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

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

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere0217559
JournalPloS one
Volume14
Issue number5
DOIs
StatePublished - May 1 2019

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HIV
synthesis
nationalities and ethnic groups
disease course
Disease Progression
Public Health Surveillance
Public health
risk groups
Health care
peers
communication (human)
Advisory Committees
committees
Screening
Ethnic Groups
decision making
Health Care Costs
Decision making
public health
Health

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Krebs, E., Enns, B., Wang, L., Zang, X., Panagiotoglou, D., Rio, C. D., ... Nosyk, B. (2019). Developing a dynamic HIV transmission model for 6 U.S. cities: An evidence synthesis. PloS one, 14(5), [e0217559]. https://doi.org/10.1371/journal.pone.0217559

Developing a dynamic HIV transmission model for 6 U.S. cities : An evidence synthesis. / Krebs, Emanuel; Enns, Benjamin; Wang, Linwei; Zang, Xiao; Panagiotoglou, Dimitra; Rio, Carlos Del; Dombrowski, Julia; Feaster, Daniel J; Golden, Matthew; Granich, Reuben; Marshall, Brandon; Mehta, Shruti H.; Metsch, Lisa; Schackman, Bruce R.; Strathdee, Steffanie A.; Nosyk, Bohdan.

In: PloS one, Vol. 14, No. 5, e0217559, 01.05.2019.

Research output: Contribution to journalArticle

Krebs, E, Enns, B, Wang, L, Zang, X, Panagiotoglou, D, Rio, CD, Dombrowski, J, Feaster, DJ, Golden, M, Granich, R, Marshall, B, Mehta, SH, Metsch, L, Schackman, BR, Strathdee, SA & Nosyk, B 2019, 'Developing a dynamic HIV transmission model for 6 U.S. cities: An evidence synthesis', PloS one, vol. 14, no. 5, e0217559. https://doi.org/10.1371/journal.pone.0217559
Krebs E, Enns B, Wang L, Zang X, Panagiotoglou D, Rio CD et al. Developing a dynamic HIV transmission model for 6 U.S. cities: An evidence synthesis. PloS one. 2019 May 1;14(5). e0217559. https://doi.org/10.1371/journal.pone.0217559
Krebs, Emanuel ; Enns, Benjamin ; Wang, Linwei ; Zang, Xiao ; Panagiotoglou, Dimitra ; Rio, Carlos Del ; Dombrowski, Julia ; Feaster, Daniel J ; Golden, Matthew ; Granich, Reuben ; Marshall, Brandon ; Mehta, Shruti H. ; Metsch, Lisa ; Schackman, Bruce R. ; Strathdee, Steffanie A. ; Nosyk, Bohdan. / Developing a dynamic HIV transmission model for 6 U.S. cities : An evidence synthesis. In: PloS one. 2019 ; Vol. 14, No. 5.
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AU - Panagiotoglou, Dimitra

AU - Rio, Carlos Del

AU - Dombrowski, Julia

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AU - Golden, Matthew

AU - Granich, Reuben

AU - Marshall, Brandon

AU - Mehta, Shruti H.

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N2 - 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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