Multilevel Resilience and HIV Virologic Suppression Among African American/Black Adults in the Southeastern United States.

OBJECTIVE: To assess overall and by neighborhood risk environments whether multilevel resilience resources were associated with HIV virologic suppression among African American/Black adults in the Southeastern United States. SETTING AND METHODS: This clinical cohort sub-study included 436 African American/Black participants enrolled in two parent HIV clinical cohorts. Resilience was assessed using the Multilevel Resilience Resource Measure (MRM) for African American/Black adults living with HIV, where endorsement of a MRM statement indicated agreement that a resilience resource helped a participant continue HIV care despite challenges or was present in a participant's neighborhood. Modified Poisson regression models estimated adjusted prevalence ratios (aPRs) for virologic suppression as a function of categorical MRM scores, controlling for demographic, clinical, and behavioral characteristics at or prior to sub-study enrollment. We assessed for effect measure modification (EMM) by neighborhood risk environments. RESULTS: Compared to participants with lesser endorsement of multilevel resilience resources, aPRs for virologic suppression among those with greater or moderate endorsement were 1.03 (95% confidence interval: 0.96-1.11) and 1.03 (0.96-1.11), respectively. Regarding multilevel resilience resource endorsement, there was no strong evidence for EMM by levels of neighborhood risk environments. CONCLUSIONS: Modest positive associations between higher multilevel resilience resource endorsement and virologic suppression were at times most compatible with the data. However, null findings were also compatible. There was no strong evidence for EMM concerning multilevel resilience resource endorsement, which could have been due to random error. Prospective studies assessing EMM by levels of the neighborhood risk environment with larger sample sizes are needed.

Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.

Cross-sectional analysis of factors associated with medication adherence in western Kenya.

OBJECTIVES: Poor medication adherence in low-income and middle-income countries is a major cause of suboptimal hypertension and diabetes control. We aimed to identify key factors associated with medication adherence in western Kenya, with a focus on cost-related and economic wealth factors. SETTING: We conducted a cross-sectional analysis of baseline data of participants enrolled in the Bridging Income Generation with Group Integrated Care study in western Kenya. PARTICIPANTS: All participants were ≥35 years old with either diabetes or hypertension who had been prescribed medications in the past 3 months. PRIMARY AND SECONDARY OUTCOME MEASURES: Baseline data included sociodemographic characteristics, wealth and economic status and medication adherence information. Predictors of medication adherence were separated into the five WHO dimensions of medication adherence: condition-related factors (comorbidities), patient-related factors (psychological factors, alcohol use), therapy-related factors (number of prescription medications), economic-related factors (monthly income, cost of transportation, monthly cost of medications) and health system-related factors (health insurance, time to travel to the health facility). A multivariable analysis, controlling for age and sex, was conducted to determine drivers of suboptimal medication adherence in each overarching category. RESULTS: The analysis included 1496 participants (73.7% women) with a mean age of 60 years (range 35-97). The majority of participants had hypertension (69.2%), 8.8% had diabetes and 22.1% had both hypertension and diabetes. Suboptimal medication adherence was reported by 71.2% of participants. Economic factors were associated with medication adherence. In multivariable analysis that investigated specific subtypes of costs, transportation costs were found to be associated with worse medication adherence. In contrast, we found no evidence of association between monthly medication costs and medication adherence. CONCLUSION: Suboptimal medication adherence is highly prevalent in Kenya, and primary-associated factors include costs, particularly indirect costs of transportation. Addressing all economic factors associated with medication adherence will be important to improve outcomes for non-communicable diseases. TRIAL REGISTRATION NUMBER: NCT02501746.

Population-Based Estimates and Predictors of Child and Adolescent Linkage to HIV Care or Death in Western Kenya.

