Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness.

Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.

Electronic Health Record Data for Lyme Disease Surveillance, Massachusetts, USA, 2017-2018.

Lyme disease surveillance based on provider and laboratory reports underestimates incidence. We developed an algorithm for automating surveillance using electronic health record data. We identified potential Lyme disease markers in electronic health record data (laboratory tests, diagnosis codes, prescriptions) from January 2017-December 2018 in 2 large practice groups in Massachusetts, USA. We calculated their sensitivities and positive predictive values (PPV), alone and in combination, relative to medical record review. Sensitivities ranged from 57% (95% CI 47%-69%) for immunoassays to 87% (95% CI 70%-100%) for diagnosis codes. PPVs ranged from 53% (95% CI 43%-61%) for diagnosis codes to 58% (95% CI 50%-66%) for immunoassays. The combination of a diagnosis code and antibiotics within 14 days or a positive Western blot had a sensitivity of 100% (95% CI 86%-100%) and PPV of 82% (95% CI 75%-89%). This algorithm could make Lyme disease surveillance more efficient and consistent.

Tabby2: a user-friendly web tool for forecasting state-level TB outcomes in the United States.

BACKGROUND: In the United States, the tuberculosis (TB) disease burden and associated factors vary substantially across states. While public health agencies must choose how to deploy resources to combat TB and latent tuberculosis infection (LTBI), state-level modeling analyses to inform policy decisions have not been widely available. METHODS: We developed a mathematical model of TB epidemiology linked to a web-based user interface - Tabby2. The model is calibrated to epidemiological and demographic data for the United States, each U.S. state, and the District of Columbia. Users can simulate pre-defined scenarios describing approaches to TB prevention and treatment or create their own intervention scenarios. Location-specific results for epidemiological outcomes, service utilization, costs, and cost-effectiveness are reported as downloadable tables and customizable visualizations. To demonstrate the tool's functionality, we projected trends in TB outcomes without additional intervention for all 50 states and the District of Columbia. We further undertook a case study of expanded treatment of LTBI among non-U.S.-born individuals in Massachusetts, covering 10% of the target population annually over 2025-2029. RESULTS: Between 2022 and 2050, TB incidence rates were projected to decline in all states and the District of Columbia. Incidence projections for the year 2050 ranged from 0.03 to 3.8 cases (median 0.95) per 100,000 persons. By 2050, we project that majority (> 50%) of TB will be diagnosed among non-U.S.-born persons in 46 states and the District of Columbia; per state percentages range from 17.4% to 96.7% (median 83.0%). In Massachusetts, expanded testing and treatment for LTBI in this population was projected to reduce cumulative TB cases between 2025 and 2050 by 6.3% and TB-related deaths by 8.4%, relative to base case projections. This intervention had an incremental cost-effectiveness ratio of $180,951 (2020 USD) per quality-adjusted life year gained from the societal perspective. CONCLUSIONS: Tabby2 allows users to estimate the costs, impact, and cost-effectiveness of different TB prevention approaches for multiple geographic areas in the United States. Expanded testing and treatment for LTBI could accelerate declines in TB incidence in the United States, as demonstrated in the Massachusetts case study.

An outbreak of HIV infection among people who inject drugs in northeastern Massachusetts: findings and lessons learned from a medical record review.

BACKGROUND: We conducted a medical record review for healthcare utilization, risk factors, and clinical data among people who inject drugs (PWID) in Massachusetts to aid HIV outbreak response decision-making and strengthen public health practice. SETTING: Two large community health centers (CHCs) that provide HIV and related services in northeastern Massachusetts. METHODS: Between May and July 2018, we reviewed medical records for 88 people with HIV (PWH) connected to the outbreak. The review period included care received from May 1, 2016, through the date of review. Surveillance data were used to establish date of HIV diagnosis and assess viral suppression. RESULTS: Sixty-nine (78%) people had HIV infection diagnosed during the review period, including 10 acute infections. Persons had a median of 3 primary care visits after HIV diagnosis and zero before diagnosis. During the review period, 72% reported active drug or alcohol use, 62% were prescribed medication assisted treatment, and 41% were prescribed antidepressants. The majority (68, 77%) had a documented ART prescription. HIV viral suppression at < 200 copies/mL was more frequent (73%) than the overall across the State (65%); it did not correlate with any of the sociodemographic characteristics studied in our population. Over half (57%) had been hospitalized at least once during the review period, and 36% had a bacterial infection at hospitalization. CONCLUSIONS: Medical record review with a field investigation of an outbreak provided data about patterns of health care utilization and comorbidities not available from routine HIV surveillance or case interviews. Integration of HIV screening with treatment for HIV and SUD can strengthen prevention and care services for PWID in northeastern Massachusetts.

