp)
Percent Below Poverty Level	1.05 (1.02, 1.08)	0.002	1.10 (1.07, 1.14)	< 0.001
(per increase of 5 pp)
Percent MSM 2009–13	1.22 (1.12, 1.33)	< 0.001	1.27 (1.10, 1.48)	0.002
(per increase of 1 pp)
Prevalence of HCV Infection 2013–16	2.83 (1.58, 5.09)	< 0.001	1.00 (0.69, 1.44)	0.995
(per increase of 1 pp)
Region
Northeast	1.0		1.0
Midwest	0.90 (0.38, 2.11)	0.808	0.70 (0.50, 0.99)	0.048
South	2.48 (1.08, 5.69)	0.032	0.64 (0.42, 0.99)	0.045
West	1.05 (0.42, 2.63)	0.915	0.72 (0.49, 1.06)	0.099

* Results determined using a multivariable mixed-effects Poisson regression models including the variable of interest, a fixed effect for year, and a random intercept.

† Results determined using a multivariable mixed-effects Poisson regression model including all variables in the table, fixed effects for year and 2010 HIV incidence rate, and a random intercept.

pp = percentage point.

In the multivariable model, we found a 4% decline in estimated HIV incidence rate for every 10 percentage point (pp) increase in viral suppression (adjusted rate ratio [aRR] = 0.96; 95% confidence interval (CI) = 0.93, 0.99, Table 3). While adjusting for viral suppression and relevant state level demographic characteristics, every 5 pp increase in the proportion of persons with less than a high school education, of Black race and below the poverty line, higher HIV incidence rates were observed: (aRR 1.20, 95% CI 1.04, 1.38; aRR 1.42, 95% CI 1.31, 1.54; aRR 1.10, 95% CI 1.07, 1.14; respectively). For every 1 pp increase in adult men who were MSM, there was a 27% increase in HIV incidence rate (aRR 1.27, 95% CI 1.10, 1.48). We found a 5% (aRR 0.95; 95% CI 0.92, 0.97) decline in HIV incidence rate for every 5 pp increase in percent uninsured.

The largest impact of viral suppression after adjusting for relevant state level demographic characteristics was found in the Southern states: aRR 0.64 (95% CI 0.42, 0.99) as compared with the Northeastern states (Table 3). Impacts were slightly less in the Midwest: aRR 0.70 (95% CI 0.50, 0.99) and the West: aRR 0.72 (95% CI 0.49, 1.06). Fig 3 suggests that if percent viral suppression increased from 30% to 50%, the fitted incidence would decrease from 18.8 to 17.3 per 100,000 in the Northeast in 2015, while it would decrease from 12.1 to 11.1 per 100,000 in the South.

Although HCV prevalence was strongly associated with a RR of 2.83 in the unadjusted model, it was not upon adjustment (aRR 1.00), suggesting that trends in injection drug use (for which HCV prevalence serves as a proxy) during these years was not an important independent driver of HIV incidence.

The sensitivity of this model to missing data was evaluated by including HIV incidence estimates that are ordinarily suppressed in the surveillance system due to being statistically unreliable with relative standard error >50%. No changes in fitted incidence rates larger than 10% were found.

Discussion

We observed that between 2010 and 2015 among 29 states and the District of Columbia every 10 pp increase in viral suppression during the previous year was associated with an adjusted 4% decline in the estimated incidence rate of HIV in the subsequent year. This statistical association is consistent with the magnitude of the effects observed in community-randomized trials conducted in India, the SEARCH trial and the PopART trial, as well as the public health experience of British Columbia [7, 8, 18]. By scaling the statistics of published data, these studies observed a 2.3%, 6.7%, 5.4% and a 10% decline in HIV incidence rate for every 10 pp increase in viral suppression, respectively. Our ecologic analysis is remarkable considering that the populations of these 30 jurisdictions amount to 64.5% of the United States population in 2010, and are not only diverse in income, race, and ethnicity but are also subject to diverse health insurance contexts.

By virtue of having the highest incidence of any United States region, Southern states also have the opportunity to make the greatest impact on incident HIV with better viral suppression of PWH. In 2014, Southern states account for an estimated 44% percent of all PWH (diagnosed and undiagnosed) in the United States despite being populated by only a third (37%) of the overall population. Geographically, Southern states are a focus region for the ‘Ending the HIV Epidemic’ initiative.

