Late presentation to HIV care despite good access to health services: current epidemiological trends and how to do better.

In 2014, there were 36.9 million people worldwide living with human immunodeficiency virus (PLWH), of whom 17.1 million did not know they were infected. Whilst the number of new human immunodeficiency virus (HIV) infections has declined globally since 2000, there are still regions where new infection rates are rising, and diagnosing HIV early in the course of infection remains a challenge. Late presentation to care in HIV refers to individuals newly presenting for HIV care with a CD4 count below 350 cells/µl or with an acquired immune deficiency syndrome (AIDS)-defining event. Late presentation is associated with increased patient morbidity and mortality, healthcare costs and risk of onward transmission by individuals unaware of their status. Further, late presentation limits the effectiveness of all subsequent steps in the cascade of HIV care. Recent figures from 34 countries in Europe show that late presentation occurs in 38.3% to 49.8% of patients newly presenting for care, depending on region. In Switzerland, data from patients enrolled in the Swiss HIV Cohort Study put the rate of late presentation at 49.8% and show that patients outside established HIV risk groups are most likely to be late presenters. Provider-initiated testing needs to be improved to reach these groups, which include heterosexual men and women and older patients. The aim of this review is to describe the scale and implications of late presentation using cohort data from Switzerland and elsewhere in Europe, and to highlight initiatives to improve early HIV diagnosis. The importance of recognising indicator conditions and the potential for missed opportunities for HIV testing is illustrated in three clinical case studies

Triggers of change in sexual behavior among people with HIV: The Swiss U = U statement and Covid-19 compared.

We assessed changes in sexual behaviour among people with HIV (PWH) over 20 years. Condom use with stable partners steadily declined from over 90% to 29% since the Swiss U = U statement with similar trajectories between men who have sex with men (MSM) and heterosexuals. Occasional partnership remained higher among MSM compared to heterosexuals even during COVID-19 social distancing

Triggers of change in sexual behavior among people with HIV: The Swiss U = U statement and Covid-19 compared

We assessed changes in sexual behaviour among people with HIV (PWH) over 20 years. Condom use with stable partners steadily declined from over 90% to 29% since the Swiss U = U statement with similar trajectories between men who have sex with men (MSM) and heterosexuals. Occasional partnership remained higher among MSM compared to heterosexuals even during COVID-19 social distancing

Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men

Machine learning is increasingly introduced into medical fields, yet there is limited evidence for its benefit over more commonly used statistical methods in epidemiological studies. We introduce an unsupervised machine learning framework for longitudinal features and evaluate it using sexual behaviour data from the last 20 years from over 3'700 participants in the Swiss HIV Cohort Study (SHCS). We use hierarchical clustering to find subgroups of men who have sex with men in the SHCS with similar sexual behaviour up to May 2017, and apply regression to test whether these clusters enhance predictions of sexual behaviour or sexually transmitted diseases (STIs) after May 2017 beyond what can be predicted with conventional parameters. We find that behavioural clusters enhance model performance according to likelihood ratio test, Akaike information criterion and area under the receiver operator characteristic curve for all outcomes studied, and according to Bayesian information criterion for five out of ten outcomes, with particularly good performance for predicting future sexual behaviour and recurrent STIs. We thus assess a methodology that can be used as an alternative means for creating exposure categories from longitudinal data in epidemiological models, and can contribute to the understanding of time-varying risk factors

Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men.

Machine learning is increasingly introduced into medical fields, yet there is limited evidence for its benefit over more commonly used statistical methods in epidemiological studies. We introduce an unsupervised machine learning framework for longitudinal features and evaluate it using sexual behaviour data from the last 20 years from over 3'700 participants in the Swiss HIV Cohort Study (SHCS). We use hierarchical clustering to find subgroups of men who have sex with men in the SHCS with similar sexual behaviour up to May 2017, and apply regression to test whether these clusters enhance predictions of sexual behaviour or sexually transmitted diseases (STIs) after May 2017 beyond what can be predicted with conventional parameters. We find that behavioural clusters enhance model performance according to likelihood ratio test, Akaike information criterion and area under the receiver operator characteristic curve for all outcomes studied, and according to Bayesian information criterion for five out of ten outcomes, with particularly good performance for predicting future sexual behaviour and recurrent STIs. We thus assess a methodology that can be used as an alternative means for creating exposure categories from longitudinal data in epidemiological models, and can contribute to the understanding of time-varying risk factors

Anticholinergic and Sedative Medications Are Associated With Neurocognitive Performance of Well Treated People With Human Immunodeficiency Virus.

