Abstract
Large-scale, multisite data sets offer the potential for exploring the public health benefits of biomedical interventions. Data harmonization is an emerging strategy to increase the comparability of research data collected across independent studies, enabling research questions to be addressed beyond the capacity of any individual study.
The National Institute on Drug Abuse recently implemented this novel strategy to prospectively collect and harmonize data across 22 independent research studies developing and empirically testing interventions to effectively deliver an HIV continuum of care to diverse drug-abusing populations.
We describe this data collection and harmonization effort, collectively known as the Seek, Test, Treat, and Retain Data Collection and Harmonization Initiative, which can serve as a model applicable to other research endeavors.
Determining how improved HIV care among drug users can affect virological health, including implications for HIV transmission and incidence, is an urgent public health research priority. Addressing questions about the broad impact of medical, behavioral, and psychosocial interventions is typically beyond the scope of a single research project and requires data from multiple studies pooled into a unified data set. These integrated data sets provide the opportunity to leverage scarce resources to efficiently address critical research questions with the potential to increase statistical power and determine intervention effects across a broad patient population, examine differential effectiveness by clinical site and patient characteristics, and increase external validity of research findings by enabling cross-site replications and identification of subpopulation effects. Significant methodological challenges must be overcome when one is pooling data from independent studies, and strategies are needed to simultaneously develop analytic methods for integrated data sets and improved measurement to reduce study-specific variation in the definition, operationalization, and creation of health and social science data.
The purpose of this article is to describe a prospective data collection and harmonization initiative undertaken by the National Institute on Drug Abuse (NIDA). We conceptualized data collection and harmonization as a broad set of investigator and funding agency activities in which research questions, hypotheses, critical data domains, measures, and specific items necessary to address these questions are determined a priori, rather than making these determinations after study completion. This approach maximizes the likelihood that comparable data are available to address broader research questions and enlists the participation of investigators before studies move into the field.
THE SEEK, TEST, TREAT, AND RETAIN INITIATIVE
HIV-infected drug users have reduced access to antiretroviral therapy (ART), initiate therapy at advanced stages of HIV infection, and are more likely to experience problematic adherence compared with those individuals who do not use drugs.1 The high prevalence of HIV among drug users, combined with low worldwide coverage of HIV prevention, treatment, and care services for this population, presents a substantial global health challenge.2 Determining HIV status, immediately initiating ART for HIV seropositives, and reducing viral load to nondetectable levels can simultaneously improve the health of the individual, reduce transmissibility to others, and, on an aggregate level, improve population health.3–7
The success of this strategy depends on reaching out to high-risk, hard-to-reach groups who have not been recently tested (Seek), engaging them in HIV testing (Test), initiating HIV-infected individuals on ART (Treat), and keeping them in long-term HIV care (Retain). Gardner et al. has estimated that fewer than 19% of those infected with HIV in the United States population at large achieve viral suppression,8 and implementing this Seek, Test, Treat, and Retain (STTR) continuum of HIV care for drug users is especially challenging. In collaboration with other National Institutes of Health partners, NIDA funded 22 studies (Table 1) to develop and test interventions enhancing the STTR treatment cascade for criminal justice–involved (n = 12) and vulnerable drug abusers (n = 10). The STTR Data Collection and Harmonization Initiative was originated by NIDA staff and seeks to facilitate the creation of a cross-study integrated data set with the potential to address overarching research questions related to the public health impact of improving HIV care among drug users. This effort is one of the largest known examples of prospective data collection and harmonization efforts to date.
