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ABSTRACT There are limited options to estimate the treatment effects of variables which are continuous and measured at multiple time points, particularly if the true dose–response curve should be estimated as closely as possible. However, these situations may be of relevance: in pharmacology, one may be interested in how outcomes of people living with—and treated for—HIV, such as viral failure, would vary for time‐varying interventions such as different drug concentration trajectories. A challenge for doing causal inference with continuous interventions is that the positivity assumption is typically violated. To address positivity violations, we develop projection functions, which reweigh and redefine the estimand of interest based on functions of the conditional support for the respective interventions. With these functions, we obtain the desired dose–response curve in areas of enough support, and otherwise a meaningful estimand that does not require the positivity assumption. We develop g$$ g $$‐computation type plug‐in estimators for this case. Those are contrasted with g‐computation estimators which are applied to continuous interventions without specifically addressing positivity violations, which we propose to be presented with diagnostics. The ideas are illustrated with longitudinal data from HIV positive children treated with an efavirenz‐based regimen as part of the CHAPAS‐3 trial, which enrolled children <13$$ <13 $$ years in Zambia/Uganda. Simulations show in which situations a standard g‐computation approach is appropriate, and in which it leads to bias and how the proposed weighted estimation approach then recovers the alternative estimand of interest. [ABSTRACT FROM AUTHOR]

Introduction: Older adolescents aged 15-19 years continue to have high rates of loss to follow up (LTFU), and high rates of virologic non-suppression (VNS) compared to younger adolescents and adults. Adolescent females are at risk of pregnancy, which puts those living with HIV at a dualvulnerability. Our study assessed the factors associated with VNS and LTFU in older adolescents (including pregnant females) who initiated antiretroviral therapy (ART) in South Africa. Methods: We included adolescents aged 15-19 years initiating ART between 2004 and 2019, with [greater than or equal to] one viral load (VL) measurement between 4 and 24.5 months, and [greater than or equal to] 6 months follow-up, from six South African cohorts of the International epidemiology Databases to Evaluate AIDS-Southern Africa (IeDEA-SA). We defined VNS as VL [greater than or equal to]400 copies/ml and LTFU as not being in care for [greater than or equal to]180 days from ART start and not known as transferred out of the clinic or dead in the first 24 months on ART We examined factors associated with VNS and LTFU using Fine&Gray competing risk models. Results: We included a totalof 2733 adolescents, 415 (15.2%) males, median (IQR) age at ART start of 18.6 (17.3, 19.4) years. Among females, 585/2318 (25.2%) were pregnant. Over the 24-month follow-up, 424 (15.5%) of alladolescents experienced VNS: range (11.1% pregnant females and 20.5% males). Over half of all adolescents were LTFU before any other event could occur. The hazard of VNS reduced with increasing age and CD4 count above 200 cells/[micro]l at ART initiation among all adolescents having adjusted for allmeasured patient characteristics [adjusted sub-distribution hazard ratio (aSHR) 19 vs. 15 years: 0.50 (95% CI: 0.36, 0.68), aSHR: >500 vs. =200 cells/[micro]l: 0.22 (95% CI: 0.16, 0.31)]. The effect of CD4 count persisted in pregnant females. Increasing age and CD4 count >200 cells/[micro]l were risk factors for LTFU among alladolescents. Conclusions: Older adolescents had a high risk of LTFU shortly after ART start and a low risk of VNS, especially those initiating treatment during pregnancy. Interventions addressing adherence and retention should be incorporated into adolescent-friendly services to prevent VNS and LTFU and endeavour to trace lost adolescents as soon as they are identified. Keywords: adolescents; antiretroviraltherapy; HIV; loss to follow up; pregnancy; virologic non-suppression, 1 | INTRODUCTION In 2019, about 1.7 (1.1-2.4) million adolescents aged 10-19 years and 3.4 million youth aged 15-24 years were living with HIV worldwide [1], with the majority living [...]

