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1. Is There a Temporal Relationship between COVID-19 Infections among Prison Staff, Incarcerated Persons and the Larger Community in the United States?

   https://doi.org/10.3390/ijerph18136873  

   Wallace, Danielle; Eason, John M.; Walker, Jason; Towers, Sherry; Grubesic, Tony H.; Nelson, Jake R. (July 2021, International Journal of Environmental Research and Public Health)

   null (Ed.)

   Background: Our objective was to examine the temporal relationship between COVID-19 infections among prison staff, incarcerated individuals, and the general population in the county where the prison is located among federal prisons in the United States. Methods: We employed population-standardized regressions with fixed effects for prisons to predict the number of active cases of COVID-19 among incarcerated persons using data from the Federal Bureau of Prisons (BOP) for the months of March to December in 2020 for 63 prisons. Results: There is a significant relationship between the COVID-19 prevalence among staff, and through them, the larger community, and COVID-19 prevalence among incarcerated persons in the US federal prison system. When staff rates are low or at zero, COVID-19 incidence in the larger community continues to have an association with COVID-19 prevalence among incarcerated persons, suggesting possible pre-symptomatic and asymptomatic transmission by staff. Masking policies slightly reduced COVID-19 prevalence among incarcerated persons, though the association between infections among staff, the community, and incarcerated persons remained significant and strong. Conclusion: The relationship between COVID-19 infections among staff and incarcerated persons shows that staff is vital to infection control, and correctional administrators should also focus infection containment efforts on staff, in addition to incarcerated persons. 

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2. Impact of Pandemic-Induced Service Disruptions and Behavioral Changes on Hepatitis C Virus and HIV Transmission Amongst People Who Inject Drugs: A Modeling Study

   https://doi.org/10.1093/infdis/jiae599  

   Wang, Jasmine; Genberg, Becky L; Feder, Kenneth A; Kirk, Gregory D; Mehta, Shruti H; Grantz, Kyra; Wesolowski, Amy (December 2024, The Journal of Infectious Diseases)

   Abstract BackgroundThe coronavirus disease 2019 (COVID-19) pandemic may have disproportionally impacted vulnerable groups such as people who inject drugs (PWID) through reduced health care services as well as social changes from pandemic mitigation measures. Understanding how the COVID-19 pandemic and associated mitigation strategies subsequently changed the trajectory of hepatitis C virus (HCV) and human immunodeficiency virus (HIV) transmission is critical to estimating disease burdens, identifying outbreak risk, and developing informed intervention strategies. MethodsUsing behavioral data from the AIDS Linked to the IntraVenous Experience (ALIVE) study, an ongoing community-based cohort of PWID in Baltimore, United States, and an individual-based network model, we explored the impacts of service disruptions combined with changes in social networks and injecting behaviors of PWID on HCV and HIV transmission. ResultsAnalyses of ALIVE data showed that during the pandemic, there was an acceleration in injection cessation trajectories overall, but those who continued injecting increased the frequency of injection; at the same time, individual drug-use networks became smaller and the probability of injecting with others decreased. Simulation results demonstrated that HCV and HIV prevalence increased from service disruptions alone, but these effects were mitigated when including observed behavior changes in addition. ConclusionsModel results combined with rich individual behavioral data indicated that pandemic-induced behavioral changes of PWID that lasted longer than service disruptions could have offset the increasing disease burden caused by disrupted service access during the pandemic. 

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3. Assessing cost-effectiveness of hepatitis C testing pathways in Georgia using the Hep C Testing Calculator

   https://doi.org/10.1038/s41598-021-00362-y  

   Adee, Madeline; Zhuo, Yueran; Zhong, Huaiyang; Zhan, Tiannan; Aggarwal, Rakesh; Shilton, Sonjelle; Chhatwal, Jagpreet (December 2021, Scientific Reports)

   Abstract The cost of testing can be a substantial contributor to hepatitis C virus (HCV) elimination program costs in many low- and middle-income countries such as Georgia, resulting in the need for innovative and cost-effective strategies for testing. Our objective was to investigate the most cost-effective testing pathways for scaling-up HCV testing in Georgia. We developed a Markov-based model with a lifetime horizon that simulates the natural history of HCV, and the cost of detection and treatment of HCV. We then created an interactive online tool that uses results from the Markov-based model to evaluate the cost-effectiveness of different HCV testing pathways. We compared the current standard-of-care (SoC) testing pathway and four innovative testing pathways for Georgia. The SoC testing was cost-saving compared to no testing, but all four new HCV testing pathways further increased QALYs and decreased costs. The pathway with the highest patient follow-up, due to on-site testing, resulted in the highest discounted QALYs (123 QALY more than the SoC) and lowest costs ($127,052 less than the SoC) per 10,000 persons screened. The current testing algorithm in Georgia can be replaced with a new pathway that is more effective while being cost-saving. 

