Combined estimation of disease progression and retention on antiretroviral therapy among treated individuals with HIV in the USA: a modelling study

Wang, Linwei; Krebs, Emanuel; Min, Jeong Eun; Mathews, W. Christopher; Nijhawan, Ank E.; Somboonwit, Charurut; Aberg, Judith A.; Moore, Richard D.; Gebo, Kelly A.; Nosyk, Bohdan; Edelstein, Howard; Rutstein, Richard M.; Baranoski, Amy S.; Allen, S; Boswell, Stephen L.; Mayer, Konstantin; Agwu, Allison L.; Beil, Robert; Felsen, Uriel R.; Urbina, Antonio; Korthuis, P. Todd; Akbar, Muhammad; Gaur, Aditya H.; Valenti, William M.; Hellinger, Fred J.; Fleishman, John A.; Mills, R. M.; Keruly, Jeanne C.; Voss, Cindy; Collins, Charles B.; Diaz-Reyes, Rebeca

doi:10.1016/s2352-3018(19)30148-1

Cited by 19 publications

(20 citation statements)

References 29 publications

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“…28 28 We estimated stratified, regional ART initiation and persistence rates through separate analysis of HIV Research Network data. 30 In the absence of comprehensive city-level HIV testing data, 28 we back-calculated HIV testing rates from high quality race/ethnicity-and risk behaviour-specific surveillance data for new diagnoses published by local public health departments. 29 We accounted for in-migration of PLHIV and overall population growth, also stratified by race/ethnicity.…”

Section: Model Descriptionmentioning

confidence: 99%

The Potential Epidemiological Impact of Coronavirus Disease 2019 (COVID-19) on the Human Immunodeficiency Virus (HIV) Epidemic and the Cost-effectiveness of Linked, Opt-out HIV Testing: A Modeling Study in 6 US Cities

Zang

Krebs

Chen

et al. 2020

Clinical Infectious Diseases

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Background Widespread viral and serological testing for SARS-CoV-2 may present a unique opportunity to also test for HIV infection. We estimated the potential impact of adding linked, opt-out HIV testing alongside SARS-CoV-2 testing on HIV incidence and the cost-effectiveness of this strategy in six US cities. Methods Using a previously-calibrated dynamic HIV transmission model, we constructed three sets of scenarios for each city: (1) sustained current levels of HIV-related treatment and prevention services (status quo); (2) temporary disruptions in health services and changes in sexual and injection risk behaviours at discrete levels between 0%-50%; and (3) linked HIV and SARS-CoV-2 testing offered to 10%-90% of the adult population in addition to scenario (2). We estimated cumulative HIV infections between 2020-2025 and incremental cost-effectiveness ratios of linked HIV testing over 20 years. Results In the absence of linked, opt-out HIV testing, we estimated a total of 16.5% decrease in HIV infections between 2020-2025 in the best-case scenario (50% reduction in risk behaviours and no service disruptions), and 9.0% increase in the worst-case scenario (no behavioural change and 50% reduction in service access). We estimated that HIV testing (offered at 10%-90% levels) could avert a total of 576-7,225 (1.6%-17.2%) new infections. The intervention would require an initial investment of $20.6M-$220.7M across cities; however, the intervention would ultimately result in savings in health care costs in each city. Conclusions A campaign in which HIV testing is linked with SARS-CoV-2 testing could substantially reduce HIV incidence and reduce direct and indirect health care costs attributable to HIV.

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Section: Model Descriptionmentioning

confidence: 99%

The Potential Epidemiological Impact of Coronavirus Disease 2019 (COVID-19) on the Human Immunodeficiency Virus (HIV) Epidemic and the Cost-effectiveness of Linked, Opt-out HIV Testing: A Modeling Study in 6 US Cities

Zang

Krebs

Chen

et al. 2020

Clinical Infectious Diseases

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Section: Model Descriptionmentioning

confidence: 99%

The Potential Epidemiological Impact of COVID-19 on the HIV/AIDS Epidemic and the Cost-Effectiveness of Linked, Opt-Out HIV Testing: A Modeling Study in Six US Cities

et al. 2020

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“…However, the limited evidence available to determine the proportion of HIV-infected individuals in the acute stage of HIV disease progression was of low-quality (Table 4), and these parameters (n = 84) represented half of all parameters common across cities (Table 1). Approximately one-quarter of the city-specific parameters that determined the probabilities of mortality (n = 372) from each health state were of best- or moderate-quality, including primary analyses of data from The HIV Research Network (HIVRN) that were used to derive mortality rates for PLHIV receiving ART [107, 111].…”