Objectives: Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience.Methods: To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends.Results: Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference.Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased.Conclusions: Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

The notion that an independent central bank reduces a country’s inflation is a controversial hypothesis. To date, it has not been possible to satisfactorily answer this question because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses. We develop a causal model that summarizes the economic process of inflation. Based on this causal model and recent data, we discuss and identify the assumptions under which the effect of central bank independence on inflation can be identified and estimated. Given these and alternative assumptions, we estimate this effect using modern doubly robust effect estimators, i.e., longitudinal targeted maximum likelihood estimators. The estimation procedure incorporates machine learning algorithms and is tailored to address the challenges associated with complex longitudinal macroeconomic data. We do not find strong support for the hypothesis that having an independent central bank for a long period of time necessarily lowers inflation. Simulation studies evaluate the sensitivity of the proposed methods in complex settings when certain assumptions are violated and highlight the importance of working with appropriate learning algorithms for estimation.

BackgroundWe compared Kaposi sarcoma (KS) risk in adults who started antiretroviral therapy (ART) across the Asia-Pacific, South Africa, Europe, Latin, and North America.MethodsWe included cohort data of human immunodeficiency virus (HIV)-positive adults who started ART after 1995 within the framework of 2 large collaborations of observational HIV cohorts. We present incidence rates and adjusted hazard ratios (aHRs).ResultsWe included 208140 patients from 57 countries. Over a period of 1066572 person-years, 2046 KS cases were diagnosed. KS incidence rates per 100000 person-years were 52 in the Asia-Pacific and ranged between 180 and 280 in the other regions. KS risk was 5 times higher in South African women (aHR, 4.56; 95% confidence intervals [CI], 2.73-7.62) than in their European counterparts, and 2 times higher in South African men (2.21; 1.34-3.63). In Europe, Latin, and North America KS risk was 6 times higher in men who have sex with men (aHR, 5.95; 95% CI, 5.09-6.96) than in women. Comparing patients with current CD4 cell counts ≥700 cells/µL with those whose counts were

Objectives: The objective of this study is to assess the outcomes of children, adolescents and young adults with HIV reported as lost to follow‐up, correct mortality estimates for children, adolescents and young adults with HIV for unascertained outcomes in those loss to follow‐up (LTFU) based on tracing and linkage data separately using data from the International epidemiology Databases to Evaluate AIDS in Southern Africa. Methods: We included data from two different populations of children, adolescents and young adults with HIV; (1) clinical data from children, adolescents and young adults with HIV aged ≤24 years from Lesotho, Malawi, Mozambique, Zambia and Zimbabwe; (2) clinical data from children, adolescents and young adults with HIV aged ≤14 years from the Western Cape (WC) in South Africa. Outcomes of patients lost to follow‐up were available from (1) a tracing study and (2) linkage to a health information exchange. For both populations, we compared six methods for correcting mortality estimates for all children, adolescents and young adults with HIV. Results: We found substantial variations of mortality estimates among children, adolescents and young adults with HIV reported as lost to follow‐up versus those retained in care. Ascertained mortality was higher among lost and traceable children, adolescents and young adults with HIV and lower among lost and linkable than those retained in care (mortality: 13.4% [traced] vs. 12.6% [retained‐other Southern Africa countries]; 3.4% [linked] vs. 9.4% [retained‐WC]). A high proportion of lost to follow‐up children, adolescents and young adults with HIV had self‐transferred (21.0% and 47.0%) in the traced and linked samples, respectively. The uncorrected method of non‐informative censoring yielded the lowest mortality estimates among all methods for both tracing (6.0%) and linkage (4.0%) approaches at 2 years from ART start. Among corrected methods using ascertained data, multiple imputation, incorporating ascertained data (MI(asc.)) and inverse probability weighting with logistic weights were most robust for the tracing approach. In contrast, for the linkage approach, MI(asc.) was the most robust. Conclusions: Our findings emphasise that lost to follow‐up is non‐ignorable and both tracing and linkage improved outcome ascertainment: tracing identified substantial mortality in those reported as lost to follow‐up, whereas linkage did not identify out‐of‐facility deaths, but showed that a large proportion of those reported as lost to follow‐up were self‐transfers. [ABSTRACT FROM AUTHOR]