Background We previously showed that anticholinergic (ACH) medications contribute to self-reported neurocognitive impairment (NCI) in elderly people with human immunodeficiency virus (PWH). The current cross-sectional study further evaluated the effect of ACH and sedative drugs on neurocognitive function in PWH who underwent comprehensive neuropsychological evaluation. Methods A medication review was performed in PWH enrolled in the prospective Neurocognitive Assessment in Metabolic and Aging Cohort within the Swiss HIV Cohort Study. Neurocognitive functions were analyzed in 5 domains (motor skills, speed of information, attention/working memory, executive functions, and verbal learning memory). The effect of ACH and sedative medications on neurocognitive functioning was evaluated using linear regression models for the continuous (mean z-score) outcome and multivariable logistic regression models for the binary (presence/absence) outcome. Results A total of 963 PWH (80% male, 92% Caucasian, 96% virologically suppressed, median age 52) were included. Fourteen percent of participants were prescribed ≥1 ACH medication and 9% were prescribed ≥1 sedative medication. Overall, 40% of participants had NCI. Sedative medication use was associated with impaired attention/verbal learning and ACH medication use with motor skills deficits both in the continuous (mean z-score difference -0.26 to -0.14, P < .001 and P = .06) and binary (odds ratio [OR], ≥1.67; P < .05) models. Their combined use was associated with deficits in overall neurocognitive functions in both models (mean z-score difference -0.12, P = .002 and OR = 1.54, P = .03). These associations were unchanged in a subgroup analysis of participants without depression (n = 824). Conclusions Anticholinergic and sedative medications contribute to NCI. Clinicians need to consider these drugs when assessing NCI in PWH

Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men

Machine learning is increasingly introduced into medical fields, yet there is limited evidence for its benefit over more commonly used statistical methods in epidemiological studies. We introduce an unsupervised machine learning framework for longitudinal features and evaluate it using sexual behaviour data from the last 20 years from over 3'700 participants in the Swiss HIV Cohort Study (SHCS). We use hierarchical clustering to find subgroups of men who have sex with men in the SHCS with similar sexual behaviour up to May 2017, and apply regression to test whether these clusters enhance predictions of sexual behaviour or sexually transmitted diseases (STIs) after May 2017 beyond what can be predicted with conventional parameters. We find that behavioural clusters enhance model performance according to likelihood ratio test, Akaike information criterion and area under the receiver operator characteristic curve for all outcomes studied, and according to Bayesian information criterion for five out of ten outcomes, with particularly good performance for predicting future sexual behaviour and recurrent STIs. We thus assess a methodology that can be used as an alternative means for creating exposure categories from longitudinal data in epidemiological models, and can contribute to the understanding of time-varying risk factors

Virological outcome and management of persistent low-level viraemia in HIV-1-infected patients: 11 years of the Swiss HIV Cohort Study

BACKGROUND Management of persistent low-level viraemia (pLLV) in patients on combined antiretroviral therapy (cART) with previously undetectable HIV viral loads (VLs) is challenging. We examined virological outcome and management among patients enrolled in the Swiss HIV Cohort Study (SHCS). METHODS In this retrospective study (2000-2011), pLLV was defined as a VL of 21-400 copies/mL on ≥3 consecutive plasma samples with ≥8 weeks between first and last analyses, in patients undetectable for ≥24 weeks on cART. Control patients had ≥3 consecutive undetectable VLs over ≥32 weeks. Virological failure (VF), analysed in the pLLV patient group, was defined as a VL>400 copies/mL. RESULTS Among 9972 patients, 179 had pLLV and 5389 were controls. Compared to controls, pLLV patients were more often on unboosted PI-based (adjusted odds ratio, aOR, [95%CI] 3.2 [1.8-5.9]) and NRTI-only combinations (aOR 2.1 [1.1-4.2]) than on NNRTI and boosted PI-based regimens. At 48 weeks, 102/155 pLLV patients (66%) still had pLLV, 19/155 (12%) developed VF, and 34/155 (22%) had undetectable VLs. Predictors of VF were previous VF (aOR 35 [3.8-315]), unboosted PI-based (aOR 12.8 [1.7-96]) or NRTI-only combinations (aOR 115 [6.8-1952]), and VLs>200 during pLLV (aOR 3.7 [1.1-12]). No VF occurred in patients with persistent very LLV (pVLLV, 21-49 copies/mL; N=26). At 48 weeks, 29/39 patients (74%) who changed cART had undetectable VLs, compared to 19/74 (26%) without change (P<0.001). CONCLUSIONS Among patients with pLLV, VF was predicted by previous VF, cART regimen and VL ≥200. Most patients who changed cART had undetectable VLs 48 weeks later. These findings support cART modification for pLLV >200 copies/ml