Cost-effectiveness and Budgetary Impact of Hepatitis C Virus Testing, Treatment, and Linkage to Care in US Prisons.

BACKGROUND: Hepatitis C virus (HCV) testing and treatment uptake in prisons remains low. We aimed to estimate clinical outcomes, cost-effectiveness (CE), and budgetary impact (BI) of HCV testing and treatment in United States (US) prisons or linkage to care at release. METHODS: We used individual-based simulation modeling with healthcare and Department of Corrections (DOC) perspectives for CE and BI analyses, respectively. We simulated a US prison cohort at entry using published data and Washington State DOC individual-level data. We considered permutations of testing (risk factor based, routine at entry or at release, no testing), treatment (if liver fibrosis stage ≥F3, for all HCV infected or no treatment), and linkage to care (at release or no linkage). Outcomes included quality-adjusted life-years (QALY); cases identified, treated, and cured; cirrhosis cases avoided; incremental cost-effectiveness ratios; DOC costs (2016 US dollars); and BI (healthcare cost/prison entrant) to generalize to other states. RESULTS: Compared to "no testing, no treatment, and no linkage to care," the "test all, treat all, and linkage to care at release" model increased the lifetime sustained virologic response by 23%, reduced cirrhosis cases by 54% at a DOC annual additional cost of $1440 per prison entrant, and would be cost-effective. At current drug prices, targeted testing and liver fibrosis-based treatment provided worse outcomes at higher cost or worse outcomes at higher cost per QALY gained. In sensitivity analysis, fibrosis-based treatment restrictions were cost-effective at previous higher drug costs. CONCLUSIONS: Although costly, widespread testing and treatment in prisons is considered to be of good value at current drug prices.

Opioid Use Fueling HIV Transmission in an Urban Setting: An Outbreak of HIV Infection Among People Who Inject Drugs-Massachusetts, 2015-2018.

Objectives. To describe and control an outbreak of HIV infection among people who inject drugs (PWID).Methods. The investigation included people diagnosed with HIV infection during 2015 to 2018 linked to 2 cities in northeastern Massachusetts epidemiologically or through molecular analysis. Field activities included qualitative interviews regarding service availability and HIV risk behaviors.Results. We identified 129 people meeting the case definition; 116 (90%) reported injection drug use. Molecular surveillance added 36 cases to the outbreak not otherwise linked. The 2 largest molecular groups contained 56 and 23 cases. Most interviewed PWID were homeless. Control measures, including enhanced field epidemiology, syringe services programming, and community outreach, resulted in a significant decline in new HIV diagnoses.Conclusions. We illustrate difficulties with identification and characterization of an outbreak of HIV infection among a population of PWID and the value of an intensive response.Public Health Implications. Responding to and preventing outbreaks requires ongoing surveillance, with timely detection of increases in HIV diagnoses, community partnerships, and coordinated services, all critical to achieving the goal of the national Ending the HIV Epidemic initiative.

Sustained Reduction in HIV Diagnoses in Massachusetts, 2000-2014.