TABLE 1—
Seek, Test, Treat, and Retain Research Studies for Criminal Justice and Vulnerable Populations
| Principal Investigators (Grant Institution) | Project Title | Study Location |
| Criminal justice populations | ||
| Frederick Altice (Yale University) | HIV, Buprenorphine, and the Criminal Justice System | Washington, DC |
| Curt Beckwith, MD, Irene Kuo, PhD, MPH, Ann Kurth, RN, PhD, MPH (The Miriam Hospital, George Washington University, New York University) | CARE Corrections: Technology for Jail HIV/HCV Testing, Linkage, and Care | Rhode Island and Washington, DC |
| William Cunningham, MD, MPH (University of California, Los Angeles) | Effectiveness of Peer Navigation to Link Released HIV+ Jail Inmates to HIV Care | California |
| Michael Gordon, DPA, Josiah Rich, MD, MPH (Friends Research Institute, The Miriam Hospital) | A Randomized Controlled Trial and Cohort Study of HIV Testing and Linkage to Care | Maryland and Rhode Island |
| Alexander Kral, PhD (Research Triangle Institute) | Finding, Testing, and Treating High-Risk Probationers and Parolees With HIV | California |
| Lawrence Ouellet, PhD, Michael Puisis, DO, Jeremy David Young, MD, MPH (University of Illinois, Chicago) | Seek, Test, Treat: An Integrated Jail–Prison–Community Model for Illinois | Illinois |
| Josiah Rich, MD, MPH, Liza Solomon, DrPH (The Miriam Hospital, Abt Associates Inc.) | Improving Linkage to HIV Care Following Release from Incarceration | Florida, Rhode Island, Texas, North Carolina, Puerto Rico |
| Stanley Sacks, PhD (National Development and Research Institutes Inc) | START Together: HIV Testing and Treatment in and After Jail | New York |
| David Seal, PhD (Medical School of Wisconsin) | Seek, Test, and Treat Strategies | Wisconsin |
| Sandra Springer, MD, Frederick Altice, MD (Yale University) | Naltrexone for Opioid Dependent Released HIV+ Criminal Justice Populations | Massachusetts and Connecticut |
| Vu Quan, MD (Johns Hopkins University) | Seek, Test, Treat Strategies for Vietnamese Drug Users: A Randomized Controlled Trial | Vietnam |
| David Wohl, MD, Carol Golin, MD, Patrick Flynn, PhD, Kevin Knight, PhD (University of North Carolina, Texas Christian University) | Randomized Controlled Trial of an Augmented Test, Treat, Link, and Retain Model for NC and Texas Prisoners | North Carolina and Texas |
| Vulnerable populations | ||
| Chinazo Cunningham, MD (Albert Einstein College of Medicine) | Abstinence Reinforcing Contingency Management to Suppress HIV Viral Load | New York |
| Ann Duerr, MD, PhD, MPH (Fred Hutchinson Cancer Research Center) | HIV Testing and Treatment to Prevent Onward HIV Transmission Among High-Risk MSM | Peru |
| Wafaa El-Sadr, MD, MPH, Sharon Mannheimer, MD (Columbia University) | STAR—Seek, Test, and Retain. Linkages for Black HIV+, Substance Using MSM | New York |
| Marya Gwadz, PhD (New York University) | Peer-Driven Intervention to Seek, Test, and Treat Heterosexuals at High Risk for HIV | New York |
| Jacqueline Tulsky, MD (University of California, San Francisco) | Seek, Test, Treat, and Retain Strategies Leveraging Mobile Health Technologies | California |
| Ann Kurth, RN, PhD, MPH, Peter Cherutich, MBChB, MPH (New York University and the National AIDS & STI Control Program) | Test and Linkage to Care (TLC_IDU) | Kenya |
| Greg Lucas, MD, PhD, Shruti Meta, PhD, MPH (Johns Hopkins University) | Integrated Care Clinics for IDUs in India: A Cluster Randomized Trial | India |
| Lisa Metsch, PhD, Carlos Del Rio, MD (University of Miami, Emory University) | Project Retain: Providing Integrated Care for HIV-Infected Crack Cocaine Users | Florida and Georgia |
| Jeffrey Samet, MD, MPH (Boston Medical Center) | Linking Russian Narcology and HIV Care to Enhance Treatment, Retention, and Outcomes | Russia and Ukraine |
| Wendee Wechsberg, PhD (Research Triangle Institute) | Combination Prevention for Vulnerable Women in South Africa | South Africa |
Note. IDU = intravenous drug user; MSM = men who have sex with men.