BACKGROUND: Population-level estimates of linkage to HIV care among children and adolescents (CAs) can facilitate progress toward 95-95-95 goals. SETTING: This study was conducted in Bunyala, Chulaimbo, and Teso North subcounties, Western Kenya. METHODS: Linkage to care was defined among CAs diagnosed with HIV through Academic Model Providing Access to Healthcare (AMPATH)'s home-based counseling and testing initiative (HBCT) by merging HBCT and AMPATH Medical Record System data. Using follow-up data from Bunyala, we examined factors associated with linkage or death, using weighted multinomial logistic regression to account for selection bias from double-sampled visits. Based on the estimated model, we imputed the trajectory for each person in 3 subcounties until a simulated linkage or death occurred or until the end of 8 years when an individual was simulated to be censored. RESULTS: Of 720 CAs in the analytic sample, 68% were between 0 and 9 years and 59% were female. Probability of linkage among CAs in the combined 3 subcounties was 48%-49% at 2 years and 64%-78% at 8 years while probability of death was 13% at 2 years and 19% at 8 years. Single or double orphanhood predicted linkage (adjusted odds ratio [aOR]: 2.66, 95% confidence interval [CI]: 1.33 to 5.32) and death (aOR: 9.85 [95% CI: 2.21 to 44.01]). Having a mother known to be HIV-positive also predicted linkage (aOR = 1.94, 95% CI: 0.97 to 3.86) and death (aOR: 14.49, 95% CI: 3.32 to 63.19). CONCLUSION: HIV testers/counselors should continue to ensure linkage among orphans and CAs with mothers known to be HIV-positive and also to support other CAs to link to HIV care.

Added Value of Next Generation Sequencing in Characterizing the Evolution of HIV-1 Drug Resistance in Kenyan Youth.

Drug resistance remains a global challenge in children and adolescents living with HIV (CALWH). Characterizing resistance evolution, specifically using next generation sequencing (NGS) can potentially inform care, but remains understudied, particularly in antiretroviral therapy (ART)-experienced CALWH in resource-limited settings. We conducted reverse-transcriptase NGS and investigated short-and long-term resistance evolution and its predicted impact in a well-characterized cohort of Kenyan CALWH failing 1st-line ART and followed for up to ~8 years. Drug resistance mutation (DRM) evolution types were determined by NGS frequency changes over time, defined as evolving (up-trending and crossing the 20% NGS threshold), reverting (down-trending and crossing the 20% threshold) or other. Exploratory analyses assessed potential impacts of minority resistance variants on evolution. Evolution was detected in 93% of 42 participants, including 91% of 22 with short-term follow-up, 100% of 7 with long-term follow-up without regimen change, and 95% of 19 with long-term follow-up with regimen change. Evolving DRMs were identified in 60% and minority resistance variants evolved in 17%, with exploratory analysis suggesting greater rate of evolution of minority resistance variants under drug selection pressure and higher predicted drug resistance scores in the presence of minority DRMs. Despite high-level pre-existing resistance, NGS-based longitudinal follow-up of this small but unique cohort of Kenyan CALWH demonstrated continued DRM evolution, at times including low-level DRMs detected only by NGS, with predicted impact on care. NGS can inform better understanding of DRM evolution and dynamics and possibly improve care. The clinical significance of these findings should be further evaluated.

Effectiveness of Monoclonal Antibody Therapy for Preventing COVID-19 Hospitalization and Mortality in a Statewide Population.

BACKGROUND: Monoclonal antibody (MAB) treatments for COVID-19 received Emergency Use Authorization in the United States. METHODS: We used surveillance data from Rhode Island to conduct a retrospective, statewide cohort study to estimate the effectiveness of MABs for preventing hospitalization and death during periods when Alpha and Delta variants were predominant. RESULTS: From 1/17/2021-10/26/2021, 285 long-term congregate care (LTCC) residents and 3,113 non-congregate patients met our eligibility criteria and received MAB; they were matched to 285 and 6,226 controls, respectively. Among LTCC residents, 8.8% (25/285) of patients who received MAB were hospitalized or died compared to 25.3% (72/285) of those who did not receive MAB (adjusted difference=16.7%, 95% confidence interval CI=11.0-22.3%). Among non-congregate patients, 4.5% (140/3,113) of patients who received MAB were hospitalized or died compared to 11.8% (737/6,226) of those who did not receive MAB (adjusted difference=7.2%, 95% CI=6.0-8.4%). CONCLUSIONS: Administration of MABs led to an absolute reduction in hospitalization or death during periods when Alpha and Delta variants were predominant.

An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic.

Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges through data integration, analysis, and reporting. We integrated heterogeneous data sources across systems and developed an open-source, automatic bioinformatics pipeline that provides molecular HIV cluster data to inform public health responses to new statewide HIV-1 diagnoses, overcoming data management, computational, and analytical challenges. We demonstrate implementation of this pipeline in a statewide HIV epidemic and use it to compare the impact of specific phylogenetic and distance-only methods and datasets on molecular HIV cluster analyses. The pipeline was applied to 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island, USA, that provide statewide molecular HIV data to support routine public health case management by a multi-disciplinary team. The resulting cluster analyses and near-real-time reporting guided public health actions in 37 phylogenetically clustered cases out of 57 new HIV-1 diagnoses. Of the 37, only 21 (57%) clustered by distance-only methods. Through a unique academic-public health partnership, an automated open-source pipeline was developed and applied to prospective, routine analysis of statewide molecular HIV data in near-real-time. This collaboration informed public health actions to optimize disruption of HIV transmission.

Validity of Self-Report for Ascertaining HIV Status Among Circular Migrants and Permanent Residents in South Africa: A Cross-Sectional, Population-Based Analysis.

While expanded HIV testing is needed in South Africa, increasing accurate self-report of HIV status is an essential parallel goal in this highly mobile population. If self-report can ascertain true HIV-positive status, persons with HIV (PWH) could be linked to life-saving care without the existing delays required by producing medical records or undergoing confirmatory testing, which are especially burdensome for the country's high prevalence of circular migrants. We used Wave 1 data from The Migration and Health Follow-Up Study, a representative adult cohort, including circular migrants and permanent residents, randomly sampled from the Agincourt Health and Demographic Surveillance System in a rural area of Mpumalanga Province. Within the analytic sample (n = 1,918), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of self-report were calculated with dried blood spot (DBS) HIV test results as the standard. Among in-person participants (n = 2,468), 88.8% consented to DBS-HIV testing. HIV prevalence was 25.3%. Sensitivity of self-report was 43.9% (95% CI: 39.5-48.5), PPV was 93.4% (95% CI: 89.5-96.0); specificity was 99.0% (95% CI: 98.3-99.4) and NPV was 83.9% (95% CI: 82.8-84.9). Self-report of an HIV-positive status was predictive of true status for both migrants and permanent residents in this high-prevalence setting. Persons who self-reported as living with HIV were almost always truly positive, supporting a change to clinical protocol to immediately connect persons who say they are HIV-positive to ART and counselling. However, 56% of PWH did not report as HIV-positive, highlighting the imperative to address barriers to disclosure.

A flexible approach for causal inference with multiple treatments and clustered survival outcomes.

When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring, and unmeasured confounding for causal analyses. Few off-the-shelf causal inference tools are available to simultaneously tackle these issues. We develop a flexible random-intercept accelerated failure time model, in which we use Bayesian additive regression trees to capture arbitrarily complex relationships between censored survival times and pre-treatment covariates and use the random intercepts to capture cluster-specific main effects. We develop an efficient Markov chain Monte Carlo algorithm to draw posterior inferences about the population survival effects of multiple treatments and examine the variability in cluster-level effects. We further propose an interpretable sensitivity analysis approach to evaluate the sensitivity of drawn causal inferences about treatment effect to the potential magnitude of departure from the causal assumption of no unmeasured confounding. Expansive simulations empirically validate and demonstrate good practical operating characteristics of our proposed methods. Applying the proposed methods to a dataset on older high-risk localized prostate cancer patients drawn from the National Cancer Database, we evaluate the comparative effects of three treatment approaches on patient survival, and assess the ramifications of potential unmeasured confounding. The methods developed in this work are readily available in the R $$ \mathsf{R}\kern.15em $$ package riAFTBART $$ \mathsf{riAFTBART} $$ .

Effectiveness Associated With Vaccination After COVID-19 Recovery in Preventing Reinfection.