OBJECTIVES: To describe secular trends in reported HIV diagnoses in Massachusetts concurrent with treatment access expansion. METHODS: We characterized cases of HIV infection reported to the Massachusetts HIV/AIDS Surveillance Program between 2000 and 2014 by sex, age, race/ethnicity, and exposure mode. We used Poisson regression to test the statistical significance of trends in diagnoses. RESULTS: Between 2000 and 2014, annual new HIV infections diagnosed in Massachusetts decreased by 47% (P < .001 for trend). We observed significant reductions in diagnoses among women (58% when comparing 2000 with 2014), men (42%), Whites (54%), Blacks (51%), and Hispanics (35%; P < .001 for trend). New diagnoses decreased significantly among men who have sex with men (19%), persons who inject drugs (91%), and heterosexuals (86%; P < .001 for trend). We saw statistically significant downward trends among all men by race/ethnicity, but the trend among Black men who have sex with men was nonsignificant. CONCLUSIONS: Sustained reduction in new HIV diagnoses was concurrent with Massachusetts's Medicaid expansion, state health care reform, and public health strategies to improve care access. A contributory effect of expanded HIV treatment and population-level viral suppression is hypothesized for future research.

COVID-19 Severity in People With HIV Compared With Those Without HIV.

BACKGROUND: People with HIV (PWH) may be at risk for more severe COVID-19 outcomes. We compared risk for severe COVID-19 in PWH with matched individuals without HIV. METHODS: We identified adults in Massachusetts with a positive SARS-CoV-2 test, March 2020-July 2022, using electronic medical record data from 3 large clinical practice groups. We then used regression models to compare outcomes among PWH versus propensity score-matched people without HIV (matched 20:1) for severe COVID-19 (pneumonia or acute respiratory distress syndrome), hospitalization, and hospital length of stay. RESULTS: We identified 171,058 individuals with COVID-19; among them, 768 PWH were matched to 15,360 individuals without HIV. Overall, severe COVID-19 and hospitalization were similar in PWH and those without HIV (severe COVID-19: 3.8% vs 3.0%, adjusted odds ratio [OR] 1.27, 95% confidence interval [CI]: 0.86-1.87; hospitalization: 12.1% vs 11.3%, adjusted OR: 1.08, 95% CI: 0.87 to 1.35). Compared with people without HIV, PWH with low CD4 T-cell counts (<200 cells/mm 3 ) had more severe COVID-19 (adjusted OR: 3.99, 95% CI: 2.06 to 7.74) and hospitalization (adjusted OR: 2.26, 95% CI: 1.35 to 3.80), but PWH with high CD4 counts had lower odds of hospitalization (adjusted OR: 0.73, 95% CI: 0.52 to 1.03). CONCLUSIONS: PWH with low CD4 T-cell counts had worse COVID-19 outcomes compared with people without HIV, but outcomes for those with high CD4 counts were similar to, or better than, those without HIV. It is unclear whether these findings are generalizable to settings where PWH have less access to and engagement with health care.

Potential impact of curative and preventive interventions toward hepatitis C elimination in people who inject drugs-A network modeling study.

BACKGROUND: Injection-equipment-sharing networks play an important role in hepatitis C virus (HCV) transmission among people who inject drugs (PWID). Direct-acting antiviral (DAA) treatments for HCV infection and interventions to prevent HCV transmission are critical components of an overall hepatitis C elimination strategy, but how they contribute to the elimination outcomes in different PWID network settings are unclear. METHODS: We developed an agent-based network model of HCV transmission through the sharing of injection equipment among PWID and parameterized and calibrated the model with rural PWID data in the United States. We modeled curative and preventive interventions at annual coverage levels of 12.5 %, 25 %, or 37.5 % (cumulative percentage of eligible individuals engaged), and two allocation approaches: random vs targeting PWID with more injection partners (hereafter 'degree-based'). We compared the impact of these intervention strategies on prevalence and incidence of HCV infections. We conducted sensitivity analysis on key parameters governing the effects of curative and preventive interventions and PWID network characteristics. RESULTS: Combining curative and preventive interventions at 37.5 % annual coverage with degree-based allocation decreased prevalence and incidence of HCV infection by 67 % and 70 % over two years, respectively. Curative interventions decreased prevalence by six to 12 times more than preventive interventions, while curative and preventive interventions had comparable effectiveness on reducing incidence. Intervention impact increased with coverage almost linearly across all intervention strategies, and degree-based allocation was always more effective than random allocation, especially for preventive interventions. Results were sensitive to parameter values defining intervention effects and network mean degree. CONCLUSION: DAA treatments are effective in reducing both prevalence and incidence of HCV infection in PWID, but preventive interventions play a significant role in reducing incidence when intervention coverage is low. Increasing coverage, including efforts in reaching individuals with the most injection partners, preventing reinfection, and improving compliance and retention in preventive services can substantially improve the outcomes. PWID network characteristics should be considered when designing hepatitis C elimination programs.

