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.

Fig 1. Dynamic compartmental HIV transmission model schematic diagram.

For each city, the adult population aged 15–64 was stratified into compartments on the basis of (1) gender (male or female), (2) race/ethnicity (black/African American, Hispanic/Latino, and non-Hispanic white/others), and (3) HIV risk behavior type (men who have sex with men (MSM), people who inject drugs (PWID), MWID, and heterosexual (HET)). MSM, MWID, and HET were further stratified into subgroups based on HIV sexual risk behavior intensity (high vs low), and PWID and MWID were categorized based on whether they were receiving opioid agonist treatment (OAT). Individuals within each of these 42 strata (MSM: 6 groups, MWID: 12 groups; PWID: 12 groups; HET: 12 groups) progress through the model according to the 19 health states illustrated above. Prior to HIV infection, HIV-negative individuals can be screened for HIV (screened in past 12 months), and screened MSM or MWID can take pre-exposure prophylaxis (PrEP). HIV transmission can occur through three modes: heterosexual contact, homosexual contact, and needle-sharing. We specified the pattern of sexual mixing between risk groups and race/ethnicity, where assortativity determines the proportion of sexual contacts within the same group, and we varied the level of assortativity across cities (28). Following HIV infection, individuals transition through acute infection (3 months), then are classified as infected but not diagnosed, diagnosed but ART-naïve, and on- or off-ART, and partitioned according to CD4 cell count (CD4 ≥ 500, 200–499, and <200). Health state transitions occur at monthly intervals, with transition to death a possibility from each of the health states depicted, with varying probabilities.

Fig 2. Model parameter category proportions.

The boxes are proportionally scaled to the corresponding model parameter category sizes. Model parameter category labels: Population estimates ‒ 1. Initial HIV-negative and HIV-infected population estimates; HIV transmission ‒ 2. Parameters used to calculate the probability of HIV transmission; Treatment and HIV disease progression ‒ 3. Screening, diagnosis, treatment and HIV disease progression; Prevention ‒ 4. HIV prevention programs, including syringe service programs (SSP), OAT, and PrEP; Costs ‒ 5. The costs of medical care for HIV-negative and HIV-infected individuals; and QALYs ‒ 6. Health utility weights for each stage of HIV disease progression. ART: Antiretroviral treatment; All Pop.: Census population estimates; QALYs: Quality-adjusted life-years; Mixing: Sexual mixing patterns.

Fig 3. Heterogeneity in selected parameter estimates by city, risk group, gender and race/ethnicity.

MSM: Men who have sex with men; PWID: People who inject drugs; HET: Heterosexuals; ART: Antiretroviral treatment; F: Female; M: Male.

Fig 4. Coverage of sterile syringes programs for people who inject drugs.