Section: Resultsmentioning

confidence: 99%

“…Screening, diagnosis, treatment and HIV disease progression parameters represented 18% (n = 312) of all model parameters and were derived from 5 peer-reviewed publications [71, 72, 111, 121–123] and 6 public health and surveillance reports [86, 88, 90, 96–98] (Table 2) and from primary analyses using 6 data sets [42, 107, 109, 110, 124, 125] (Table 3). HIV testing rates (n = 42) were derived from primary analyses of sample data from NHBS [42, 110], the US Centers for Disease Control and Prevention’s (CDC) Behavioral Risk Factor Surveillance System [109], and the New York City Community Health Survey [125].…”

Section: Resultsmentioning

confidence: 99%

Developing a dynamic HIV transmission model for 6 U.S. cities: An evidence synthesis

et al. 2019

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Background Dynamic HIV transmission models can provide evidence-based guidance on optimal combination implementation strategies to treat and prevent HIV/AIDS. However, these models can be extremely data intensive, and the availability of good-quality data characterizing regional microepidemics varies substantially within and across countries. We aim to provide a comprehensive and transparent description of an evidence synthesis process and reporting framework employed to populate and calibrate a dynamic, compartmental HIV transmission model for six US cities. Methods We executed a mixed-method evidence synthesis strategy to populate model parameters in six categories: (i) initial HIV-negative and HIV-infected populations; (ii) parameters used to calculate the probability of HIV transmission; (iii) screening, diagnosis, treatment and HIV disease progression; (iv) HIV prevention programs; (v) the costs of medical care; and (vi) health utility weights for each stage of HIV disease progression. We identified parameters that required city-specific data and stratification by gender, risk group and race/ethnicity a priori and sought out databases for primary analysis to augment our evidence synthesis. We ranked the quality of each parameter using context- and domain-specific criteria and verified sources and assumptions with our scientific advisory committee. Findings To inform the 1,667 parameters needed to populate our model, we synthesized evidence from 59 peer-reviewed publications and 24 public health and surveillance reports and executed primary analyses using 11 data sets. Of these 1,667 parameters, 1,517 (91%) were city-specific and 150 (9%) were common for all cities. Notably, 1,074 (64%), 201 (12%) and 312 (19%) parameters corresponded to categories (i), (ii) and (iii), respectively. Parameters ranked as best- to moderate-quality evidence comprised 39% of the common parameters and ranged from 56%-60% across cities for the city-specific parameters. We identified variation in parameter values across cities as well as within cities across risk and race/ethnic groups. Conclusions Better integration of modelling in decision making can be achieved by systematically reporting on the evidence synthesis process that is used to populate models, and by explicitly assessing the quality of data entered into the model. The effective communication of this process can help prioritize data collection of the most informative components of local HIV prevention and care services in order to reduce decision uncertainty and strengthen model conclusions.

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Combined estimation of disease progression and retention on antiretroviral therapy among treated individuals with HIV in the USA: a modelling study

Cited by 19 publications

References 29 publications

The Potential Epidemiological Impact of Coronavirus Disease 2019 (COVID-19) on the Human Immunodeficiency Virus (HIV) Epidemic and the Cost-effectiveness of Linked, Opt-out HIV Testing: A Modeling Study in 6 US Cities

The Potential Epidemiological Impact of Coronavirus Disease 2019 (COVID-19) on the Human Immunodeficiency Virus (HIV) Epidemic and the Cost-effectiveness of Linked, Opt-out HIV Testing: A Modeling Study in 6 US Cities

The Potential Epidemiological Impact of COVID-19 on the HIV/AIDS Epidemic and the Cost-Effectiveness of Linked, Opt-Out HIV Testing: A Modeling Study in Six US Cities

Developing a dynamic HIV transmission model for 6 U.S. cities: An evidence synthesis

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