Hepatitis C Cascades: Data to Inform Hepatitis C Elimination in the United States.

The United States has a goal to eliminate hepatitis C as a public health threat by 2030. To accomplish this goal, hepatitis C virus (HCV) care cascades (hereinafter, HCV cascades) can be used to measure progress toward HCV elimination and identify disparities in HCV testing and care. In this topical review of HCV cascades, we describe common definitions of cascade steps, review the application of HCV cascades in health care and public health settings, and discuss the strengths and limitations of data sources used. We use examples from the Massachusetts Department of Public Health as a case study to illustrate how multiple data sources can be leveraged to produce HCV cascades for public health purposes. HCV cascades in health care settings provide actionable data to improve health care quality and delivery of services in a single health system. In public health settings at jurisdictional and national levels, HCV cascades describe HCV diagnosis and treatment for populations, which can be challenging in the absence of a single data source containing complete, comprehensive, and timely data representing all steps of a cascade. Use of multiple data sources and strategies to improve interoperability of health care and public health data systems can advance the use of HCV cascades and speed progress toward HCV elimination.

Costs and cost-effectiveness of a collaborative data-to-care intervention for HIV treatment and care in the United States.

INTRODUCTION: Data-to-care programmes utilize surveillance data to identify persons who are out of HIV care, re-engage them in care and improve HIV care outcomes. We assess the costs and cost-effectiveness of re-engagement in an HIV care intervention in the United States. METHODS: The Cooperative Re-engagement Control Trial (CoRECT) employed a data-to-care collaborative model between health departments and HIV care providers, August 2016-July 2018. The health departments in Connecticut (CT), Massachusetts (MA) and Philadelphia (PHL) collaborated with HIV clinics to identify newly out-of-care patients and randomize them to receive usual linkage and engagement in care services (standard-of-care control arm) or health department-initiated active re-engagement services (intervention arm). We used a microcosting approach to identify the activities and resources involved in the CoRECT intervention, separate from the standard-of-care, and quantified the costs. The cost data were collected at the start-up and recurrent phases of the trial to incorporate potential variation in the intervention costs. The costs were estimated from the healthcare provider perspective. RESULTS: The CoRECT trial in CT, MA and PHL randomly assigned on average 327, 316 and 305 participants per year either to the intervention arm (n = 166, 159 and 155) or the standard-of-care arm (n = 161, 157 and 150), respectively. Of those randomized, the number of participants re-engaged in care within 90 days in the intervention and standard-of-care arms was 85 and 70 in CT, 84 and 70 in MA, and 98 and 67 in PHL. The additional number of participants re-engaged in care in the intervention arm compared with those in the standard-of-care arm was 15 (CT), 14 (MA) and 31 (PHL). We estimated the annual total cost of the CoRECT intervention at $490,040 in CT, $473,297 in MA and $439,237 in PHL. The average cost per participant enrolled was $2952, $2977 and $2834 and the average cost per participant re-engaged in care was $5765, $5634 and $4482. We estimated an incremental cost per participant re-engaged in care at $32,669 (CT), $33,807 (MA) and $14,169 (PHL). CONCLUSIONS: The costs of the CoRECT intervention that identified newly out-of-care patients and re-engaged them in HIV care are comparable with other similar interventions, suggesting a potential for its cost-effectiveness in the US context.

The Cooperative Re-Engagement Controlled Trial (CoRECT): Durable Viral Suppression Assessment.