Notable national demographic changes during the six-year period analyzed included a 15.0% decline in the proportion with less than a high school education, an 11.5% decline in the proportion of the United States population below the poverty level and a 36.0% decline in the percentage uninsured, particularly in 2014 and 2015. After adjustment for viral suppression and demographic characteristics, HIV incidence rate was 36% lower in Southern states compared to northeastern states. While health insurance coverage tends to be better in Northeastern states than Southern states [19], an analysis of private health insurance billing data between 2012 and 2013 found that the percentage of persons without an antiretroviral prescription was highest for persons residing in the Northeast region at 30.8% [20]. This observation suggests a possible link between reduced viral suppression among privately insured diagnosed PWH and HIV incidence in the Northeast.

In multivariable analysis, state level percent Black race had the strongest association with the estimated HIV incidence rate followed by percent MSM. Blacks/African Americans endure a disproportionate burden of new HIV diagnoses in the United States: in 2017, they accounted for 13% of the United States population but 43% of new HIV diagnoses. This racial disparity in HIV diagnoses has been linked to poverty, substandard education, unstable housing, inadequate health insurance, disproportionate incarceration rates and limited social mobility [21-23]. Additionally, gay and bisexual men accounted for 67% of all HIV diagnoses in 2016 despite being approximately 4% of the United States male population [16] and, of these new diagnoses in MSM, 38% were among Black/African American persons. Rather than geographic region being the explanatory variable, confounding state level demographic variables appear to explain the disproportionate burden of HIV incidence in Northeastern states.

We observed a trend of overall increases in viral suppression and decreased HIV incidence across the United States. In 2014, the major provisions of the Affordable Care Act (ACA) of 2010 –which included an expansion of Medicaid (the federal insurance program for persons requiring health insurance)–came into force. This expansion played a vital role in increasing access to and provision of care and treatment for PWH. However, not all states expanded Medicaid and some essential HIV services and medications were not covered by Medicaid. Typically, these outstanding services (e.g., case management, transportation, pharmacy services) were provided by the Ryan White HIV/AIDS Program (RWHAP), a federal program established in 1990 to fill gaps in HIV care and treatment for low-income PWH [24]. By 2016, the uninsured share of the United States population had roughly halved, with estimates ranging from 20 to 24 million additional people covered. The decline in the uninsured population was captured in our analysis.

Given this context, one may examine the interplay we observed between HIV incidence, insurance and poverty. While controlling for percent viral suppression, our analysis showed a 10% increase in the HIV incidence rate for every 5 pp increase in the percent of persons living below the national poverty level, but that for every 5 pp increase in the percent of uninsured persons was associated with a 5% decrease in the HIV incidence rate. Superficially, these two observations may appear contradictory because poverty would appear to correlate with lack of insurance. Indeed, nearly half of all PWH in the United States have a household income at or below the federal poverty line and 28.4% of PWH had no insurance [25]. However, a recent analysis of the RWHAP showed that, among recipients, viral suppression increased from 69.5% in 2010 to 85.9% in 2017 [26]. The RWHAP could explain the association we observed of a decreased HIV incidence rate among the uninsured. A separate analysis of the period 2009–2013 showed that, among 18,095 PWH with and without RWHAP assistance, those who were uninsured and underinsured were more likely to receive ART and be virally suppressed than those with other types of healthcare coverage [27].

Indiana and West Virginia had increases in incident HIV over time, and were also among the states most impacted by the growing opioid and injection drug use epidemics [28]. We therefore attempted to assess whether injection drug use impacted the relationship between viral suppression and HIV incidence. We included HCV prevalence by jurisdiction in our model as a proxy for injection drug use because HCV infection is strongly associated with injection drug use. We did not observe an association between HCV prevalence and HIV incidence rate after adjusting for relevant demographic characteristics. This observation is consistent with surveillance reports from the same period that show that injection drug use was a relatively low contributor to the annual number of new diagnoses.

