M.J.S. reports research grants from Merck and Pfizer. All other authors declare that no competing interests exist. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.
Conceived and designed the experiments: DBH. Performed the experiments: DBH. Analyzed the data: DBH. Contributed reagents/materials/analysis tools: KB KAG NAH MAH LPJ GDK MMK PTK RDM SN PP MJS TRS JHW AC HS JET JNM BR SJG. Wrote the paper: DBH. Made substantial contributions to interpretation of data: DBH KB KAG NAH MAH LPJ GDK MMK PTK RDM SN PP MJS TRS JHW AC HS JET KNA JNM ES SJG. Revised manuscript critically for important intellectual content: DBH KB KAG NAH MAH LPJ GDK MMK PTK RDM SN PP MJS TRS JHW AC HS JET KNA JNM BR ES SJG. Gave final approval of the version to be published: DBH KB KAG NAH MAH LPJ GDK MMK PTK RDM SN PP MJS TRS JHW AC HS JET KNA JNM BR ES SJG.
Current address: Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
U.S. state AIDS Drug Assistance Programs (ADAPs) are federally funded to provide antiretroviral therapy (ART) as the payer of last resort to eligible persons with HIV infection. States differ regarding their financial contributions to and ways of implementing these programs, and it remains unclear how this interstate variability affects HIV treatment outcomes.
We analyzed data from HIV-infected individuals who were clinically-eligible for ART between 2001 and 2009 (i.e., a first reported CD4+ <350 cells/uL or AIDS-defining illness) from 14 U.S. cohorts of the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD). Using propensity score matching and Cox regression, we assessed ART initiation (within 6 months following eligibility) and virologic suppression (within 1 year) based on differences in two state ADAP features: the amount of state funding in annual ADAP budgets and the implementation of waiting lists. We performed an
Among 8,874 persons, 56% initiated ART within six months following eligibility. Persons living in states with no additional state contribution to the ADAP budget initiated ART on a less timely basis (hazard ratio [HR] 0.73, 95% CI 0.60–0.88). Living in a state with an ADAP waiting list was not associated with less timely initiation (HR 1.12, 95% CI 0.87–1.45). Neither additional state contributions nor waiting lists were significantly associated with virologic suppression. Persons with an IDU history initiated ART on a less timely basis (HR 0.67, 95% CI 0.47–0.95).
We found that living in states that did not contribute additionally to the ADAP budget was associated with delayed ART initiation when treatment was clinically indicated. Given the changing healthcare environment, continued assessment of the role of ADAPs and their features that facilitate prompt treatment is needed.
Reducing HIV-related health disparities is a priority of the United States (U.S.) National HIV/AIDS Strategy (NHAS)
In particular, differences by state response to the Ryan White CARE Act Part B AIDS Drug Assistance Programs (ADAPs), which are used by about one-quarter of HIV-infected individuals in care in the United States
The published research on the clinical consequences of specific features of ADAPs, primarily based on mathematical modeling, has found the overall program to be cost-effective
To understand the association between state ADAP policies and treatment outcomes, we assessed differences in ART initiation and viral load suppression among newly treatment-eligible participants in U.S. cohorts of the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD), a collaboration of prospective cohort studies of HIV-infected individuals in the U.S. and Canada, between 2001 and 2009. We compared these outcomes based on two potentially unfavorable ADAP circumstances: not having additional state funding in the annual ADAP budget and the use of waiting lists. Our research question was whether individuals living in states under each of these circumstances were less likely to have timely ART initiation and virologic suppression, compared with similar individuals not living in states under the same circumstances. A secondary question was whether these differences were more pronounced among those with a history of injection drug use. We hypothesized that effects would be greater in this population, owing to their greater needs with respect to engagement in care and starting treatment
NA-ACCORD is a collaboration of single- and multi-site HIV cohorts that includes over 100,000 individuals from more than 100 research sites in the U.S. and Canada
The source population for our analyses consisted of HIV-infected individuals in the NA-ACCORD who were newly eligible to initiate ART between 2001 and 2009, based on existing treatment guidelines during this period (an incident AIDS-defining event or CD4+ lymphocyte [CD4+] count recorded <350 cells/uL)
Because we were interested in answering the question of whether individuals would have had different outcomes if they did not live in a state without a particular ADAP characteristic, we limited certain analyses to a subset of individuals who lived in states with that particular feature in place at the time of ART eligibility, and similar individuals who lived in states without that feature.