Importance: The benefit of vaccination for preventing reinfection among individuals who have been previously infected with SARS-CoV-2 is largely unknown. Objective: To obtain population-based estimates of the probability of SARS-CoV-2 reinfection and the effectiveness associated with vaccination after recovery from COVID-19. Design, Setting, and Participants: This cohort study used Rhode Island statewide surveillance data from March 1, 2020, to December 9, 2021, on COVID-19 vaccinations, laboratory-confirmed cases, hospitalizations, and fatalities to conduct a population-based, retrospective study during periods when wild type, Alpha, and Delta strains of SARS-CoV-2 were predominant. Participants included Rhode Island residents aged 12 years and older who were previously diagnosed with COVID-19 and unvaccinated at the time of first infection, stratified into 3 subpopulations: long-term congregate care (LTCC) residents, LTCC employees, and the general population (ie, individuals not associated with congregate settings). Data were analyzed from October 2021 to January 2022. Exposures: Completion of the primary vaccination series, defined as 14 days after the second dose of an mRNA vaccine or 1 dose of vector virus vaccine. Main Outcomes and Measures: The main outcome was SARS-CoV-2 reinfection, defined as a laboratory-confirmed positive result on a polymerase chain reaction (PCR) or antigen test at least 90 days after the first laboratory-confirmed positive result on a PCR or antigen test. Results: Overall, 3124 LTCC residents (median [IQR] age, 81 [71-89]; 1675 [53.6%] females), 2877 LTCC employees (median [IQR] age, 41 [30-53]; 2186 [76.0%] females), and 94 516 members of the general population (median [IQR] age, 35 [24-52] years; 45 030 [47.6%] females) met eligibility criteria. Probability of reinfection at 9 months for those who remained unvaccinated after recovery from prior COVID-19 was 13.0% (95% CI, 12.0%-14.0%) among LTCC residents, 10.0% (95% CI, 8.8%-11.5%) among LTCC employees, and 1.9% (95% CI, 1.8%-2.0%) among the general population. Completion of the primary vaccination series after infection was associated with 49% (95% CI, 27%-65%) protection among LTCC residents, 47% (95% CI, 19%-65%) protection among LTCC employees, and 62% (95% CI, 56%-68%) protection in the general population against reinfection, adjusting for potential sociodemographic and clinical confounders and temporal variation in infection rates. Conclusions and Relevance: These findings suggest that risk of SARS-CoV-2 reinfection after recovery from COVID-19 was relatively high among individuals who remained unvaccinated. Vaccination after recovery from COVID-19 was associated with reducing risk of reinfection by approximately half.

Beyond HIV outbreaks: protocol, rationale and implementation of a prospective study quantifying the benefit of incorporating viral sequence clustering analysis into routine public health interventions.

INTRODUCTION: HIV continues to have great impact on millions of lives. Novel methods are needed to disrupt HIV transmission networks. In the USA, public health departments routinely conduct contact tracing and partner services and interview newly HIV-diagnosed index cases to obtain information on social networks and guide prevention interventions. Sequence clustering methods able to infer HIV networks have been used to investigate and halt outbreaks. Incorporation of such methods into routine, not only outbreak-driven, contact tracing and partner services holds promise for further disruption of HIV transmissions. METHODS AND ANALYSIS: Building on a strong academic-public health collaboration in Rhode Island, we designed and have implemented a state-wide prospective study to evaluate an intervention that incorporates real-time HIV molecular clustering information with routine contact tracing and partner services. We present the rationale and study design of our approach to integrate sequence clustering methods into routine public health interventions as well as related important ethical considerations. This prospective study addresses key questions about the benefit of incorporating a clustering analysis triggered intervention into the routine workflow of public health departments, going beyond outbreak-only circumstances. By developing an intervention triggered by, and incorporating information from, viral sequence clustering analysis, and evaluating it with a novel design that avoids randomisation while allowing for methods comparison, we are confident that this study will inform how viral sequence clustering analysis can be routinely integrated into public health to support the ending of the HIV pandemic in the USA and beyond. ETHICS AND DISSEMINATION: The study was approved by both the Lifespan and Rhode Island Department of Health Human Subjects Research Institutional Review Boards and study results will be published in peer-reviewed journals.

Real-Life Feasibility of HIV Drug Resistance Testing Using Dried Filter Analytes in Kenyan Children and Adolescents Living with HIV.