A set of activities, potentially applicable to other research endeavors, has facilitated the STTR Data Collection and Harmonization Initiative. Participation in data collection and harmonization activities, including annual face-to-face meetings and contributing to the integrated data set, was stipulated within the terms and conditions of funding. NIDA staff and study investigators worked collaboratively to define the scope of the data collection and harmonization initiative, share study protocols and data collection information forms, develop overarching research questions and hypotheses, and determine data domains necessary to address these questions.
This process was carried out by 2 parallel groups—one studying criminal justice populations and the other vulnerable drug-using populations. Eventually, investigators selected 10 domains as primary areas for data collection across the studies focused on criminal justice populations and 11 domains for studies focused on vulnerable populations (e.g., drug use, viral load; Figure 1). Investigators identified optional or secondary data domains to facilitate additional collaboration across subsets of projects. Funding constraints and study designs made it impractical for these domains to be captured by every study. For example, 8 studies included the collection of economic data. Investigators are collaborating to harmonize these data with the potential of examining the cost effectiveness of improving the STTR treatment cascade for drug abusers across a number of specific interventions.
FIGURE 1—
Core measurement domains for criminal justice and vulnerable population studies.
Note. STI = sexually transmitted infection; STTR = Seek, Test, Treat, and Retain. Central core measures are used in every study. Optional core measures (e.g., HIV stigma) may be used based on specific interests of a subset of studies.
Workgroups comprising representatives from each research study and NIDA conducted the second phase of data collection and harmonization activities. They generated a comprehensive list of measures used to capture data within each primary domain. Workgroups reviewed this list and proposed a set of measures and specific questions to collect data within their domain. Principal investigators, facilitated by NIDA, discussed the merits of workgroup recommendations, eventually reaching consensus on a list of measures, questions, and follow-up time frames for data collection across all studies.
In our experience, reaching agreement on measures, the wording of specific items within the measures, and determining data collection time points can be challenging. However, the key feature of a data collection and harmonization process is to simultaneously provide a set of common core measures while also allowing flexibility regarding data collection based on the unique characteristics of the individual research studies. This coupling of structure and flexibility contributed to the success of our efforts. Investigators agreed to use core measures to collect data within study-relevant domains, but were not expected to expand the scope of their work just to include an additional measure. For example, a study limited to identifying drug-using individuals and providing HIV testing was not expected to collect virological and immunological status data.
Completing a successful data collection and harmonization initiative requires ongoing coordination and monitoring. Even when investigators agree on a common data collection process, deviations often occur. These deviations must be recorded and examined to determine whether data are commensurate across studies. To assist in this process, NIDA funded 2 different groups to serve as a data coordination center. The National Addiction and HIV Data Archive Program at the University of Michigan’s Interuniversity Consortium for Political and Social Research conducted initial data coordination activities. This program’s staff assisted with a variety of activities, including providing common language for patient consent forms allowing de-identified data to be included in a multistudy data set; monitoring study accrual, use, and fidelity of core measures; developing codebooks and other materials relevant to the integrated data set; establishing a data deposit schedule; and integrating data deposited by investigators. Investigators provided quarterly updates on study accrual, the administration of core measures, referent time frames used for the core measures, and any changes to the measures or administration procedures.
To continue data collection activities and support the pooling and analysis of data for publications, NIDA subsequently funded a permanent data coordinating center at the University of Washington. Strategies under consideration to facilitate the harmonization of pooled data include (1) considering study-specific estimates and statistical testing for heterogeneity between these estimates; (2) hierarchical linear models that treat the studies as random effects; (3) naïve pooling of studies from similar populations, with statistical testing to ensure that there is no effect measure modification between different study population; and (4) harmonization of scales by using item response theory (or other latent variable approaches) coupled with robust cocalibration of differences in study instruments. In some cases, we may consider meta-analytic approaches, principally in the case of serious effect heterogeneity (to make it easier to explicitly show study differences) or to handle certain patterns of omitted covariates between studies, but this is not considered to be a primary analytic approach in the STTR consortium.
DATA HARMONIZATION PROJECTIONS AND CASE STUDIES
Table 2 presents the number of sites measuring the core domain, the number of sites using the recommended core domain measure, and the number of sites that adapted the recommended core domain measure. Overall, use of the harmonized measures is high. Among the projects focused on criminal justice populations, a minimum of 9 (of 11 studies conducting primary data collection) are measuring core domains, with 45% to 100% of the studies using the recommended core measure (with variation by domain). Adaptation of the core measure ranges from 67% to 100% of studies.