Our analysis has a number of limitations, chief among them being that by 2015 not all states were reporting all viral load and CD4+ lymphocyte test results data. Even some of the 30 jurisdictions in our subset did not have viral suppression data for all years between 2010 and 2015, or had HIV incidence estimates that were statistically unreliable and therefore not included in the analysis. Among the states not included was Florida, which–after California and Texas–is the third most populous state. Nevertheless, the jurisdictions included in this analysis represent almost two thirds of the United States population. Another limitation is that we used a single measure of viral load to determine who was or was not virally suppressed; however single measures of viral load do not necessarily approximate durable viral suppression [29]. While some analyses examined the impact of ART coverage on HIV incidence [7], a strength of our analysis is the examination of the use of viral suppression to impact HIV incidence rather than ART coverage given the well-known phenomenon of nonadherence to ART [30]. Finally, our analysis does not account for changes in condom use or the use of pre-exposure HIV prophylaxis (PrEP). The increases of sexually transmitted diseases during this period in the United States–to the highest recorded levels–appear to confirm that condom use declined during this period [31-33]. CDC recommended PrEP in 2014 and by 2016 an estimated 78,000 people had filled a prescription for it [34]. By 2016 only 7% of the estimated 1.1 million U.S. persons who had indications for PrEP were prescribed it [35]. It is unlikely that PrEP-related decreases in HIV acquisition would have significantly affected our analysis.

A key area for improvement in order to more fully realize the strategy of treatment as prevention is recognizing that an estimated 43% of new HIV transmissions were generated by persons who were aware of their HIV infection but were not in care, and 38% of new infections were generated by persons unaware of their status [36]. Therefore, identifying persons with undiagnosed HIV and re-engaging and initiating ART for approximately half a million PWH who were not linked to care or who dropped out of care are vital to successfully prosecuting the ‘Ending the HIV Epidemic’ campaign in the United States.

In conclusion, our ecologic analysis suggests that between 2010 and 2015, the increase in viral suppression observed after the rollout of ART was associated with a modest reduction in the United States’ estimated HIV incidence rate. Initiating and adhering to ART with regimens that include integrase inhibitors and single-tablet triple therapies have made viral suppression easier than ever before. Based upon another ecologic analysis that controlled for viral suppression, PrEP may already have made a contribution towards reducing incident HIV in the United States [37]. Our analysis appears to underscore the importance of the RWHAP in helping uninsured and under-insured PWH achieve viral suppression and thereby reduce incident HIV, and that the greatest impact of viral suppression may be effected in Southern states. The ‘Ending the HIV Epidemic’ announced as a national strategy in 2019 is aimed at intensifying treatment as prevention and PrEP in the 48 counties with the highest burden of HIV in order to reduce national HIV incidence by 90% within 10 years [3]. This approach applies a lesson learned from results of the recently concluded randomized trials in Africa for treatment as prevention to go “beyond universal testing and treatment to universal testing, treatment and prophylaxis to achieve HIV epidemic control” [18].

4 Aug 2020

PONE-D-20-02032

Impact of viral suppression among persons with HIV upon estimated HIV incidence between 2010 and 2015 in the United States

PLOS ONE

Dear Dr. Samandari,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The reviewers have suggested some small changes and highlighted points including the potential confounding effects of undiagnosed HIV infections which we ask you to address in your revised manuscript.

Please submit your revised manuscript by 29th September 2020. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Anna C Hearps

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2.We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

3.We note that [Figure(s) 2] in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

1.    You may seek permission from the original copyright holder of Figure(s) [2] to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

2.    If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: General comments:

-This is a novel and important paper. The authors have written a very clear, descriptive, and ecological study with significant contribution to the literature.

-what is “most recent test in each year”? Recent to each end of year follow-up? Were multiple tests (or even cumulative time suppressed) considered?

-All the tables are clear; except table 2 I may suggest grouping (instead of alphabetical), but region so it is easier to see variation between states in each region

-In model, was co-linearity a factor with some of the demographic measures?

-If space allows, may I suggest a comment on ART coverage and testing rates by state in the discussion

-solid discussion + limitations section; very thorough; addressed gaps appropriately

Reviewer #2: Comments to the authors

This paper describes the association between state-level viral suppression among HIV-diagnosed individuals and the fittest HIV incidence rate in the following year across the United States. The authors conclude that increased viral suppression is associated with a modest decrease in HIV incidence in the following year, and report declining trends in HIV incidence across most US states during the study period. This paper describes important population-level evidence to support U=U and Treatment as Prevention, and would be of interest to PLOS-ONE readership. The statistical analysis is sound and appropriate, however there are some major limitations in using viral suppression estimates among HIV-diagnosed individuals as a proxy of community-level viraemia, which should be further discussed.