HIV-1 drug resistance remains a global challenge, yet access to testing is limited, particularly in resource-limited settings. We examined feasibility and limitations of genotyping using dried filter analytes in treatment-experienced Kenyan youth with HIV. Youth infected with HIV perinatally were enrolled in 2016-2018 at the Academic Model Providing Access to Healthcare in Eldoret, western Kenya. Samples were shipped in real-time at ambient temperature to the US, and those with viral load (VL)>1,000 copies/mL were tested based on convenience. Dried blood spots genotyping was attempted when unsuccessful from Hemaspots. Multiple logistic regression was used to examine predictors of genotyping success. Samples from 49 participants (median age 15 years, 43% female, median CD4 496 cells/µL [18%], median 8 years on therapy, median VL 11,827 copies/mL) were shipped after median 7 days from collection, arrived in 20 shipments after median 5 days, and extracted after median 2 days (1 day for samples processed on arrival; and 42 days for frozen Hemaspots). Overall, 29/49 (59%) samples with VL > 1,000 copies/mL and 25/32 (78%) with VL > 5,000 copies/mL were genotyped by either Hemaspots or DBS. Successful genotyping was associated with higher Hemaspot volume and higher VL. Real-life HIV-1 drug resistance testing from dried filter analytes is feasible, even in settings with constrained resources. Findings, particularly relevant where resistance testing is limited for clinical care, raise awareness to implementation practicability of this guidelines-recommended test in care of more individuals and populations. Further optimization of filter analytes is needed to overcome related challenges. IMPORTANCE In this manuscript we use dried filter analytes shipped from Kenya to the US in real time, to demonstrate the real-life feasibility of conducting HIV drug resistance testing in a vulnerable population of young children and adolescents with HIV in a resource limited setting. Such testing, which is recommended in resource-rich settings, is unavailable in most resource limited settings for individual clinical care. We show that real-life HIV drug resistance testing from dried filter analytes is feasible, even in settings with constrained resources. These findings raise awareness to the importance of HIV drug resistance for individual care, even in such settings, and emphasize the implementation practicability of this guidelines-recommended test.

HIV-1 Treatment Failure, Drug Resistance, and Clinical Outcomes in Perinatally Infected Children and Adolescents Failing First-Line Antiretroviral Therapy in Western Kenya.

BACKGROUND: Long-term impact of drug resistance in perinatally infected children and adolescents living with HIV (CALWH) is poorly understood. We determined drug resistance and examined its long-term impact on failure and mortality in Kenyan CALWH failing first-line non-nucleoside reverse transcriptase inhibitor-based antiretroviral therapy (ART). SETTING: Academic Model Providing Access to Healthcare, western Kenya. METHODS: Participants were enrolled in 2010-2013 (timepoint 1) and a subsample re-enrolled after 4-7 years (timepoint 2). Viral load (VL) was performed on timepoint 1 samples, with genotyping of those with detectable VL. Primary endpoints were treatment failure (VL >1000 copies/mL) at and death before timepoint 2. Multinomial regression analysis was used to characterize resistance effect on death, failure, and loss-to-follow-up, adjusting for key variables. RESULTS: The initial cohort (n = 480) was 52% (n = 251) female, median age 8 years, median CD4% 31%, 79% (n = 379) on zidovudine/abacavir + lamivudine + efavirenz/nevirapine for median 2 years. Of these, 31% (n = 149) failed at timepoint 1. Genotypes at timepoint 1, available on n = 128, demonstrated 93% (n = 119) extensive resistance, affecting second line. Of 128, 22 failed at timepoint 2, 17 died, and 32 were lost to follow-up before timepoint 2. Having >5 resistance mutations at timepoint 1 was associated with higher mortality [relative risk ratio (RRR) = 8.7, confidence interval (CI) 2.1 to 36.3] and loss to follow-up (RRR = 3.2, CI 1.1 to 9.2). Switching to second line was associated with lower mortality (RRR <0.05, CI <0.05 to 0.1) and loss to follow-up (RRR = 0.1, CI <0.05 to 0.3). CONCLUSION: Extensive resistance and limited switch to second line in perinatally infected Kenyan CALWH failing first-line ART were associated with long-term failure and mortality. Findings emphasize urgency for interventions to sustain effective, life-long ART in this vulnerable population.

Modeling the Impact of Social Determinants of Health on HIV.