TABLE 2—
Core Domains and Use of Core Domain Measures Across Seek, Test, Treat, and Retain (STTR) Criminal Justice and Vulnerable Populations Groups
| Recommended Measure of Core Domain | Sites Measuring Core Domain, No. (%) | Sites Using Recommended Core Domain Measure, No. (%) | Sites Adapting Core Domain Measure,a No. (%) |
| Criminal justice groupsb (n = 11) | |||
| Demographic questionnaire | 11 (100) | 11 (100) | 11 (100) |
| Drug and alcohol use: AUDIT9 or TCU-II10 | 11 (100) | 10 (91) | 10 (100) |
| Mental health: MINI11 or CES-D12 | 10 (91) | 5 (45) | 5 (100) |
| HIV treatment adherence: ACTG13 | 9 (82) | 7 (64) | 6 (86) |
| HIV risk behavior: Risk Behavior Assessmentc,14 | 10 (91) | 9 (82) | 8 (89) |
| Access to care: Access to Care Scale; Social Support Subscale; SF-36/RAND-3615 | 9 (82) | 9 (82) | 6 (67) |
| Criminal justice status: Criminal Justice Risk Screener and Legal Status16 or National Crime Information Center Codes17 | 9 (82) | 7 (64) | 6 (86) |
| HIV/HCV/STI testing (status and practices): HIV/HCV/STI Testing Status Questionnaire (self-report); Organizational Testing Practices Questionnaire | 10 (91) | 8 (73) | 7 (88) |
| Service use: service use measuresd,18–20 | 10 (91) | 7 (64) | 7 (100) |
| Virological or immunological status: viral load assay (viral load) or flow cytometry (CD4) | 9 (82) | 9 (82) | NA |
| Vulnerable populations groups (n = 10) | |||
| Demographic questionnaire | 10 (100) | 10 (100) | 10 (100) |
| Drug and alcohol use: AUDIT9 or TCU-II10 | 10 (100) | 10 (100) | 9 (90) |
| Mental health: CES-D12 | 8 (80) | 7 (70) | 2 (29) |
| HIV treatment adherence: VAS21,22 | 9 (90) | 8 (80) | 2 (25) |
| HIV risk behavior: Women’s Health Coop Baseline Questionnaire14; Risk Behavior Assessmentc,14 |
10 (100) | 10 (100) | 10 (100) |
| Access to care: Access to Care Scale; Social Support Subscale; SF-36/RAND-3615 | 9 (90) | 9 (90) | 8 (89) |
| Health literacy: Health Literacy Screener23 | 8 (80) | 8 (80) | 0 (0) |
| Barriers to care: Kalichman Barriers to Care Scale24 | 10 (100) | 10 (100) | 6 (60) |
| HIV/HCV/STI testing (status and practices): HIV/HCV/STI Testing Status Questionnaire (self-report); Organizational Testing Practices Questionnaire | 9 (90) | 7 (70) | 7 (100) |
| Service use: service use measuresd,18–20 | 8 (80) | 7 (70) | 7 (100) |
| Virological or immunological status: viral load assay (viral load) or flow cytometry (CD4) | 9 (90) | 9 (90) | NA |
Note. ACTG = The AIDS Clinical Trials Group Adherence Questionnaire; AUDIT = Alcohol Use Disorder Identification Test; CES-D = Center for Epidemiologic Studies Depression Scale; MINI = Mini-International Neuropsychiatric Interview (6th ed); NA = not applicable; SF-36/RAND-36 = RAND Medical Outcomes Study 36-Item Short Form Health Survey; STI = sexually transmitted infection; TCU-II = Texas Christian University Drug Screen II; VAS = Visual Analog Scale.
This column represents the frequency and percentage of sites that adapted the core domain measures among those that used the core domain measure.
Although there are 12 STTR criminal justice groups, only 11 are collecting primary data so all analyses in this table are based on n = 11 studies in the denominator.