BACKGROUND: A collaborative, data-to-care strategy to identify persons with HIV (PWH) newly out-of-care, combined with an active public health intervention, significantly increases the proportion of PWH re-engaged in HIV care. We assessed this strategy's impact on durable viral suppression (DVS). METHODS: A multisite, prospective randomized controlled trial for out-of-care individuals using a data-to-care strategy and comparing public health field services to locate, contact, and facilitate access to care versus the standard of care. DVS was defined as the last viral load, the viral load at least 3 months before, and any viral load between the 2 were all <200 copies/mL during the 18-month postrandomization. Alternative definitions of DVS were also analyzed. RESULTS: Between August 1, 2016-July 31, 2018, 1893 participants were randomized from Connecticut (n = 654), Massachusetts (n = 630), and Philadelphia (n = 609). Rates of achieving DVS were similar in the intervention and standard-of-care arms in all jurisdictions (all sites: 43.4% vs 42.4%, P = 0.67; Connecticut: 46.7% vs 45.0%, P = 0.67; Massachusetts: 40.7 vs 44.4%, P = 0.35; Philadelphia: 42.4% vs 37.3%, P = 0.20). There was no association between DVS and the intervention (RR: 1.01, CI: 0.91-1.12; P = 0.85) adjusting for site, age categories, race/ethnicity, birth sex, CD4 categories, and exposure categories. CONCLUSION: A collaborative, data-to-care strategy, and active public health intervention did not increase the proportion of PWH achieving DVS, suggesting additional support to promote retention in care and antiretroviral adherence may be needed. Initial linkage and engagement services, through data-to-care or other means, are likely necessary but insufficient for achieving DVS for all PWH.

Syndromic Surveillance for COVID-19, Massachusetts, February 2020-November 2022: The Impact of Fever and Severity on Algorithm Performance.

OBJECTIVES: Syndromic surveillance can help identify the onset, location, affected populations, and trends in infectious diseases quickly and efficiently. We developed an electronic medical record-based surveillance algorithm for COVID-19-like illness (CLI) and assessed its performance in 5 Massachusetts medical practice groups compared with statewide counts of confirmed cases. MATERIALS AND METHODS: Using data from February 2020 through November 2022, the CLI algorithm was implemented in sites that provide ambulatory and inpatient care for about 25% of the state. The initial algorithm for CLI was modeled on influenza-like illness: an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code for COVID-19 and an ICD-10-CM diagnosis code suggesting severe lower respiratory tract infection or ≥1 ICD-10-CM diagnosis code for upper or lower respiratory tract infection plus fever. We generated weekly counts of CLI cases and patients with ≥1 clinical encounter and visually compared trends with those of statewide laboratory-confirmed cases. RESULTS: The initial algorithm tracked well with the spring 2020 wave of COVID-19, but the components that required fever did not clearly detect the November 2020-January 2021 surge and identified <1% of weekly encounters as CLI. We revised the algorithm by adding more mild symptoms and removing the fever requirement; this revision improved alignment with statewide confirmed cases through spring 2022 and increased the proportion of encounters identified as CLI to about 2% to 6% weekly. Alignment between CLI trends and confirmed COVID-19 case counts diverged again in fall 2022, likely because of decreased COVID-19 testing and increases in other respiratory viruses. PRACTICE IMPLICATIONS: Our work highlights the importance of using a broad definition for COVID-19 syndromic surveillance and the need for surveillance systems that are flexible and adaptable to changing trends and patterns in disease or care.

Using the exploration, preparation, implementation, sustainment (EPIS) framework to assess the cooperative re-engagement controlled trial (CoRECT).

Background: "Data to Care" (D2C) is a strategy which relies on a combination of public health surveillance data supplemented by clinic data to support continuity of HIV care. The Cooperative Re-Engagement Controlled Trial (CoRECT) was a CDC-sponsored randomized controlled trial of a D2C model, which provided an opportunity to examine the process of implementing an intervention for people with HIV (PWH) who are out-of-care across three public health department jurisdictions. Using the EPIS (Exploration, Preparation, Implementation, Sustainment) framework, we aimed to retrospectively describe the implementation process for each site to provide insights and guidance to inform future D2C activities implemented by public health agencies and their clinical and community partners. Methods: After completion of CoRECT, the three (Connecticut, Massachusetts, Philadelphia) trial sites reviewed study protocols and held iterative discussions to describe and compare their processes regarding case identification, interactions with partnering clinics and patients, and sustainability. The EPIS framework provided a structure for comparing key organizational and operational practices and was applied to the entire implementation process. Results: The trial sites varied in their implementation processes and the specific elements of the intervention. Factors including prior D2C experience, data management and analytic infrastructure, staff capacity, and relationships with clinic partners informed intervention development and implementation. Additionally, this review identified key lessons learned including to: (1) explore new supplemental sources for public health surveillance data; (2) work with stakeholders representing core functions/components in the early stages of the intervention design process; (3) build flexibility into all components of the follow-up activities; and (4) integrate data sharing, project management, and follow-up activities within existing DPH organizational structure. Conclusion: The CoRECT study provides a general blueprint and lessons learned for implementing a D2C intervention for re-engagement in HIV care. Interventions should be tailored to local operational and structural factors, and responsive to evolving clinical and public health practices.