My overarching concern is that the authors have not accounted for undiagnosed HIV in their analysis. The authors state that viral suppression measures come from data reported to CDC from jurisdictions with complete reporting of HIV viral load tests, so the viral suppression parameter reflects the proportion of *diagnosed* individuals who are virally suppressed. However, the more proximal variable related to HIV incidence would be state-level viraemia, i.e. the proportion of the *entire population* which have detectable HIV. Therefore, large difference in rates of undiagnosed HIV across states / over time may bias results. Are there any estimates of HIV prevalence / rate of undiagnosed HIV in each state which could be used in conjunction with viral suppression rates among those diagnosed to estimate community-level viremia? If this is not possible this should be discussed in the limitations, or the authors should elaborate on the rationale for using viral suppression among those diagnosed as the driver of HIV incidence.

Lines 89-92. The authors state that HIV incidence is estimated using first reported CD4 cell count after HIV diagnosis and a CD4 depletion model. It would be prudent to elaborate on the methods of this model, and the parameters used. Two citations are given, however it is my understanding that each of the paper cited use slightly different methods / parameters in the depletion model. Can the authors provide the model specs or formulae in supplementary materials?

Line112. Can the authors provide a justification for using a 1-year lag period?

Lines 118-119: “Quartiles for estimated HIV incidence rate and percent viral suppression were calculated across the entire time frame”.. do you mean they were calculated for each state for each year? The results “Most states did not experience changes to a lower quartile” suggests this is the case. If so please clarify.. “Annual quartiles for…”

Table 3 please add p-values

Line 193-195: “The impact on HIV incidence rate in Southern states diminished after adjustment for viral suppression and demographic characteristics to being 36% lower compared with the Northeastern states.” This statement is slightly confusing, the impact of what? Suggest changing to something such as “After adjustment for viral suppression and demographic characteristics, HIV incidence rate was 36% lower in Southern states compared to northeastern states” or “The relative difference in HIV incidence in Southern states compared to Northeasterns states diminished after.”

Lines 251-252. “The increases of sexually transmitted diseases during this period in the United States – to the highest recorded levels – suggest that condom use did not increase.29” I think a citation here to actual data on condom use would be more suitable.

Line 264 “In conclusion, our ecologic analysis suggests that between 2010 and 2015, the increase in viral suppression observed after the rollout of ART modestly reduced the estimated HIV incidence rate in the United States.” Given the limitations mentioned and the ecological nature of this study, authors should avoid causal terms, eg reduced. Consider “was associated with a reduction in”.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Kate Salters

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

6 Sep 2020

Journal Requirements:

3.We note that [Figure(s) 2] in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

AUTHORS: The maps in Figure 2 were created using the urbnmapr package in R. This package uses shapefiles from the US Census Bureau, which are not protected under copyright.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: General comments:

-This is a novel and important paper. The authors have written a very clear, descriptive, and ecological study with significant contribution to the literature.

AUTHORS: Thank you.

-what is “most recent test in each year”? Recent to each end of year follow-up? Were multiple tests (or even cumulative time suppressed) considered?

AUTHORS: The most recent test in each year is the last recorded viral load test result within the given calendar year. Multiple tests were not considered, only the last recorded viral load test. Cumulative time suppressed was not considered.

-All the tables are clear; except table 2 I may suggest grouping (instead of alphabetical), but region so it is easier to see variation between states in each region

AUTHORS: This change has been made to Table 2.

-In model, was co-linearity a factor with some of the demographic measures?

AUTHORS: Yes, multicollinearity in the model was a concern since the demographic measures are correlated. However, the estimated rate ratios and precision estimates were similar between unadjusted and adjusted models, indicating validity of the estimated associations was likely not impacted by these correlations.

-If space allows, may I suggest a comment on ART coverage and testing rates by state in the discussion

AUTHORS: Although we reference several articles that studied the impact of ART coverage on HIV incidence (e.g., Montaner PLoS One 2014), we chose to examine the impact of viral suppression on HIV incidence given the well-known phenomenon of non-adherence to ART (Durham Int J STD AIDS 2018, Mills JAMA 2006, Wohl JAIDS 2017). Viral suppression is more proximal to incident HIV and if not achieved will lead to transmission of the virus (Cohen NEJM 2016, Table 2). We have added a sentence to highlight this strength of our analysis in the Discussion. Regarding testing rates, we believe Reviewer #1 is alluding to the concern with underdiagnosis of HIV in the US. We address this in a question raised by Reviewer #2 below and have modified a sentence in the Methods to more explicitly address this concern.