Adaptation by National Institute on Drug Abuse/Yale AIDS Program (Project CONNECT/TRUST).
Adaptation from the HIV/AIDS Treatment Adherence Outcomes and Cost Study. The demographics, HIV/HCV/STI testing status (self-report), and Organizational Testing Practices Questionnaires were developed by National Institute on Drug Abuse staff and several STTR principal investigators.
Ten studies focused on vulnerable drug-abusing populations are conducting primary data collection. Among these studies, a minimum of 8 studies are collecting data within core measurement domains. A range of 70% to 100% of the studies are using the recommended core measures, with variation by domain. Adaptation of measures varies by domain and ranges from 0% to 100% (Table 2).9–24 These modifications include changes to individual items within measures, changes to response categories, and asking time-specific questions using different referent time points (e.g., asking about substance use over past month vs the past 6 months). Study investigators, coordinating center staff, and NIDA are developing methodological approaches to ensure data comparability and deal with fluctuations in collection procedures. These methodological approaches will be the focus of future publications.
Based on study accrual and data received to date, we project the STTR harmonized data set will include 52 525 individuals, providing a rich data set. The anticipated characteristics of this group include 39 132 men, 13 211 women, 12 466 involved in the criminal justice system, and 11 418 HIV-infected individuals (Table 3). In addition, this data set will contain data for numerous biological endpoints including HIV testing results, viral load, CD4 count, and urine drug testing.
TABLE 3—
Planned Total Enrollment and Predicted Demographic and Biological Data Across the Seek, Test, Treat, and Retain Projects
| Variable | Criminal Justice Population (n = 12 466), No. (%) | Vulnerable Populations (n = 40 059), No. (%) |
| Gender | ||
| Female | 2 346 (18.8) | 10 865 (27.1) |
| Male | 10 045 (80.6) | 29 087 (72.6) |
| HIV status | ||
| Infected | 3 015 (24.2) | 8 403 (21) |
| Not infected | 9 803 (78.6) | 30 305 (75.7) |
| Viral load | 2 102 (16.9) | 30 267 (75.6) |
| CD4 | 1 852 (14.9) | 30 107 (75.2) |
| HIV testing | 10 953 (87.9) | 38 676 (96.6) |
| Drug testing | 302 (2.4) | 759 (1.9) |
Note. Planned = aggregate of the total sample size projected across all of the grants; predicted = aggregate of all of the estimated projected demographic and biologic data across grants. The total includes 182 individuals (75 from the criminal justice populations and 107 from the vulnerable populations) who did not identify gender or transgender individuals. The sum of the HIV-infected and non–HIV-infected samples are not the same as the total. In a few cases, principal investigators were not able to estimate the number of HIV-infected and non–HIV-infected persons in their samples.
Here we present 2 examples to illustrate how the collected harmonized data will be used to explore the potential impact on a range of outcomes associated with the HIV treatment cascade. We plan to make these case studies the focus of subsequent publications.
The first case study will seek to quantify the aggregate impact of interventions to improve linkage to HIV care and ART adherence on viral load across 17 studies of HIV-positive drug users. Data will include viral load, ART regimen, and ART adherence (from the Visual Analog Scale or the AIDS Clinical Trials Group Adherence Scale13). Because of the potential demographic, biomedical, social, and structural differences between the criminal justice and vulnerable populations, 2 separate data analytic plans will explore population-specific differences. Data will come from 10 studies and include 3886 participants for the STTR criminal justice group and from 7 studies with 8466 participants for the STTR vulnerable populations group. We will use the structural equation modeling approach of Imai et al.25 to estimate the association between aggregate intervention effect and viral load, which will also allow for the assessment of the possible mediating role of ART medication adherence for this association. Collecting and harmonizing data across these 17 studies will allow us to address the effects of linkage and adherence interventions on viral load across different subpopulations and to identify important mediators and moderators (e.g., behavioral or psychosocial) that may be linked to differential viral load outcomes.