In a secondary analysis, we examined individuals with a documented history of injection drug use (IDU). To account for potential underreporting of IDU, we also included individuals without a documented history of IDU but with a diagnosis of hepatitis C infection recorded in the absence of either a report of hemophilia, contact with blood products, or among men, sex with men. While this may have included some individuals without a history of IDU, we conducted sensitivity analyses excluding these additional individuals.
Our first outcome of interest was time to ART initiation, using the date of ART eligibility (i.e., the first date that an incident AIDS-defining illness or a CD4+ count <350 cells/uL was recorded) as the time origin. Time to ART initiation was defined as the duration between the date of eligibility and the date an ART regimen was prescribed (denoted in the medical record), or if this was not available, when a regimen was started (denoted by self-report). Time was censored at six months after eligibility to focus on more timely treatment initiation. ART regimens comprised at least three active antiretroviral agents, including a protease inhibitor, a non-nucleoside reverse transcriptase inhibitor, an entry inhibitor, or an integrase strand transfer inhibitor; or three nucleoside reverse transcriptase inhibitors, including abacavir or tenofovir. Ritonavir in the presence of another protease inhibitor was not included in this definition.
The other outcome of interest was time from ART eligibility to viral load (VL) suppression (within one year). Suppression was based on a laboratory result report of an HIV-1 RNA level ≤500 copies/mL. This threshold was used to account for differences in detection limits of commercial assays over the study period
For each individual in our study, the two state ADAP features in place on the date of ART eligibility were assessed and stratified into dichotomous categories that could be classified as more cost-containing versus less cost-containing: (1) amount of state funding provided to the annual ADAP budget (none vs. any); and (2) use of waiting lists in the state (yes vs. no). Information on state ADAP features was derived from the results of surveys conducted by the National Alliance of State and Territorial AIDS Directors (NASTAD) and published in annual reports
Characteristic | 2001 | 2009 | ||||||
All U.S. states |
34 states |
All U.S. states |
34 states |
|||||
Median | IQR | Median | IQR | Median | IQR | Median | IQR | |
Demographic variables | ||||||||
Population density (per square mile) | 90 | 42–221 | 137 | 63–274 | 100 | 43–230 | 150 | 66–282 |
% of population that is of black race | 7.2 | 2.3–15.8 | 10.9 | 4.1–19.8 | 7.6 | 3.1–16.3 | 11.5 | 5.3–19.7 |
Annual household income (current U.S. dollars, thousands) | 51,004 | 46,473–58,205 | 51,663 | 47,095–56,861 | 49,909 | 45,455–56,568 | 49,271 | 45,036–56,853 |
% of population living below FPL | 10.5 | 8.5–14.1 | 11.1 | 8.5–14.2 | 13.3 | 10.9–15.8 | 13.9 | 11.7–16.6 |
State Medicaid HIV spending per capita | N/A | N/A | N/A | N/A | 18,757 | 15,768–22,710 | 19,621 | 16,417–23,088 |
AIDS Drug Assistance Program (ADAP) features | ||||||||
% state contribution to total ADAP budget expenditures | 9 | 0–21 | 14 | 3–28 | 11 | 0–25 | 19 | 5–31 |
States contributing to total ADAP budget, by percentage (N, %) | ||||||||
0% | N = 15 | 29% | N = 6 | 18% | N = 17 | 33% | N = 8 | 24% |
Less than 20% | N = 22 | 43% | N = 17 | 50% | N = 15 | 29 | N = 9 | 26% |
20% or more | N = 14 | 27% | N = 11 | 32% | N = 19 | 37% | N = 17 | 50% |
% of all available antiretroviral drugs on formulary | 100 | 100–100 | 100 | 100–100 | 100 | 97–100 | 100 | 97–100 |
Financial eligibility threshold as % of FPL | 300 | 230–350 | 300 | 281–370 | 300 | 300–400 | 300 | 300–400 |
States with waiting list at least once during study (N, %) | - | - | - | - | N = 20 | 39% | N = 11 | 32% |
Including the District of Columbia.