-solid discussion + limitations section; very thorough; addressed gaps appropriately

AUTHORS: Thank you.

Reviewer #2: Comments to the authors

This paper describes the association between state-level viral suppression among HIV-diagnosed individuals and the fittest HIV incidence rate in the following year across the United States. The authors conclude that increased viral suppression is associated with a modest decrease in HIV incidence in the following year, and report declining trends in HIV incidence across most US states during the study period. This paper describes important population-level evidence to support U=U and Treatment as Prevention, and would be of interest to PLOS-ONE readership. The statistical analysis is sound and appropriate, however there are some major limitations in using viral suppression estimates among HIV-diagnosed individuals as a proxy of community-level viraemia, which should be further discussed.

My overarching concern is that the authors have not accounted for undiagnosed HIV in their analysis. The authors state that viral suppression measures come from data reported to CDC from jurisdictions with complete reporting of HIV viral load tests, so the viral suppression parameter reflects the proportion of *diagnosed* individuals who are virally suppressed. However, the more proximal variable related to HIV incidence would be state-level viraemia, i.e. the proportion of the *entire population* which have detectable HIV. Therefore, large difference in rates of undiagnosed HIV across states / over time may bias results. Are there any estimates of HIV prevalence / rate of undiagnosed HIV in each state which could be used in conjunction with viral suppression rates among those diagnosed to estimate community-level viremia? If this is not possible this should be discussed in the limitations, or the authors should elaborate on the rationale for using viral suppression among those diagnosed as the driver of HIV incidence.

AUTHORS: The proportion of PWH virally suppressed for each state was calculated using all persons living with HIV (both diagnosed and undiagnosed) as the denominator, so undiagnosed HIV is accounted for in the calculation of this variable. Our methodology and source data are now more explicitly described in the revised manuscript.

Additionally, as an alternative, we had repeated the multivariable analysis to include the proportion of diagnosed HIV as a separate state-level variable in the model and the proportion of viral suppression was calculated only for those with diagnosed HIV. The results were similar, with the exception that the effect of region diminished after adjustment for the proportion with diagnosed HIV. These results are included below for comparison.

Unadjusted and adjusted associations of viral suppression (among only those diagnosed with HIV) and demographic variables with HIV incidence

Unadjusted Rate Ratio (95% CI)* Adjusted Rate Ratio (95% CI)**

Viral Suppression (%) in Previous Year

(per increase of 10%) 1.01 (0.98, 1.04) 0.95 (0.92, 0.98)

Percent Diagnosed with HIV in Previous Year

(per increase of 10%) 1.09 (1.05, 1.14) 1.08 (1.03, 1.14)

Percent < High School Graduate

(per increase of 5%) 1.07 (0.95, 1.21) 1.18 (1.03, 1.35)

Percent Black

(per increase of 5%) 1.34 (1.27, 1.42) 1.36 (1.27, 1.46)

Percent Hispanic

(per increase of 5%) 1.04 (0.89, 1.21) 1.09 (1.03, 1.15)

Percent Uninsured

(per increase of 5%) 0.93 (0.91, 0.95) 0.94 (0.91, 0.96)

Percent Below Poverty Level

(per increase of 5%) 1.05 (1.02, 1.08) 1.10 (1.06, 1.13)

Percent MSM 2009-13

(per increase of 1%) 1.22 (1.12, 1.33) 1.15 (1.01, 1.30)

Prevalence of HCV Infection 2013-16

(per increase of 1%) 2.83 (1.58, 5.09) 0.99 (0.73, 1.35)

Region

Northeast 1.0 1.0

Midwest 0.90 (0.38, 2.11) 0.92 (0.68, 1.24)

South 2.48 (1.08, 5.69) 0.81 (0.56, 1.15)

West 1.05 (0.42, 2.63) 1.00 (0.72, 1.39)

Lines 89-92. The authors state that HIV incidence is estimated using first reported CD4 cell count after HIV diagnosis and a CD4 depletion model. It would be prudent to elaborate on the methods of this model, and the parameters used. Two citations are given, however it is my understanding that each of the paper cited use slightly different methods / parameters in the depletion model. Can the authors provide the model specs or formulae in supplementary materials?

