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

Abstract

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.

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

“…This study features several limitations in the structure of the model and the underlying evidence base that we have described in prior publications. 23,28,29,31 In addition, specific to this analysis, the potential duration of the COVID-19 pandemic is unknown, and there is limited evidence of its impact on the PLHIV population and level of HIV health service interruption in the US. If the level of disruption in services is higher and lasts longer and/or the uptake of HIV testing does not achieve the ambitious levels we have proposed, the estimated additional number of HIV cases that could occur would be even greater.…”

“…A detailed account of the underlying evidence synthesis, calibration, and validation are available in prior publications. 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. 28…”

Hopf bifurcation analysis of nonlinear HIV infection model and the effect of delayed immune response with drug therapies

“…It is interesting to know the duration of the asymptotic period for subsequent consequent clinical evaluations. Dynamic HIV transmission models can provide evidence-based guidance on optimal combination implementation strategies to treat and prevent HIV/AIDS [33].…”

Dissemination Science to Advance the Use of Simulation Modeling: Our Obligation Moving Forward

“…Our team’s experience in developing, translating, and disseminating the results of a simulation model for policy decisions in human immunodeficiency virus (HIV) prevention and treatment has been instructive (and humbling). In an effort to set a new standard for transparency, we published separate articles on the evidence synthesis, 3 calibration and validation, 4 baseline projections, 5 cost-effectiveness of individual interventions, 6 and, finally, our cost-effectiveness analysis of different strategies to combat HIV/AIDS in disparate geographic regions. 7 We followed these efforts with a closer examination of the uncertainty in our findings and the value of collecting more information to reduce this uncertainty.…”