METHODS AND FINDINGS: Genetic instruments to proxy 12 risk factors were constructed by identifying single nucleotide polymorphisms (SNPs) that were robustly (P < 5 × 10-8) and independently associated with each respective risk factor in previously reported genome-wide association studies. These risk factors included genetic liability to 3 factors (endometriosis, polycystic ovary syndrome, type 2 diabetes) scaled to reflect a 50% higher odds liability to disease. We obtained summary statistics for the association of these SNPs with risk of overall and histotype-specific invasive epithelial ovarian cancer (22,406 cases; 40,941 controls) and low malignant potential tumours (3,103 cases; 40,941 controls) from the Ovarian Cancer Association Consortium (OCAC). The OCAC dataset comprises 63 genotyping project/case-control sets with participants of European ancestry recruited from 14 countries (US, Australia, Belarus, Germany, Belgium, Denmark, Finland, Norway, Canada, Poland, UK, Spain, Netherlands, and Sweden). SNPs were combined into multi-allelic inverse-variance-weighted fixed or random effects models to generate effect estimates and 95% confidence intervals (CIs). Three complementary sensitivity analyses were performed to examine violations of MR assumptions: MR-Egger regression and weighted median and mode estimators. A Bonferroni-corrected P value threshold was used to establish strong evidence (P < 0.0042) and suggestive evidence (0.0042 < P < 0.05) for associations. In MR analyses, there was strong or suggestive evidence that 2 of the 12 risk factors were associated with invasive epithelial ovarian cancer and 8 of the 12 were associated with 1 or more invasive epithelial ovarian cancer histotypes. There was strong evidence that genetic liability to endometriosis was associated with an increased risk of invasive epithelial ovarian cancer (odds ratio [OR] per 50% higher odds liability: 1.10, 95% CI 1.06-1.15; P = 6.94 × 10-7) and suggestive evidence that lifetime smoking exposure was associated with an increased risk of invasive epithelial ovarian cancer (OR per unit increase in smoking score: 1.36, 95% CI 1.04-1.78; P = 0.02). In analyses examining histotypes and low malignant potential tumours, the strongest associations found were between height and clear cell carcinoma (OR per SD increase: 1.36, 95% CI 1.15-1.61; P = 0.0003); age at natural menopause and endometrioid carcinoma (OR per year later onset: 1.09, 95% CI 1.02-1.16; P = 0.007); and genetic liability to polycystic ovary syndrome and endometrioid carcinoma (OR per 50% higher odds liability: 0.89, 95% CI 0.82-0.96; P = 0.002). There was little evidence for an association of genetic liability to type 2 diabetes, parity, or circulating levels of 25-hydroxyvitamin D and sex hormone binding globulin with ovarian cancer or its subtypes. The primary limitations of this analysis include the modest statistical power for analyses of risk factors in relation to some less common ovarian cancer histotypes (low grade serous, mucinous, and clear cell carcinomas), the inability to directly examine the association of some ovarian cancer risk factors that did not have robust genetic variants available to serve as proxies (e.g., oral contraceptive use, hormone replacement therapy), and the assumption of linear relationships between risk factors and ovarian cancer risk.

METHODS: This cross-sectional study recruited adult PWH during routine follow-up at five HIV clinical sites in the Asia-Pacific region. Participants were screened for depression using Patient Health Questionnaire-9 and SU using Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). Quality of life (QoL) was assessed with WHOQOL-HIV BREF and functional ability with World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0). Factors associated with mean QoL and disability scores were analysed using linear regression.

METHODS: Adults > 18 years of age on second-line ART for ≥ 6 months were eligible. Cross-sectional data on HIV viral load (VL) and genotypic resistance testing were collected or testing was conducted between July 2015 and May 2017 at 12 Asia-Pacific sites. Virological failure (VF) was defined as VL > 1000 copies/mL with a second VL > 1000 copies/mL within 3-6 months. FASTA files were submitted to Stanford University HIV Drug Resistance Database and RAMs were compared against the IAS-USA 2019 mutations list. VF risk factors were analysed using logistic regression.

