Survival analysis of baseline and longitudinal risk factors for IIT in a cohort of 191,162 patients initiating antiretroviral therapy in South Africa from Jan 2017-March 2022.

Survival analysis of baseline and longitudinal risk factors for IIT in a cohort of 191,162 patients initiating antiretroviral therapy in South Africa from Jan 2017-March 2022.</p

Log binomial regression results risk factors for LTFU in a cohort of 191,162 patients initiating antiretroviral therapy in South Africa from Jan 2017-March 2022.

Log binomial regression results risk factors for LTFU in a cohort of 191,162 patients initiating antiretroviral therapy in South Africa from Jan 2017-March 2022.</p

Visit archetypes based on longitudinal patterns of ART visit attendance as described in Table 1.

Archetypes are mutually exclusive, completely exhaustive and defined by the historical pattern of visit attendance of interruptions in treatment (red), late attendance (orange) and visits attended on time (green). Analysis is focused on how these historical patterns are able to predict attendance at the next visit in the time series (grey).</p

Model performance in analysis of prediction of interruption in treatment in a cohort of 191,162 patients initiating antiretroviral therapy in South Africa from Jan 2017-March 2022.

We compared the performance of (A) the top 13 predictors from the validated CatBoost model, (B) the addition of previous visit archetypes to the validated CatBoost model and (C) a model using only previous visit archetypes as predictor.</p

Longitudinal ART clinic visit attendance in the first two years of antiretroviral therapy in a cohort of 191,162 patients initiating antiretroviral therapy in South Africa from Jan 2017-March 2022.

Purple dots represent the proportion of patients that do not return to treatment after treatment initiation. Green lines represent the monthly proportion of visits that are attended more than 28 days after a scheduled appointment (ITT) and yellow lines represent the proportion of monthly visits attended by patients within 28 days of a scheduled visit date after previously experiencing an ITT (Return after ITT).</p

Visits archetype definitions based on longitudinal patterns of ART visit attendance.

Visits archetype definitions based on longitudinal patterns of ART visit attendance.</p

Adjusted survival analysis of baseline and longitudinal risk factors for interruption in treatment in a cohort of 191,162 patients initiating antiretroviral therapy in South Africa from Jan 2017-March 2022.

Results from semi-parametric extension of the cox model are summarised as exponentiated hazards ratios (box) and 95% confidence intervals (whiskers). Colours denote reference (black) and comparator (grey) groups for categorical variables and size denotes number of observations in each variable group.</p

Characteristics of current visits archetypes based on longitudinal patterns of ART visit attendance in a cohort of 191,162 patients initiating antiretroviral therapy in South Africa from Jan 2017-March 2022.

Characteristics of current visits archetypes based on longitudinal patterns of ART visit attendance in a cohort of 191,162 patients initiating antiretroviral therapy in South Africa from Jan 2017-March 2022.</p

CatBoost predicted probability of IIT for all visits summarised by previous visits type in a cohort of 191,162 patients initiating antiretroviral therapy in South Africa from Jan 2017-March 2022.

Hazard ratios for each visit archetype from adjusted survival model for all patients (Table 3) are labelled above.</p

Outcomes of laboratory-confirmed SARS-CoV-2 infection during resurgence driven by Omicron lineages BA.4 and BA.5 compared with previous waves in the Western Cape Province, South Africa.

OBJECTIVE: We aimed to compare clinical severity of Omicron BA.4/BA.5 infection with BA.1 and earlier variant infections among laboratory-confirmed SARS-CoV-2 cases in the Western Cape, South Africa, using timing of infection to infer the lineage/variant causing infection. METHODS: We included public sector patients aged ≥20 years with laboratory-confirmed COVID-19 between 1-21 May 2022 (BA.4/BA.5 wave) and equivalent prior wave periods. We compared the risk between waves of (i) death and (ii) severe hospitalization/death (all within 21 days of diagnosis) using Cox regression adjusted for demographics, comorbidities, admission pressure, vaccination and prior infection. RESULTS: Among 3,793 patients from the BA.4/BA.5 wave and 190,836 patients from previous waves the risk of severe hospitalization/death was similar in the BA.4/BA.5 and BA.1 waves (adjusted hazard ratio (aHR) 1.12; 95% confidence interval (CI) 0.93; 1.34). Both Omicron waves had lower risk of severe outcomes than previous waves. Prior infection (aHR 0.29, 95% CI 0.24; 0.36) and vaccination (aHR 0.17; 95% CI 0.07; 0.40 for at least 3 doses vs. no vaccine) were protective. CONCLUSION: Disease severity was similar amongst diagnosed COVID-19 cases in the BA.4/BA.5 and BA.1 periods in the context of growing immunity against SARS-CoV-2 due to prior infection and vaccination, both of which were strongly protective