Faculty Bibliography

On June 1, 2020, NYC Health + Hospitals, in partnership with the NYC Department of Health and Mental Hygiene, other city agencies, and a large network of community partners, launched the New York City Test & Trace (T2) COVID-19 response program to identify and isolate cases, reduce transmission through contact tracing, and provide support to residents during isolation or quarantine periods. In this paper, we describe lessons learned with respect to planning and implementation of case notification and contact tracing. Our findings are based on extensive document review and analysis of 74 key informant interviews with T2 leadership and frontline staff, cases, and contacts conducted between January and September 2022. Interviews elicited respondent background, history of program development, program leadership and structure, goals of the program, program evolution, staffing, data systems, elements of community engagement, trust with community, program reach, timeliness, equity, general barriers and challenges, general facilitators and best practices, and recommendations/improvement for the program. Facilitators and barriers revealed in the interviews primarily revolved around hiring and managing staff, data and technology, and quality of interactions with the public. Based on these facilitators and barriers, we identify suggestions to support effective planning and response for future case notification and contact tracing programs, including recommendations for planning during latent periods, case management and data systems, and processes for outreach to cases and contacts.

The COVID-19 pandemic highlighted the importance of addressing social needs in a crisis context. Some US jurisdictions integrated a social service component into case investigation and contact tracing (CI/CT) programs, including the New York City (NYC) Test & Trace (T2) Program; the Take Care initiative referred NYC residents who tested positive or were exposed to COVID-19 to services to support isolation and quarantine and meet basic needs. More research is needed to determine effective implementation strategies for integrating social needs provision into CI/CT programs. To identify barriers and facilitators to the implementation of the Take Care initiative, we conducted key informant interviews with program staff, community-based organization partners, and cases and contacts as part of a larger evaluation of the T2 program. Interviews were recorded, transcribed, and analyzed using rapid qualitative methods. Key facilitators to implementation included utilizing a case management software system, employing strategies to encourage service uptake, leveraging cross-agency collaborations, and partnering with community-based organizations for resource navigation. Barriers identified included external management of the software system, challenges reaching and engaging the public, administrative complications due to shifting collaborations, and management of CBO partners' structure and hiring. Based on our findings, we provide recommendations to support effective planning and implementation of social needs service provision in a crisis context. Future research should focus on testing promising implementation strategies highlighted in this study and applying them to varied contexts and crisis situations.

BACKGROUND:New York City (NYC) was the first COVID-19 epicenter in the United States and home to one of the country's largest contact tracing programs, NYC Test & Trace (T2). Understanding points of attrition along the stages of program implementation and follow-up can inform contact tracing efforts for future epidemics or pandemics. The objective of this study was to evaluate the completeness and timeliness of T2 case and contact notification and monitoring using a "cascade of care" approach. METHODS:This cross-sectional study included all SARS-CoV-2 cases and contacts reported to T2 from May 31, 2020 to January 1, 2022. Attrition along the "cascade of care" was defined as: (1) attempted, (2) reached, (3) completed intake (main outcome), (4) eligible for monitoring, and (5) successfully monitored. Timeliness was assessed: (1) by median days from a case's date of testing until their positive result was reported to T2, (2) from result until the case was notified by T2, and (3) from a case report of a contact until notification of the contact. RESULTS:A total of 1.45 million cases and 1.38 million contacts were reported to T2 during this period. For cases, attrition occurred evenly across the first three cascade steps (~-12%) and did not change substantially until the Omicron wave in December 2021. During the Omicron wave, the proportion of cases attempted dropped precipitously. For contacts, the largest attrition occurred between attempting and reaching (-27%), and attrition rose with each COVID-19 wave as contact volumes increased. Attempts to reach contacts discontinued entirely during the Omicron wave. Overall, 67% of cases and 49% of contacts completed intake interviews (79% and 57% prior to Omicron). T2 was timely, with a median of 1 day to receive lab results, 2 days to notify cases, and < 1 day to notify contacts. CONCLUSIONS:T2 provided a large volume of NYC residents with timely notification and monitoring. Engagement in the program was lower for contacts than cases, with the largest gap coming from inability to reach individuals during call attempts. To strengthen future test-and-trace efforts, strategies are needed to encourage acceptance of local contact tracer outreach attempts.