FPL = federal poverty level, IQR = interquartile range, N/A = not available. State demographic variables from annual U.S. Census population estimates and the Current Population Survey
Other individual-level variables assessed at the time of ART eligibility and included as potential confounders were age, race/ethnicity (black; Hispanic; white or other), sex and transmission risk (men who have sex with men; male IDU; female IDU; male heterosexual or other risk, female heterosexual or other risk), CD4+ count, HIV viral load, calendar year, and documented histories of drug abuse, alcohol abuse, and mental illness. Drug abuse, alcohol abuse, and mental illness were categorized on the basis of more specific diagnoses derived from electronic medical record diagnoses and chart reviews. As potential psychosocial barriers to ART initiation, they were grouped as a single ordinal variable, representing the number of barriers experienced
To account for differences in ART initiation influenced by characteristics of the cohorts or clinics themselves, we categorized cohorts into the following categories: multi-site clinical cohort, single-site clinical cohort, and interval cohort. Interval cohorts differ from clinical cohorts in both timing and data collection; individuals are followed at specified intervals (e.g., every six months) that are unrelated to health care visits, and data are collected according to defined protocols
State-specific characteristics related to population demographics and Medicaid spending may also affect decisions on how ADAPs are run, as well as ART initiation. To account for these potential confounding differences, we included the following state variables, linked to individuals by the year of ART eligibility and categorized into quartiles: population density
To estimate the effect of each ADAP characteristic on treatment outcomes, we used propensity score matching to account for potential differences between persons living in a state with a specific ADAP characteristic (“exposed” participants) and persons living in a state without that characteristic (“unexposed” participants). Details of the use of this method are included in
We also performed analyses that did not use propensity score matching but rather conventional multivariable Cox regression analysis. Such models may be less able to adjust for known confounders if there is limited covariate overlap, but use the entire study population instead of a more limited subset. We also used conventional Cox regression analysis for our pre-specified subgroup analysis among IDU, because we could not get adequate balance on confounders in the propensity score model.
To further explore the relationship between a state contribution to the annual ADAP budget and increases in ART initiation, we looked for evidence of a “dose-response” trend in state funding. Because our propensity score models used logistic regression and thus require a dichotomous “treatment”, we used conventional Cox models to explore this relationship. We created three levels of state funding: 0% of the total ADAP budget (i.e., no state contribution), >0% but <20%, and 20% or more.
Finally, we performed several sensitivity analyses to examine assumptions about the relationship between state ADAP characteristics and the outcomes of interest. These included use of alternate statistical methods, modifications to the exposure definition, and additional subgroup analyses (see
There were 8,874 individuals initially eligible between 2001 and 2009 for inclusion in this analysis.
Gray indicates the population of interest for the propensity score-matched analyses.
N | % | N | % | N | % | |
Age at eligibility, years (median, IQR) | 40 | 33–46 | 41 | 34–47 | 37 | 31–44 |
18–29 | 1,555 | 18 | 139 | 13 | 131 | 21 |
30–39 | 2,869 | 32 | 343 | 32 | 236 | 38 |
40–49 | 2,989 | 34 | 397 | 37 | 196 | 32 |
50–59 | 1,216 | 14 | 181 | 17 | 47 | 8 |
60+ | 245 | 3 | 22 | 2 | 10 | 1.6 |
Race/ethnicity | ||||||
Black (non-Hispanic) | 3,937 | 44 | 617 | 57 | 272 | 44 |
Hispanic | 1,631 | 18 | 57 | 5 | 40 | 7 |
White (non-Hispanic) | 2,944 | 33 | 382 | 35 | 293 | 47 |
Other (non-Hispanic) | 362 | 4 | 26 | 2.4 | 15 | 2.4 |
Sex and transmission risk | ||||||
Men who have sex with men | 3,839 | 43 | 368 | 34 | 282 | 46 |
Male injection drug user | 946 | 11 | 210 | 19 | 46 | 7 |
Male, heterosexual or other risk | 1,764 | 20 | 162 | 15 | 145 | 23 |
Female injection drug user | 387 | 4 | 115 | 11 | 12 | 1.9 |
Female, heterosexual or other risk | 1,938 | 22 | 227 | 21 | 135 | 22 |
Eligibility criteria | ||||||
CD4+ count 0–199 cell/uL | 3,118 | 35 | 274 | 25 | 224 | 36 |
CD4+ count 200–349 cells/uL | 5,464 | 62 | 775 | 72 | 380 | 61 |
Incident AIDS-defining illness (i.e., CD4+ count not <350 cells/uL) | 292 | 3 | 33 | 3 | 16 | 2.6 |
Viral load at eligibility | ||||||
501–999 copies/mL | 152 | 1.7 | 12 | 1.1 | 6 | 1 |
1,000–9,999 copies/mL | 1,299 | 15 | 156 | 14 | 56 | 9 |
10,000–99,999 copies/mL | 3,743 | 42 | 464 | 43 | 248 | 40 |
100,000+ copies/mL | 2,588 | 29 | 261 | 24 | 162 | 26 |
Missing | 1,092 | 12 | 189 | 18 | 148 | 24 |
ART = antiretroviral therapy, IQR = interquartile range. Percentages may not add up to 100 due to rounding.