Expanding Latent Tuberculosis Infection Testing and Treatment in Massachusetts Primary Care Clinics via the ECHO Model.

INTRODUCTION/OBJECTIVES: In the US, reactivation of latent tuberculosis infection (LTBI) accounts for 80% of new cases. In 2016, the US Preventive Services Task Force provided a new recommendation that primary care providers (PCPs) should conduct LTBI screening, whereas in the past, LTBI cases were evaluated and treated by specialty providers. This shift in care revealed knowledge gaps surrounding LTBI treatment among PCPs. This study assessed changes in PCPs' confidence for performing key aspects of LTBI care before and after participation in an LTBI Extension for Community Healthcare Outcomes (ECHO) course. METHODS: The ECHO Model™ is an evidence-based telementoring intervention. Participants were primary care team members from clinics throughout Massachusetts who voluntarily enrolled in the ECHO course. In this mixed-methods evaluation, primary outcomes were PCP self-reported confidence changes by pre- and post-course surveys and post-course semi-structured interviews. RESULTS: Twenty PCPs (43% of registered PCPs) attended at least 3 of the 6 sessions and 24 PCPs (31% of registered PCPs) completed at least one survey. Confidence increased in selecting a test (P = .004), interpreting tuberculosis infection test results (P = .03), and selecting a treatment regimen (P = .004). Qualitative interviews with 3 PCPs revealed practice changes including switching to interferon gamma release assays for testing and using rifampin for treatment. CONCLUSIONS: Use of the ECHO model to train PCPs in LTBI management is feasible and efficacious. For continuing medical education, ECHO courses can be leveraged to reduce health disparities in settings where PCPs' lack of familiarity about a treatment topic contributes to poor health outcomes.

A review of network simulation models of hepatitis C virus and HIV among people who inject drugs.

Network modelling is a valuable tool for simulating hepatitis C virus (HCV) and HIV transmission among people who inject drugs (PWID) and assessing the potential impact of treatment and harm-reduction interventions. In this paper, we review literature on network simulation models, highlighting key structural considerations and questions that network models are well suited to address. We describe five approaches (Erdös-Rényi, Stochastic Block, Watts-Strogatz, Barabási-Albert, and Exponential Random Graph Model) used to model partnership formation with emphasis on the strengths of each approach in simulating different features of real-world PWID networks. We also review two important structural considerations when designing or interpreting results from a network simulation study: (1) dynamic vs. static network and (2) injection only vs. both injection and sexual networks. Dynamic network simulations allow partnerships to evolve and disintegrate over time, capturing corresponding shifts in individual and population-level risk behaviour; however, their high level of complexity and reliance on difficult-to-observe data has driven others to develop static network models. Incorporating both sexual and injection partnerships increases model complexity and data demands, but more accurately represents HIV transmission between PWID and their sexual partners who may not also use drugs. Network models add the greatest value when used to investigate how leveraging network structure can maximize the effectiveness of health interventions and optimize investments. For example, network models have shown that features of a given network and epidemic influence whether the greatest community benefit would be achieved by allocating hepatitis C or HIV treatment randomly, versus to those with the most partners. They have also demonstrated the potential for syringe services and "buddy sharing" programs to reduce disease transmission.

The Cooperative Re-Engagement Controlled trial (CoRECT): A randomised trial to assess a collaborative data to care model to improve HIV care continuum outcomes.

