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

Date Published: May 30, 2019

Publisher: Public Library of Science

Author(s): Emanuel Krebs, Benjamin Enns, Linwei Wang, Xiao Zang, Dimitra Panagiotoglou, Carlos Del Rio, Julia Dombrowski, Daniel J. Feaster, Matthew Golden, Reuben Granich, Brandon Marshall, Shruti H. Mehta, Lisa Metsch, Bruce R. Schackman, Steffanie A. Strathdee, Bohdan Nosyk, Lauren Cipriano.


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.

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.

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.

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.

Partial Text

In the United States, more than 1.1 million people were estimated to be living with HIV in 2015, including 162,500 (15%) people who had not been diagnosed [1]. Although the number of people living with HIV is increasing and access to antiretroviral medications is extending life expectancy [2], current political uncertainty related to health financing is straining resources and challenging public health departments to use available funding efficiently [3]. Further complicating these decisions is the fact that HIV epidemics tend to be heterogeneous across geographic regions [4–6]. In the United States, the majority of people living with HIV/AIDS (PLHIV) reside in large urban centers that have unique underlying epidemiological and structural features [7]. This heterogeneity across regional microepidemics necessitates prioritizing resources according to the greatest public health benefit, accounting for the local epidemiological and structural context [6, 8, 9].

We identified 1,667 parameters needed to populate our dynamic, compartmental HIV transmission model (Table 1). Of these, 1,517 (91%) were unique to each city and the other 150 (9%) were common for all cities. The proportion of model parameters that composed each of the six model parameter categories varied extensively (Fig 2).

We have provided a comprehensive description of an extensive evidence synthesis process that is required to populate a dynamic, compartmental HIV transmission model for six US cities. We identified differences across cities in the quality and representativeness of evidence available to inform our model. However, we identified consistency in the lack of availability of best-quality local administrative data that are critical to assess health system performance, particularly in relation to population-level rates of HIV testing and ART engagement. Nonetheless, our findings, which used the best-available evidence, highlight fundamental differences across settings related to rates of health system engagement and access to HIV prevention programs. The modeling of targeted, locally-oriented combination implementation strategies is necessary to determine how scarce resources should be allocated to interventions that can provide the greatest value for money in a given microepidemic. Our findings emphasize the need for increased public health efforts to measure and monitor the most informative components of local HIV prevention and care services, including the delivery, uptake and effect of localized HIV programs.




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