Derivation of population mixing patterns from virus sequence data and their impacts on the modelling of HIV epidemics in MSM

Transcription

1 Derivation of population mixing patterns from virus sequence data and their impacts on the modelling of HIV epidemics in MSM Ngai Sze WONG 1 ; Tsz Ho KWAN 2 ; Kenny CW CHAN 3 ; Owen TY TSANG 4 ; Man Po LEE 5 ; Denise PC CHAN 1 ; Sabrina WC TO 6 ; Wing Cheong YAM 6 ; Shui Shan LEE Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong 2 Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong 3 Special Preventive Programme, Department of Health, Hong Kong Special Administrative Region Government 4 Department of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong 5 Department of Medicine, Queen Elizabeth Hospital, Kowloon, Hong Kong 6 Department of Microbiology, The University of Hong Kong, Hong Kong

2 The authors have no conflicts of interests. The opinions and assertions contained herein are private views of the authors and do not necessarily reflect those of the Centre for Health Protection, Hong Kong Special Administrative Region Government Department of Health, or the other affiliating institutions. 2

3 Random distribution? infected susceptible 3

4 High vs low risk group e.g. sexual behavior infected susceptible 4 low risk high risk

5 infected susceptible 5 low risk high risk

6 Two-mode sexual affiliation network of MSM recruited via saunas and the Internet. 6 Leung KK, et al. A comparative analysis of behaviors and sexual affiliation networks among men who have sex with men in Hong Kong. Arch Sex Behav Oct;44(7):

7 Network diagrams of MSM in the study. (a) A 2-mode network of MSM (dark circle with serial number) and their venues (open square with label) for partner-sourcing, with each link representing a positive response of the MSM to the question on the respective venue; (b) A one-mode network created from data transformed from two-mode network after dichotomization and crossproducts determination. 7 Lee SS, et al. An exploratory study on the social and genotypic clustering of HIV infection in men having sex with men. AIDS Aug 24;23(13):

8 Phylogenetic tree of HIV positive MSM Internet user; Internet-centred social cluster; sauna user; Sauna-centred social cluster; 8 Lee SS, et al. An exploratory study on the social and genotypic clustering of HIV infection in men having sex with men. AIDS Aug 24;23(13):

9 Objectives Show the role of population mixing for modelling HIV epidemics Suggest applying results of HIV molecular epidemiology for population mixing configuration 9

10 HIV epidemiology in Hong Kong By 2016, the cumulative no. of reported cases: Heterosexual: 2952 cases (35%) MSM: 3599 (43%) IDU: 351 (4%) Others, including Undetermined: 1508 (18%) Total: 8410 In 2016, 441 out of 692 new diagnoses were MSM (64%) Source: 10

11 Global Hong Kong zidovudine monotherapy HIV prevalence in MSM estimated by behavioral studies 1 st HIV reported 1 st AIDS reported dual nucleoside analogue therapy Adopted CDC guidelines with modification HAART PRiSM: 4.05% (n=859) PRiSM: 4.31% (n=843) PRiSM: Venue: 4.08% (n=816); Internet: 3.3% (n=180) HARiS: 5.85% (n=564) PRiSM: 6.54% (n=3028) CDC defined AIDS 1 st HIV isolate serological tests widely available CDC: guidelines for ART Tx initiation criteria: CD4 <=200 WHO: CD4<=350 WHO: Treat All WHO: CD4<= CDC Centers for Disease Control and Prevention; FSW female sex workers; MSM men who have sex with men; WHO World Health Organization

12 Methods Model development Deterministic compartmental model developed in R Study population: men who have sex with men (MSM, estimated ~50,000 persons in 2017) Timeline: Data description Clinical data of HIV MSM patients attending 3 major HIV specialist clinics in Hong Kong in (96% of reported MSM cases) HIV-1 sequences collected in Behavioral data for categorizing high (>8 sex partners/yr) and low risk groups (MSM community) HIV surveillance reports (as observed data for validation) HIV-infected MSM delineation Neighbor-joining method 12

13 Phylogenetic analysis and results 1135 HIV-1 genotype resistance testing sequences (protease and partial reverse transcriptase of pol gene) MUSCLE alignment Bootstrap method, 1000 replications Neighbor-joining method Identify clusters (n=143): bootstrap value 90 13

14 Presentation of clusters and nodes in Netdraw large cluster subgroups, 3 small cluster subgroups, 4 dyads or very small cluster subgroups, 1 subgroup for all isolates = 19 subgroups

15 Model structure susceptible New MSM X h 15 X l infection Non-local Key X h high risk susceptible, X l low risk susceptible, UN undiagnosed, Dx diagnosed, LF loss to follow-up, Tx treatment, NSVL non-suppressed viral load, SVL suppressed viral load CD4 level: 1: >500/μL 2: /μL 3: /μL 4: <=200/μL UN Acute UN1 UN2 UN3 UN4 UN AIDS Diagnosis rate Dx1 Dx2 Dx3 Dx4 Dx AIDS Pre-Tx LF Tx NSVL Tx SVL Tx rate Tx LF Undiagnosed After diagnosis, pre-treatment After treatment initiation

16 Modelling results (19 sub-models) 16

17 Random distribution? infected susceptible 17

18 Comparison between random mixing in one model and 19 sub-models One model 19 sub-models observed data 18

19 Summary Despite the reporting of the continuous growth of HIV epidemics among MSM in Hong Kong and in many other places, the moderate non-exponential rise of the epidemic curve might be explained by the segregation of MSM population Population mixing is a key parameter determining the fit of model Phylogenetic analysis on the available HIV-1 sequences collected is an approach for HIV-infected population delineation 19

20 Acknowledgement Funding source: The study is supported by Health and Medical Research Fund (project reference no.: CU-16-C14) of Food and Health Bureau, and the Council for the AIDS Trust Fund (MSS229R), Hong Kong Special Administrative Region Government. (by alphabetical order) Dr. Kenny CHAN, Special Preventive Programme, Department of Health, Hong Kong Special Administrative Region Government Dr. Man Po LEE, Queen Elizabeth Hospital, Hong Kong Dr. Owen TY TSANG, Princess Margaret Hospital, Hong Kong Dr. Sabrina TO, The University of Hong Kong Dr. Ka Hing WONG, Special Preventive Programme, Department of Health, Hong Kong Special Administrative Region Government Dr. W. C. YAM, The University of Hong Kong Li Ka Shing Institute of Health Sciences and Stanley Ho Centre for Emerging Infectious Diseases of The Chinese University of Hong Kong are acknowledged for providing technical support in conducting the research. 20