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4. Estimating community‐level prevalence of opioid use disorder: Extrapolating from Medicaid claims data and other publicly available data sources in Ohio, USA

   https://doi.org/10.1111/add.70278  

   Dowd, William N; Chen, Qiushi; Barbosa, Carolina; Sahinkoc, H Mert; Barocas, Joshua; Chhatwal, Jagpreet; Aldridge, Arnie P; Zarkin, Gary A; Knudsen, Amy B (April 2026, Addiction)

   Abstract Background and aimsAddressing the opioid crisis requires opioid use disorder (OUD) prevalence estimates at the community level for targeted interventions. This study presents a new method that utilizes Medicaid claims data and publicly available data to estimate OUD prevalence at the United States (US) county level and compares it with other existing estimates. Design, setting and participantsThis study utilized data on OUD diagnoses among Medicaid beneficiaries in combination with national survey data, Census data and published literature to estimate the prevalence of OUD (including undiagnosed OUD) in each of the US state of Ohio's 88 counties for the years 2019 to 2021 among the population aged 12 years and older. Prevalence estimates were adjusted for misclassification in claims data and for variation in healthcare utilization among individuals with OUD. MeasurementsCounts and proportions of the population aged 12 and older with OUD at the US state and county level. FindingsOUD prevalence for Ohioans aged 12 years and older was relatively stable at 3.6% [95% uncertainty interval (UI) = 3.5%–3.8%] in 2019 and 3.7% (95% UI = 3.5%–3.9%) in 2021. County‐level prevalence estimates ranged from 0.7% to 14.2% in 2021. Southern counties generally had higher OUD prevalence than northern counties. The prevalence estimates were strongly correlated (Pearson's r = 0.88) with prevalence estimates for 19 Ohio counties from a previous study. Compared with the previous estimates, estimates from the current study tended to be lower for most communities but remained within the 95% credible intervals of previous estimates. ConclusionsThis approach for estimating opioid use disorder prevalence within United States communities by using Medicaid claims data and publicly available data is a robust alternative to methods relying on individual‐level data or multiple linked datasets. 

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5. Dynamic analysis of HCV infection and drug resistance using an age-structured multiscale model

   https://doi.org/10.1142/S1793524524500797  

   Wang, Xia; Ge, Qing; Li, Jie; Zhao, Hongyan; Rong, Libin (September 2024, International Journal of Biomathematics)

   Direct-acting antiviral agents (DAAs) are known to interfere with various intracellular stages of the hepatitis C virus (HCV) life cycle and have demonstrated efficacy in treating HCV infection. However, DAA monotherapy can lead to drug resistance due to mutations. This paper explores the impact of DAA therapy on HCV dynamics using a multiscale age-structured partial differential equation (PDE) model that incorporates intracellular viral RNA replication within infected cells and two strains of viruses representing a drug-sensitive strain and a drug-resistant mutant variant, respectively. We derived an equivalent ordinary differential equation (ODE) model from the PDE model to simplify mathematical analysis and numerical simulations. We studied the dynamics of the two virus strains before treatment and investigated the impact of mutations on the evolution kinetics of drug-sensitive and drug-resistant viruses, as well as the competition between the two strains during treatment. We also explored the role of DAAs in blocking HCV RNA replication and releasing new virus particles from cells. During treatment, mutations do not significantly influence the dynamics of various virus strains; however, they can generate low-level HCV that may be completely inhibited due to their poor fitness. The fitness of the mutant strain compared to the drug-sensitive strain determines which strain dominates the virus population. We also investigated the prevalence and drug resistance evolution of HCV variants during DAA treatment. 

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