RESULTS: Of 1378 patients, 74% were male and 70% acquired HIV through heterosexual exposure. At second-line switch, median [interquartile range (IQR)] age was 37 (32-42) years and median (IQR) CD4 count was 103 (43.5-229.5) cells/µL; 93% received regimens with boosted protease inhibitors (PIs). Median duration on second line was 3 years. Among 101 patients (7%) with VF, CD4 count > 200 cells/µL at switch [odds ratio (OR) = 0.36, 95% confidence interval (CI): 0.17-0.77 vs. CD4 ≤ 50) and HIV exposure through male-male sex (OR = 0.32, 95% CI: 0.17-0.64 vs. heterosexual) or injecting drug use (OR = 0.24, 95% CI: 0.12-0.49) were associated with reduced VF. Of 41 (41%) patients with resistance data, 80% had at least one RAM to nonnucleoside reverse transcriptase inhibitors (NNRTIs), 63% to NRTIs, and 35% to PIs. Of those with PI RAMs, 71% had two or more.

METHODS: HIV+ patients from the Australian HIV Observational Database (AHOD) and the TREAT Asia HIV Observational Database (TAHOD) meeting specific criteria were included. In these analyses Asian and Caucasian status were defined by cohort. Factors associated with a low CD4:CD8 ratio (cutoff <0.2) prior to ART commencement, and with achieving a normal CD4:CD8 ratio (>1) at 12 and 24 months post ART commencement were assessed using logistic regression.

RESULTS: There were 591 patients from AHOD and 2,620 patients from TAHOD who met the inclusion criteria. TAHOD patients had a significantly (P<0.001) lower odds of having a baseline (prior to ART initiation) CD4:CD8 ratio greater than 0.2. After 12 months of ART, AHOD patients were more than twice as likely to achieve a normal CD4:CD8 ratio compared to TAHOD patients (15% versus 6%). However, after adjustment for confounding factors there was no significant difference between cohorts in the odds of achieving a CD4:CD8 ratio >1 (P=0.475).

METHODS: A descriptive, cross-sectional, online study, conducted between October-November 2020, assessed the impact of COVID-19 on HIV prevention and care among people living with HIV (PLHIV), key populations (KPs), and healthcare providers (HCPs). The study populations were recruited across ten Asian countries/territories, covering Hong Kong, India, Japan, Malaysia, Philippines, Singapore, Korea, Taiwan, Thailand, and Vietnam.

RESULTS: Across the region, 702 PLHIV, 551 KPs, and 145 HCPs were recruited. Both PLHIV and KPs reported decreased or had yet to visit hospitals/clinics (PLHIV: 35.9%; KPs: 57.5%), reduced HIV RNA viral load testing (21.9%; 47.3%), and interruptions in antiretroviral therapy (ART) (22.3%) or decreased/complete stop of HIV prevention medication consumption (40.9%). Travel constraints (40.6%), financial issues (28.9%), and not receiving prescription refills (26.9%) were common reasons for interrupted ART access, whereas reduced engagements in behaviours that could increase the risks of HIV acquisition and transmission (57.7%), travel constraints (41.8%), and less hospital/clinic visits (36.7%) underlie the disruptions in HIV preventive medications. Decreased visits from PLHIV/KPs and rescheduled appointments due to clinic closure were respectively reported by 50.7%-52.1% and 15.6%-17.0% of HCPs; 43.6%-61.9% observed decreased ART/preventive medication refills. Although 85.0% of HCPs adopted telemedicine to deliver HIV care services, 56.4%-64.1% of PLHIV/KPs were not using telehealth services.

METHODS: Patients from the TREAT Asia HIV Observational Database (TAHOD) and Australian HIV Observational Database (AHOD) receiving cART between 1999 and 2017 were included. Causes of death verification were based on review of the standardized Cause of Death (CoDe) form designed by the D:A:D group. Cohorts were grouped as AHOD (all high-income sites), TAHOD-high (high/upper-middle income countries) and TAHOD-low (lower-middle income countries). TAHOD sites were split into high/upper-middle income and lower-middle income country settings based on World Bank classifications. Competing risk regression was used to analyse factors associated with AIDS and non-AIDS-related mortality.