The second case study will explore the minimum or threshold length of time during which HIV- infected drug abusers need to be maintained on ART to show reductions in viral load. Data will come from 18 of the 22 STTR studies with a total of 12 552 participants across multiple time points. This analysis will first estimate the mean time to virological suppression or a 10-fold reduction in viral load (a minimally clinically relevant change) by using a Cox proportion hazards model and a log-transformed viral load measure. We will test for proportionality in hazards over time by using graphical techniques and Schoenfeld residuals. Therefore, we will be able to determine if there is a threshold effect in time to viral load or virological suppression (after which the odds of achieving virological suppression become exceedingly small). We will use inverse probability of censoring weights in each follow-up time period to account for nonrandom loss to follow-up in this population. These results will be compared with drug-using participants in a nationally distributed clinical cohort of more than 29 000 patients with HIV26 so comparisons can be made between associations in these special populations and general clinical care.
SUMMARY
The STTR Data Collection and Harmonization Initiative may serve as a model for future data collection and harmonization endeavors. Our ongoing work demonstrates the potential feasibility of such an approach and outlines steps that can be followed by others interested in undertaking a data collection and harmonization process. The Phenotypes and eXposures Toolkit, a collection of high-priority measures developed by the National Institutes of Health,27 represents a strong commitment to such initiatives and takes an important step toward providing standard measures related to complex diseases including addiction, phenotypic traits, and environmental exposures. A set of instruments related to drug use, abuse, and addiction form a core set of measures for use in drug-abuse research. The development and use of these common measures provide a platform for data collection and harmonization initiatives related to drug use and addiction.
Our experience suggests that prospective data collection and harmonization is a complex undertaking that may only be possible under certain conditions. Our efforts benefitted from the following conditions: a set of research studies with similar research questions, goals, and timelines; investigators willing to participate in data collection and harmonization; individuals who accept the role of coordinating the data collection and harmonization process and its required activities; and resources to support carrying out these activities, creating and analyzing the resulting integrated data sets, and writing for publication. Although highly motivated investigators could launch a data collection and harmonization effort on their own, involvement and support by the funding agency is likely essential to success. Funding agencies may be best suited to initiate and coordinate data harmonization initiatives because of their comprehensive knowledge of research in a particular area, potential to leverage additional resources, ability to encourage collaboration among researchers, and unique perspectives on goals that extend beyond individual research projects.
We also have discovered that embedding a data collection and harmonization process into a set of clinical research studies from the outset has benefits beyond simply addressing methodological challenges associated with integrated data analysis. Our approach has provided a diverse set of investigators the opportunity to conduct their own independent research projects while simultaneously collaborating on the creation of a large integrated data set with the potential to address additional research questions. Bringing people together at the beginning of their studies provides researchers with a rare in-depth examination of other related research studies and the opportunity for additional collaboration. This collaboration can be beneficial to logistical aspects of conducting research, such as leveraging technology needs across several studies to improve pricing and bargaining power for cell phones and wireless coverage plans, or developing common data sharing plans and consent forms. Or it may directly improve the science, for example, by precipitating a comparison of approaches for enrolling hard-to-reach populations in research studies, fostering discussion among study investigators to jointly solve methodological challenges, and developing data analysis plans for a subset of studies in topics such as cost and cost effectiveness.
We expect the STTR Data Collection and Harmonization Initiative to yield data with important scientific and public health implications, specifically the impact that enhancing the STTR treatment cascade may have on HIV-related infectivity, morbidity, and mortality. Increased pressure on research budgets and enhanced capacity to collect, store, and analyze growing data sets create an environment in which the use of data collection and harmonization initiatives may expand. We would encourage researchers and funding agencies to consider the data collection and harmonization process as a useful and potentially transformative model to address large, complex research questions, while maximizing the gain on scientific investment.
Acknowledgments
The authors would like to thank the research teams associated with the Seek, Test, Treat, and Retain Data Collection and Harmonization Initiative whose collaboration has made this project possible. We would also like to acknowledge Amy Pienta, Kaye Marz, and Matthew Wrase from the Interuniversity Consortium for Political and Social Research at University of Michigan for facilitating the initial implementation of the harmonization initiative, and Heidi Crane and Chris Delaney from the University of Washington for editorial comments.
Human Participant Protection
The subject matter of this article did not involve interaction or the collection of data from human participants; thus, human participant protection is not applicable.
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