In mid-2022, New York City (NYC) became the epicenter of the US mpox outbreak. We provided real-time mpox case forecasts to the NYC Department of Health and Mental Hygiene to aid in outbreak response. Forecasting methodologies evolved as the epidemic progressed. Initially, lacking knowledge of at-risk population size, we used exponential growth models to forecast cases. Once exponential growth slowed, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) model. Retrospectively, we explored if forecasts could have been improved using an SEIR model in place of our early exponential growth model, with or without knowing the case detection rate. Early forecasts from exponential growth models performed poorly, as 2-week mean absolute error (MAE) grew from 53 cases/week (July 1-14) to 457 cases/week (July 15-28). However, when exponential growth slowed, providing insight into susceptible population size, an SEIR model was able to accurately predict the remainder of the outbreak (7-week MAE: 13.4 cases/week). Retrospectively, we found there was not enough known about the epidemiological characteristics of the outbreak to parameterize an SEIR model early on. However, if the at-risk population and case detection rate were known, an SEIR model could have improved accuracy over exponential growth models early in the outbreak.

The HIV epidemic in sub-Saharan Africa displays a varied geographical distribution, with particular regions termed as HIV hotspots due to a higher prevalence of infection. Addressing these hotspots is essential for controlling the epidemic. However, these regions, influenced by historical factors, challenge standard interventions. Legacy effects-the lasting impact of past events-play a substantial role in the persistence of these hotspots. To address this challenge of the standard interventions, we propose a shift towards the UNAIDS 95-95-95 targets. Spatial analysis of HIV viral load and antiretroviral therapy coverage can provide a more comprehensive perspective on the epidemic's dynamics. Studies in Zambia and Zimbabwe, using this approach, have revealed disparities in HIV care metrics across regions. By focusing on the UNAIDS 95-95-95 targets, more effective control strategies can be designed, with consideration of both historical and current factors. This approach would offer a solution-oriented strategy, emphasising tailored interventions based on specific regional needs.

Background Given the disproportionate rates of incarceration and lower life expectancy (LE) among Black sexual minority men (BSMM) and Black transgender women (BTW) with HIV, we modeled the impact of decarceration and screening for psychiatric conditions and substance use on LE of US BSMM/BTW with HIV. Methods We augmented a microsimulation model previously validated to predict LE and leading causes of death in the US with estimates from the HPTN 061 cohort and the Veteran's Aging Cohort Studies. We estimated independent associations among psychiatric and substance use disorders, to simulate the influence of treatment of one condition on improvement on others. We used this augmented simulation to estimate LE for BSMM/BTW with HIV with a history of incarceration under alternative policies of decarceration (i.e., reducing the fraction exposed to incarceration), screening for psychiatric conditions and substance use, or both. Results Baseline LE was 61.3 years. Reducing incarceration by 25%, 33%, 50%, and 100% increased LE by 0.29 years, 0.31 years, 0.53 years, and 1.08 years, respectively, versus no reductions in incarceration. When reducing incarceration by 33% and implementing screening for alcohol, tobacco, substance use, and depression, in which a positive screen triggers diagnostic assessment for all psychiatric and substance use conditions and linkage to treatment, LE increased by 1.52 years compared to no screening or decarceration. Discussion LE among BSMM/BTW with HIV is short compared with other people with HIV. Reducing incarceration and improving screening and treatment of psychiatric conditions and substance use could substantially increase LE in this population.

BACKGROUND:The distribution of new HIV infections among key populations, including female sex workers (FSWs), gay men and other men who have sex with men (MSM), and people who inject drugs (PWID) are essential information to guide an HIV response, but data are limited in sub-Saharan Africa (SSA). We analyzed empirically derived and mathematical model-based estimates of HIV incidence among key populations and compared with the Joint United Nations Programme on HIV/AIDS (UNAIDS) estimates. METHODS:We estimated HIV incidence among FSW and MSM in SSA by combining meta-analyses of empirical key population HIV incidence relative to the total population incidence with key population size estimates (KPSE) and HIV prevalence. Dynamic HIV transmission model estimates of HIV incidence and percentage of new infections among key populations were extracted from 94 country applications of 9 mathematical models. We compared these with UNAIDS-reported distribution of new infections, implied key population HIV incidence and incidence-to-prevalence ratios. RESULTS:Across SSA, empirical FSW HIV incidence was 8.6-fold (95% confidence interval: 5.7 to 12.9) higher than total population female 15-39 year incidence, and MSM HIV incidence was 41.8-fold (95% confidence interval: 21.9 to 79.6) male 15-29 year incidence. Combined with KPSE, these implied 12% of new HIV infections in 2021 were among FSW and MSM (5% and 7% respectively). In sensitivity analysis varying KPSE proportions within 95% uncertainty range, the proportion of new infections among FSW and MSM was between 9% and 19%. Insufficient data were available to estimate PWID incidence rate ratios. Across 94 models, median proportion of new infections among FSW, MSM, and PWID was 6.4% (interquartile range 3.2%-11.7%), both much lower than the 25% reported by UNAIDS. CONCLUSION/CONCLUSIONS:Empirically derived and model-based estimates of HIV incidence confirm dramatically higher HIV risk among key populations in SSA. Estimated proportions of new infections among key populations in 2021 were sensitive to population size assumptions and were substantially lower than estimates reported by UNAIDS.