Background: Persons with HIV (PWH), aware of their HIV infection but not in care account for an estimated 42.6% of HIV transmissions in the United States. Health departments and clinics implemented a collaborative data-to-care strategy to identify persons newly out-of-care with the objective of increasing re-engagement, retention in medical care, and viral load suppression. Methods: A multi-site, prospective randomised trial was conducted to identify newly out-of-care PWH using surveillance and clinic data in Connecticut (CT), Massachusetts (MA) and Philadelphia (PHL). All out-of-care participants were randomised to receive standard of care or an active public health intervention. Re-engagement in care was defined as having a documented CD4 count and/or HIV viral load within 90 days of randomization. Retention was defined as having at least two CD4 count and/or HIV viral load results ≥ 3 months apart within 12 months of randomization, and viral load suppression as having a viral load < 200 copies/ml within 12 months of randomization. Findings: Between August 2016 and July 2018, 1893 out-of-care participants were randomised from CT (N = 654), MA (N = 630), and PHL (N = 609). Participants were male (69.5%), non-Hispanic Black (48.3%) and men who have sex with men (38.8%). Re-engagement within 90 days was significantly higher for the intervention group overall and in all three jurisdictions (All sites: 54.9% vs 42.1%, p < 0.0001; CT: 51.2% vs 41.9%, p = 0.02; MA: 52.7% vs 44.1%, p = 0.03; PHL 61.2% vs 40.3%, p < 0.0001). Retention in care over 12 months improved overall (p = 0.04). Median time to viral suppression was reduced overall (p = 0.0006); CT (p = 0.32), MA (p = 0.02) and PHL (p < 0.0001). Interpretation: This trial showed that a collaborative, data-to-care strategy, and active public health intervention led by health departments significantly increases the proportion of PWH re-engaged in HIV care and may improve retention in care and decrease time to viral suppression.

Leveraging Health Department Capacities, Partnerships, and Health Insurance for Infectious Disease Response in Massachusetts, 2014-2018.

Policies facilitating integration of public health programs can improve the public health response, but the literature on approaches to integration across multiple system levels is limited. We describe the efforts of the Massachusetts Department of Public Health to integrate its HIV, viral hepatitis, sexually transmitted infection (STI), and tuberculosis response through policies that mandated contracted organizations to submit specimens for testing to the Massachusetts State Public Health Laboratory; co-test blood specimens for HIV, hepatitis C virus (HCV), and syphilis; integrate HIV, viral hepatitis, and STI disease surveillance and case management in a single data system; and implement an integrated infectious disease drug assistance program. From 2014 through 2018, the number of tests performed by the Massachusetts State Public Health Laboratory increased from 16 321 to 33 674 for HIV, from 11 054 to 33 670 for HCV, and from 19 169 to 30 830 for syphilis. Service contracts enabled rapid response to outbreaks of HIV, hepatitis A, and hepatitis B. Key challenges included lack of a billing infrastructure at the Massachusetts State Public Health Laboratory; the need to complete negotiations with insurers and to establish a retained revenue account to receive health insurance reimbursements for testing services; and time to train testing providers in phlebotomy for required testing. Investing in laboratory infrastructure; creating billing mechanisms to maximize health insurance reimbursement; proactively engaging providers, community members, and other stakeholders; and building capacity to transform practices are needed. Using multilevel policy approaches to integrate the public health response to HIV, STI, viral hepatitis, and tuberculosis is feasible and adaptable to other public health programs.

Prevention options for positives: the effects of a health communication intervention for men who have sex with men living with HIV/AIDS.

This article reports the results of a small-scale quasi-experiment that tested the efficacy of the Prevention Options for Positives intervention. The experiment tested for the outcomes of group sessions combined with individual-level counseling (ILC) versus ILC-only for men who have sex with men who are HIV positive. Both arms of the intervention were based on behavior change theory and dealt specifically with communication outcomes. The results indicate that the group- and individual-level interventions combined have a greater impact on risk communication behaviors with main partners than did the ILC-only sessions. group-session/ILC participants were more likely to decide not to have sex if they were drunk or high, and more likely to tell their partner and ask their partner about HIV status than were participants in the ILC groups. Knowledge about HIV was relatively high, and there was little change across groups. The Prevention Options for Positives intervention influenced the relative importance of various referent groups, but normative beliefs were not affected. The implications of these findings for communication practice and research with HIV-positive men who have sex with men are addressed.