Introduction: The Treat-All policy--antiretroviral therapy (ART) initiation irrespective of CD4 cell criteria--increases access to treatment. Many ART programmes, however, reported increasing attrition and viral failure during treatment expansion, questioning the programmatic feasibility of Treat-All in resource-limited settings. We aimed to describe and compare programmatic outcomes between Treat-All and standard of care (SOC) in the public sectors of Eswatini. Methods: This is a prospective cohort study of [greater than or equal to]16-year-old HIV-positive patients initiated on first-line ART under Treat-Al and SOC in 18 health facilities of the Shiselweni region, from October 2014 to March 2016. SOC followed the CD4 350 and 500 cells/[mm.sup.3] treatment eligibility thresholds. Kaplan-Meier estimates were used to describe crude programmatic outcomes. Multivariate flexible parametric survival models were built to assess associations of time from ART initiation with the composite unfavourable outcome of all-cause attrition and viral failure. Results: Of the 3170 patients, 1888 (59.6%) initiated ART under Treat-All at a median CD4 cell count of 329 (IQR 168 to 488) cells/[mm.sup.3] compared with 292 (IQR 161 to 430) (p < 0.001) under SOC. Although crude programme retention at 36 months tended to be lower under Treat-All (71%) than SOC (75%) (p = 0.002), it was similar in covariate-adjusted analysis (adjusted hazard ratio [aHR] 1.06, 95% CI 0.91 to 1.23). The hazard of viral suppression was higher for Treat-All (aHR 1.12, 95% CI 1.01 to 1.23), while the hazard of viral failure was comparable (Treat-All: aHR 0.89, 95% CI 0.53 to 1.49). Among patients with advanced HIV disease (n = 1080), those under Treat-All (aHR 1.13, 95% CI 0.88 to 1.44) had a similar risk of an composite unfavourable outcome to SOC. Factors increasing the risk of the composite unfavourable outcome under both interventions were aged 16 to 24 years, being unmarried, anaemia, ART initiation on the same day as HIV care enrolment and CD4 [less than or equal to] 100 cells/[mm.sup.3]. Under Treat-All only, the risk of the unfavourable outcome was higher for pregnant women, WHO III/IV clinical stage and elevated creatinine. Conclusions: Compared to SOC, Treat-All resulted in comparable retention, improved viral suppression and comparable composite outcomes of retention without viral failure. Keywords: treat all; retention; viral failure; Swaziland; Eswatini; HIV, 1 | INTRODUCTION The World Health Organization (WHO) recommends antiretroviral therapy (ART) initiation at the time of HIV diagnosis irrespective of clinical and immunological criteria, aiming at improving patient-level outcomes [...]

This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpart.

Objectives: Despite declining TB notifications in Southern Africa, TB‐related deaths remain high. We describe patient‐ and population‐level trends in TB‐related deaths in Eswatini over a period of 11 years. Methods: Patient‐level (retrospective cohort, from 2009 to 2019) and population‐level (ecological analysis, 2009–2017) predictors and rates of TB‐related deaths were analysed in HIV‐negative and HIV‐coinfected first‐line TB treatment cases and the population of the Shiselweni region. Patient‐level TB treatment data, and population and HIV prevalence estimates were combined to obtain stratified annual mortality rates. Multivariable Poisson regressions models were fitted to identify patient‐level and population‐level predictors of deaths. Results: Of 11,883 TB treatment cases, 1302 (11.0%) patients died during treatment: 210/2798 (7.5%) HIV‐negative patients, 984/8443 (11.7%) people living with HIV (PLHIV), and 108/642 (16.8%) patients with unknown HIV‐status. The treatment case fatality ratio remained above 10% in most years. At patient‐level, fatality risk was higher in PLHIV (aRR 1.74, 1.51–2.02), and for older age and extra‐pulmonary TB irrespective of HIV‐status. For PLHIV, fatality risk was higher for TB retreatment cases (aRR 1.38, 1.18–1.61) and patients without antiretroviral therapy (aRR 1.70, 1.47–1.97). It decreases with increasing higher CD4 strata and the programmatic availability of TB‐LAM testing (aRR 0.65, 0.35–0.90). At population‐level, mortality rates decreased 6.4‐fold (−147/100,000 population) between 2009 (174/100,000) and 2017 (27/100,000), coinciding with a decline in TB treatment cases (2785 in 2009 to 497 in 2017). Although the absolute decline in mortality rates was most pronounced in PLHIV (−826/100,000 vs. HIV‐negative: −23/100,000), the relative population‐level mortality risk remained higher in PLHIV (aRR 4.68, 3.25–6.72) compared to the HIV‐negative population. Conclusions: TB‐related mortality rapidly decreased at population‐level and most pronounced in PLHIV. However, case fatality among TB treatment cases remained high. Further strategies to reduce active TB disease and introduce improved TB therapies are warranted. [ABSTRACT FROM AUTHOR]