BACKGROUND:Key populations (KPs), including female sex workers (FSWs), gay men and other men who have sex with men (MSM), people who inject drugs (PWID), and transgender women (TGW) experience disproportionate risks of HIV acquisition. The UNAIDS Global AIDS 2022 Update reported that one-quarter of all new HIV infections occurred among their non-KP sexual partners. However, this fraction relied on heuristics regarding the ratio of new infections that KPs transmitted to their non-KP partners to the new infections acquired among KPs (herein referred to as "infection ratios"). We recalculated these ratios using dynamic transmission models. SETTING/METHODS:One hundred seventy-eight settings (106 countries). METHODS:Infection ratios for FSW, MSM, PWID, TGW, and clients of FSW were estimated from 12 models for 2020. RESULTS:Median model estimates of infection ratios were 0.7 (interquartile range: 0.5-1.0; n = 172 estimates) and 1.2 (0.8-1.8; n = 127) for acquisitions from FSW clients and transmissions from FSW to all their non-KP partners, respectively, which were comparable with the previous UNAIDS assumptions (0.2-1.5 across regions). Model estimates for female partners of MSM were 0.5 (0.2-0.8; n = 20) and 0.3 (0.2-0.4; n = 10) for partners of PWID across settings in Eastern and Southern Africa, lower than the corresponding UNAIDS assumptions (0.9 and 0.8, respectively). The few available model estimates for TGW were higher [5.1 (1.2-7.0; n = 8)] than the UNAIDS assumptions (0.1-0.3). Model estimates for non-FSW partners of FSW clients in Western and Central Africa were high (1.7; 1.0-2.3; n = 29). CONCLUSIONS:Ratios of new infections among non-KP partners relative to KP were high, confirming the importance of better addressing prevention and treatment needs among KP as central to reducing overall HIV incidence.

BACKGROUND:Mathematical models are increasingly used to inform HIV policy and planning. Comparing estimates obtained using different mathematical models can test the robustness of estimates and highlight research gaps. As part of a larger project aiming to determine the optimal allocation of funding for HIV services, in this study we compare projections from five mathematical models of the HIV epidemic in South Africa: EMOD-HIV, Goals, HIV-Synthesis, Optima, and Thembisa. METHODS:The five modelling groups produced estimates of the total population, HIV incidence, HIV prevalence, proportion of people living with HIV who are diagnosed, ART coverage, proportion of those on ART who are virally suppressed, AIDS-related deaths, total deaths, and the proportion of adult males who are circumcised. Estimates were made under a "status quo" scenario for the period 1990 to 2040. For each output variable we assessed the consistency of model estimates by calculating the coefficient of variation and examining the trend over time. RESULTS:For most outputs there was significant inter-model variability between 1990 and 2005, when limited data was available for calibration, good consistency from 2005 to 2025, and increasing variability towards the end of the projection period. Estimates of HIV incidence, deaths in people living with HIV, and total deaths displayed the largest long-term variability, with standard deviations between 35 and 65% of the cross-model means. Despite this variability, all models predicted a gradual decline in HIV incidence in the long-term. Projections related to the UNAIDS 95-95-95 targets were more consistent, with the coefficients of variation below 0.1 for all groups except children. CONCLUSIONS:While models produced consistent estimates for several outputs, there are areas of variability that should be investigated. This is important if projections are to be used in subsequent cost-effectiveness studies.