See
In
Regarding the mechanisms undertaken by individual clinics to assist with access to ART drugs (
In the overall study population (N = 8,874), 56% of individuals initiated ART within six months of eligibility. Persons living in states not contributing to the ADAP budget were less likely to initiate ART within six months than persons living in states that did (39% vs. 58%).
HR | 95% CI | HR | 95% CI | |
No contribution (vs. any contribution) | ||||
Crude (N = 8,874) | 0.56 | 0.49–0.63 | 0.75 | 0.67–0.83 |
Dose-response effect (Ptrend) (N = 8,874) | <0.001 | 0.25 | ||
No contribution | 0.75 | 0.63–0.88 | 1.06 | 0.91–1.24 |
Contribution <20% | 0.90 | 0.82–0.99 | 1.07 | 0.97–1.17 |
Contribution >20% | 1.00 | Ref. | 1.00 | Ref. |
No contribution (vs. any contribution) | ||||
Crude | 0.40 | 0.31–0.51 | 0.78 | 0.64–0.96 |
Dose-response effect (Ptrend) | 0.005 | 0.29 | ||
No contribution | 0.58 | 0.40–0.86 | 1.21 | 0.83–1.74 |
Contribution <20% | 0.81 | 0.63–1.04 | 1.10 | 0.85–1.42 |
Contribution >20% | 1.00 | Ref. | 1.00 | Ref. |
ART = antiretroviral therapy, CI = confidence interval, HR = hazard ratio.
All analyses use Cox proportional hazards regression.
Hazard ratios obtained after 1∶3 matching (with replacement) 683 “exposed” to 399 “unexposed” individuals based on propensity of living in a state contributing to the ADAP budget.
Both regression-adjusted and propensity-score matched analyses account for the following variables: age; sex; race/ethnicity; transmission risk; CD4+ count and viral load at eligibility; history of alcohol abuse, substance abuse, and mental disorders; year of eligibility; type of cohort; clinic-specific mechanisms to help obtain ART; state-level population density, % population of black race, % population below poverty line, median household income, and per capita Medicaid spending on HIV.
Virologic suppression one year after ART eligibility among the entire study population was 58%, with those living in states not contributing to the ADAP budget less likely to have a suppressed viral load (51% versus 59%). In adjusted analyses, this association was not statistically significant (conventional Cox regression-adjusted HR 1.02, 95% CI 0.88–1.18; propensity score-matched HR 1.13, 95% CI 0.93–1.36).
Among the overall study population (N = 8,874), ART initiation after six months was higher among those living in a state with an existing ADAP waiting list than those living in a state without a list (73% versus 55%). A similar pattern was observed in this overall population for one-year virologic suppression (71% versus 58%). In regression-adjusted analyses, the hazard ratio based on living in a waiting list state was 1.73 (95% CI 1.45–2.07) for ART initiation and 1.21 (95% CI 1.01–1.44) for virologic suppression (
HR | 95% CI | HR | 95% CI | |
Living in a waiting list state (vs. not living in a waiting list state) | ||||
Crude (N = 8,874) | 1.55 | 1.38–1.73 | 1.39 | 1.24–1.57 |
Living in a waiting list state (vs. not living in a waiting list state) | ||||
Crude | 1.59 | 1.19–2.11 | 1.49 | 1.10–2.03 |
ART = antiretroviral therapy, CI = confidence interval, HR = hazard ratio.
All analyses use Cox proportional hazards regression.