There is growing evidence for the key role of social determinants of health (SDOH) in understanding morbidity and mortality outcomes globally. Factors such as stigma, racism, poverty or access to health and social services represent complex constructs that affect population health via intricate relationships to individual characteristics, behaviors and disease prevention and treatment outcomes. Modeling the role of SDOH is both critically important and inherently complex. Here we describe different modeling approaches and their use in assessing the impact of SDOH on HIV/AIDS. The discussion is thematically divided into mechanistic models and statistical models, while recognizing the overlap between them. To illustrate mechanistic approaches, we use examples of compartmental models and agent-based models; to illustrate statistical approaches, we use regression and statistical causal models. We describe model structure, data sources required, and the scope of possible inferences, highlighting similarities and differences in formulation, implementation, and interpretation of different modeling approaches. We also indicate further needed research on representing and quantifying the effect of SDOH in the context of models for HIV and other health outcomes in recognition of the critical role of SDOH in achieving the goal of ending the HIV epidemic and improving overall population health.

Egocentric social network characteristics and cardiovascular risk among patients with hypertension or diabetes in western Kenya: a cross-sectional analysis from the BIGPIC trial.

OBJECTIVES: Management of cardiovascular disease (CVD) is an urgent challenge in low-income and middle-income countries, and interventions may require appraisal of patients' social networks to guide implementation. The purpose of this study is to determine whether egocentric social network characteristics (SNCs) of patients with chronic disease in western Kenya are associated with overall CVD risk and individual CVD risk factors. DESIGN: Cross-sectional analysis of enrollment data (2017-2018) from the Bridging Income Generation with GrouP Integrated Care trial. Non-overlapping trust-only, health advice-only and multiplex (trust and health advice) egocentric social networks were elicited for each participant, and SNCs representing social cohesion were calculated. SETTING: 24 communities across four counties in western Kenya. PARTICIPANTS: Participants (n=2890) were ≥35 years old with diabetes (fasting glucose ≥7 mmol/L) or hypertension. PRIMARY AND SECONDARY OUTCOMES: We hypothesised that SNCs would be associated with CVD risk status (QRISK3 score). Secondary outcomes were individual CVD risk factors. RESULTS: Among the 2890 participants, 2020 (70%) were women, and mean (SD) age was 60.7 (12.1) years. Forty-four per cent of participants had elevated QRISK3 score (≥10%). No relationship was observed between QRISK3 level and SNCs. In unadjusted comparisons, participants with any individuals in their trust network were more likely to report a good than a poor diet (41% vs 21%). SNCs for the trust and multiplex networks accounted for a substantial fraction of variation in measures of dietary quality and physical activity (statistically significant via likelihood ratio test, adjusted for false discovery rate). CONCLUSION: SNCs indicative of social cohesion appear to be associated with individual behavioural CVD risk factors, although not with overall CVD risk score. Understanding how SNCs of patients with chronic diseases relate to modifiable CVD risk factors could help inform network-based interventions. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov identifier: NCT02501746; https://clinicaltrials.gov/ct2/show/NCT02501746.

Low Seroprevalence of SARS-CoV-2 in Rhode Island blood donors during may 2020 as determined using multiple serological assay formats.

BACKGROUND: Epidemic projections and public health policies addressing Coronavirus disease (COVID)-19 have been implemented without data reporting on the seroconversion of the population since scalable antibody testing has only recently become available. METHODS: We measured the percentage of severe acute respiratory syndrome- Coronavirus-2 (SARS-CoV-2) seropositive individuals from 2008 blood donors drawn in the state of Rhode Island (RI). We utilized multiple antibody testing platforms, including lateral flow immunoassays (LFAs), enzyme-linked immunosorbent assays (ELISAs) and high throughput serological assays (HTSAs). To estimate seroprevalence, we utilized the Bayesian statistical method to adjust for sensitivity and specificity of the commercial tests used. RESULTS: We report than an estimated seropositive rate of RI blood donors of approximately 0.6% existed in April-May of 2020. Daily new case rates peaked in RI in late April 2020. We found HTSAs and LFAs were positively correlated with ELISA assays to detect antibodies specific to SARS-CoV-2 in blood donors. CONCLUSIONS: These data imply that seroconversion, and thus infection, is likely not widespread within this population. We conclude that IgG LFAs and HTSAs are suitable to conduct seroprevalence assays in random populations. More studies will be needed using validated serological tests to improve the precision and report the kinetic progression of seroprevalence estimates.