AUTHORS: It is beyond the scope of this paper to elaborate the methods of previously published models which our team did not design. While two citations reference the methods, we agree with Reviewer 2 that there is some ambiguity as to the source of the HIV incidence data by state. We have added a reference and modified the Methods section to read as follows: “The National HIV Surveillance System (NHSS) serves as the source for both HIV diagnosis and viral suppression data. CDC estimates and publishes HIV incidence using the reported diagnosis data following the methodology of Song et al. using a CD4 depletion model to estimate time between infection and diagnosis. The estimation method also makes use of first reported CD4 cell count after HIV diagnosis, assuming that persons were ART naïve at the time. Reporting of first CD4 test result after diagnosis is a required element in NHSS and significant progress has been made in reporting of all levels and percentages of CD4 test results. For this analysis, we used CDC’s published HIV incidence estimates among persons 13 years or older (adults and adolescents) during 2010 through 2015.”

Line112. Can the authors provide a justification for using a 1-year lag period?

AUTHORS: The 1-year lag period was used to most accurately assess the impact of viral suppression on HIV incidence, since viral suppression was defined using the most recent test result in a given year which could often be late in the calendar year. Without a 1-year lag, we would have assumed that all virally suppressed PWH would have been suppressed at the beginning of the year to impact HIV incidence in the same year which seems implausible.

Lines 118-119: “Quartiles for estimated HIV incidence rate and percent viral suppression were calculated across the entire time frame”.. do you mean they were calculated for each state for each year? The results “Most states did not experience changes to a lower quartile” suggests this is the case. If so please clarify.. “Annual quartiles for…”

AUTHORS: To assess trends in viral suppression and HIV incidence over time, quartiles were calculated using all data points from 2010-15, not separately for each year. In the methods we state “Quartiles … were calculated across the entire time frame.”

Table 3 please add p-values

AUTHORS: P-values have been added to Table 3.

Line 193-195: “The impact on HIV incidence rate in Southern states diminished after adjustment for viral suppression and demographic characteristics to being 36% lower compared with the Northeastern states.” This statement is slightly confusing, the impact of what? Suggest changing to something such as “After adjustment for viral suppression and demographic characteristics, HIV incidence rate was 36% lower in Southern states compared to northeastern states” or “The relative difference in HIV incidence in Southern states compared to Northeasterns states diminished after.”

AUTHORS: We agree with Reviewer 2 and have modified the sentence.

Lines 251-252. “The increases of sexually transmitted diseases during this period in the United States – to the highest recorded levels – suggest that condom use did not increase.29” I think a citation here to actual data on condom use would be more suitable.

AUTHORS: We have now provided two references to support this suggestion of a decline in condom use during this period: Paz-Bailey et al. AIDS 2016 found that in the 2005, 2008, 2011, and 2014 cycles of National HIV Behavioral Surveillance: “Among 5371 HIV-positive MSM, there were increases in concordant (19% in 2005 to 25% in 2014, P<0.001) and discordant condomless sex (15 to 19%,P < 0.001). The increases were not different by ART use.” Also, Harper et al. Sex Transm Dis. 2018 who reported that in the 2003–2015 National Youth Risk Behavior Surveys: “Between 2003 and 2015, significant declines in self-reported condom use were observed among black female (63.6% in 2003 to 46.7% in 2015) and white male students (69.0% in 2003 to 58.1% in 2015).”

Line 264 “In conclusion, our ecologic analysis suggests that between 2010 and 2015, the increase in viral suppression observed after the rollout of ART modestly reduced the estimated HIV incidence rate in the United States.” Given the limitations mentioned and the ecological nature of this study, authors should avoid causal terms, eg reduced. Consider “was associated with a reduction in”.

AUTHORS: We agree and the sentence has been modified.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

2 Oct 2020

Impact of viral suppression among persons with HIV upon estimated HIV incidence between 2010 and 2015 in the United States

PONE-D-20-02032R1

Dear Dr. Samandari,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Anna C Hearps

Academic Editor

PLOS ONE

8 Oct 2020

PONE-D-20-02032R1

Impact of viral suppression among persons with HIV upon estimated HIV incidence between 2010 and 2015 in the United States

Dear Dr. Samandari:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Anna C Hearps

Academic Editor

PLOS ONE