Hazard ratios obtained after 1∶3 matching (with replacement) 398 “exposed” to 222 “unexposed” individuals based on propensity of living in a waiting list state.
Both regression-adjusted and propensity-score matched analyses account for the following variables: age; sex; race/ethnicity; transmission risk; CD4+ count and viral load at eligibility; history of alcohol abuse, substance abuse, and mental disorders; year of eligibility; type of cohort; clinic-specific mechanisms to help obtain ART; state-level population density, % population of black race, % population below poverty line, median household income, and per capita Medicaid spending on HIV.
We performed a sensitivity analysis to examine whether the non-significant association was maintained when shortening the time to ART initiation to 3 months after eligibility instead of 6 months. Here, the HR was 1.44 (95% CI 1.06–1.97) (
In this study of HIV-infected individuals in the United States who were newly clinically eligible to begin ART, we found that not having an additional state contribution to an ADAP's annual budget was associated with delayed ART initiation. This finding was robust to the type of statistical procedure used to account for known confounders, and furthermore was maintained when considering different assumptions, and when focusing on specific subpopulations, including those with a history of IDU.
Our findings are consistent with an ecologic analysis that suggested greater HIV inequities in some U.S. states as a result of lower state ADAP contributions
We found that living in a state with an active ADAP waiting list was not associated with less timely ART initiation, and in fact, in some scenarios associated with more timely ART initiation. On the surface, this may seem paradoxical; we expected that living in a state with an ADAP waiting list would be associated with less timely ART initiation. However, this finding may reflect efforts at study sites contributing data to NA-ACCORD to get patients promptly treated when there is knowledge of existing structural barriers. For example, the more timely initiation related to waiting lists that we observed among IDU could reflect special efforts by sites to engage this high-need group into care, since it is known that IDU have lower levels of engagement in HIV care compared with other risk groups
We used propensity score matching methods to create comparable groups of “exposed” and “unexposed” individuals, capitalizing on the heterogeneity of policies across different states in the NA-ACCORD. This technique measures the “average treatment effect in the treated” population, which is different from the “average treatment effect” in the entire study population that conventional regression analyses assess. We can interpret our propensity-score matched estimates regarding state ADAP features as applicable to the subset of individuals with the same risk factor distribution as those living in those states with those features (i.e., no state contribution to the ADAP budget; presence of ADAP waiting lists)
We did not find significant associations between less generous ADAP features and less timely virologic suppression. One possibility for this is that the majority of HIV-infected individuals in our population were eventually treated (the percentage increasing to 65% overall after one year of eligibility), and once they began treatment, differences in the state ADAP features we examined may have played less of a role. In other words, the majority of people reached guideline-defined treatment goals, despite the delay in starting therapy that more limited state budgets may influence. This is encouraging, even though the additional efforts expended to procure treatment in light of these delays have costs.
Furthermore, we did not report on longer-term outcomes like sustained viral load suppression and mortality. Because our study is essentially an intent-to-treat analysis, we did not take into account changes in ADAP features over the course of an individuals' treatment trajectory. An analysis of time-updated ADAP changes could help to understand these processes better, especially considering the variability in coverage by some state ADAPs of medications for other health conditions relevant to HIV-infected individuals like hepatitis infection, cardiovascular disease, and mental health conditions
We originally hypothesized that our effect estimates would be greater among IDU owing to their increased needs with respect to care engagement and treatment initiation. While our data provide some evidence of this, the overall effects are not dramatically different from those overall, suggesting that on the whole, state-level differences in the ADAP features we examined may affect their target populations similarly with respect to ART initiation. However, it is possible that other differences in state ADAP formularies, such as coverage of hepatitis treatment or opioid dependency
Recent observational studies have taken other approaches to understand the influence of ADAP features, directly examining the benefits of ADAP enrollment itself on treatment utilization
Another limitation is that our exposures of interest were based on the results of annual surveys of state ADAP offices conducted by NASTAD over the study period and therefore are dependent on the quality of these findings. However, these results are publicly available and therefore allow for transparency should similar assessments be conducted by other investigators. Unmeasured confounding may have also affected our effect estimates. Both propensity score matching and conventional regression techniques are designed to account for observed confounders, but there may be other characteristics of patients, clinics, or the states themselves that we have not accounted for in our analysis. For example, we did not account for the diffusion of each state's ADAP program among its HIV-infected population, or more nuanced differences in state Medicaid eligibility or generosity beyond per capita HIV spending, which if important could lead to some bias in our conclusions. In sensitivity analyses, we controlled for state fixed effects to try to account for all of the unobserved characteristics of a particular state, but by doing so this technique may have over-adjusted for these effects, which may have been highly correlated with the exposure of interest.
Finally, the period of eligibility for this analysis ended in 2009, when at least two major changes occurred in the HIV epidemic in the United States: the adoption of clinical guidelines recommending starting treatment at a CD4+ count of 500 cells/uL or even higher
In conclusion, our study found an association between living in a state that does not provide an additional contribution to ADAP funding and delays in ART initiation. The importance of timely ART initiation when clinically indicated is well-established
Appendix. Table S1. Mechanisms at individual clinics in NA-ACCORD to assist patients in accessing prescription drugs in 2008 (N = 22). Table S2. Sensitivity analyses for association between state ADAP features at time of eligibility and outcomes.
(DOCX)
AIDS Link to the IntraVenous Experience: Gregory D. Kirk.
Adult AIDS Clinical Trials Group Longitudinal Linked Randomized Trials: Constance A. Benson, Ronald J. Bosch, and Ann C. Collier.
Fenway Health HIV Cohort: Stephen Boswell, Chris Grasso, and Ken Mayer.
HAART Observational Medical Evaluation and Research: Robert S. Hogg, Richard Harrigan, Julio Montaner, and Angela Cescon.
HIV Outpatient Study: John T. Brooks and Kate Buchacz.
HIV Research Network: Kelly A. Gebo.
Johns Hopkins HIV Clinical Cohort: Richard D. Moore.
John T. Carey Special Immunology Unit Patient Care and Research Database, Case Western Reserve University: Benigno Rodriguez.
Kaiser Permanente Mid-Atlantic States: Michael A. Horberg.
Kaiser Permanente Northern California: Michael J. Silverberg.
Longitudinal Study of Ocular Complications of AIDS: Jennifer E. Thorne.
Multicenter Hemophilia Cohort Study–II: James J. Goedert.
Multicenter AIDS Cohort Study: Lisa P. Jacobson.
Montreal Chest Institute Immunodeficiency Service Cohort: Marina B. Klein.
Ontario HIV Treatment Network Cohort Study: Sean B. Rourke, Ann Burchell, and Anita R. Rachlis.
Retrovirus Research Center, Puerto Rico: Robert F. Hunter-Mellado and Angel M. Mayor.
Southern Alberta Clinic Cohort: M. John Gill.
Studies of the Consequences of the Protease Inhibitor Era: Steven G. Deeks and Jeffrey N. Martin.
University of Alabama at Birmingham 1917 Clinic Cohort: Michael S. Saag, Michael J. Mugavero, and James Willig.
University of North Carolina, Chapel Hill, HIV Clinic Cohort: Joseph J. Eron and Sonia Napravnik.
University of Washington HIV Cohort: Mari M. Kitahata and Heidi M. Crane.
Veterans Aging Cohort Study: Amy C. Justice, Robert Dubrow, and David Fiellin.
Vanderbilt-Meharry Centers for AIDS Research Cohort: Timothy R. Sterling, David Haas, Sally Bebawy, and Megan Turner.
Women's Interagency HIV Study: Stephen J. Gange and Kathryn Anastos.
NA-ACCORD Executive Committee: Richard D. Moore, Michael S. Saag, Stephen J. Gange, Keri N. Althoff, Mari M. Kitahata, Rosemary G. McKaig, Amy C. Justice, and Aimee M. Freeman.
NA-ACCORD Administrative Core: Richard D. Moore, Aimee M. Freeman, Carol Lent and Aaron Platt.
NA-ACCORD Data Management Core: Mari M. Kitahata, Stephen E. Van Rompaey, Heidi M. Crane, Eric Webster, Liz Morton, and Brenda Simon.
NA-ACCORD Epidemiology and Biostatistics Core: Stephen J. Gange, Keri N. Althoff, Alison G. Abraham, Bryan Lau, Jinbing Zhang, Jerry Jing, Elizabeth Golub, Shari Modur, David B. Hanna, Peter Rebeiro, Cherise Wong and Adell Mendes.
The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention (CDC) or the National Institutes of Health (NIH).