The proportion of people living with HIV with suppressed viral load is a key indicator of the HIV care continuum. We explored how this proportion varied depending on how it was calculated.
...Observational cohort study.
We calculated the proportion of the Johns Hopkins HIV Clinical Cohort who were virally suppressed each year, 2010-2018, based on different denominators; thresholds for suppression (≤20, ≤50, ≤200, or ≤400 copies/ml); and strategies for summarizing multiple viral load measurements (we classified persons as suppressed if they had any lab, ≥50% of labs, last lab, or all labs below the threshold). We also calculated 5-year risk of all-cause mortality associated with each classification of viral suppression.
Three thousand eleven persons contributed 60 858 viral load values to this analysis. Proportion classified as virally suppressed ranged from 51.8 to 92.5%, depending on the definition used and persons included in the calculation. Requiring more labs below the threshold; using a lower threshold; and assuming persons lost to follow-up were not suppressed (stricter definitions) resulted in a lower proportion estimated to be suppressed. Suppression by stricter definitions were associated with better 5-year survival.
The proportion suppressed varied greatly as a function of the subset of persons in whom it was calculated, the threshold used for suppression, and the way multiple viral loads per person per year were summarized. Measures of durable viral suppression, and low-level viremia (20-400 copies/ml), should be considered in describing the health of people with HIV, in addition to the standard estimates of suppression.
When baseline risk of an outcome varies within a population, the effect of a treatment on that outcome will vary on at least one scale (e.g., additive, multiplicative). This treatment effect ...heterogeneity is of interest in patient-centered outcomes research. Based on a literature review and solicited expert opinion, we assert the following: (1) Treatment effect heterogeneity on the additive scale is most interpretable to health-care providers and patients using effect estimates to guide treatment decision-making; heterogeneity reported on the multiplicative scale may be misleading as to the magnitude or direction of a substantively important interaction. (2) The additive scale may give clues about sufficient-cause interaction, although such interaction is typically not relevant to patients' treatment choices. (3) Statistical modeling need not be conducted on the same scale as results are communicated. (4) Statistical testing is one tool for investigations, provided important subgroups are identified a priori, but test results should be interpreted cautiously given nonequivalence of statistical and clinical significance. (5) Qualitative interactions should be evaluated in a prespecified manner for important subgroups. Principled analytic plans that take into account the purpose of investigation of treatment effect heterogeneity are likely to yield more useful results for guiding treatment decisions.
We present a novel, patient-centric, longitudinal summary of patient progress through the HIV care continuum. Using this new approach, we compare person-time spent alive, in care, on antiretroviral ...therapy (ART), and virally suppressed among people who inject drugs (PWID) and those who do not (non-IDU).
Prospective clinical observational cohort study.
We followed ART-naive patients with detectable HIV viral loads who enrolled in the Johns Hopkins HIV Clinical Cohort from enrollment until the occurrence of several care continuum-related milestones, including ART initiation and viral suppression, and until several care continuum-related failures, including loss to clinic and death. We added and subtracted cumulative incidence curves to estimate the proportion of the cohort in each of seven continuum stages across the 10 years following enrollment in clinical care.
PWID composed 32% of the study sample (n = 1443). Over 10 years following enrollment in care, PWID and non-IDU spent only 23 and 37%, respectively, of person-time in care, on ART, and virally suppressed. PWID lost 8.9 more months of life compared with non-IDU and spent an additional 5.0 months on ART but not virally suppressed, and an additional 5.5 months in care but not on ART. There were not meaningful improvements in the 5-year restricted mean person-time differences comparing PWID to non-IDU across enrollment cohorts (2000-2003, 2004-2007, 2008-2014).
Efforts to increase viral suppression among PWID should focus on increasing ART initiation and improving adherence to therapy.
When to Censor? Lesko, Catherine R; Edwards, Jessie K; Cole, Stephen R ...
American journal of epidemiology,
03/2018, Letnik:
187, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Abstract
Loss to follow-up is an endemic feature of time-to-event analyses that precludes observation of the event of interest. To our knowledge, in typical cohort studies with encounters occurring ...at regular or irregular intervals, there is no consensus on how to handle person-time between participants’ last study encounter and the point at which they meet a definition of loss to follow-up. We demonstrate, using simulation and an example, that when the event of interest is captured outside of a study encounter (e.g., in a registry), person-time should be censored when the study-defined criterion for loss to follow-up is met (e.g., 1 year after last encounter), rather than at the last study encounter. Conversely, when the event of interest must be measured within the context of a study encounter (e.g., a biomarker value), person-time should be censored at the last study encounter. An inappropriate censoring scheme has the potential to result in substantial bias that may not be easily corrected.
Restricted mean survival time (RMST) is an underutilized estimand in time-to-event analyses. Herein, we highlight its strengths by comparing time to (1) all-cause mortality and (2) initiation of ...antiretroviral therapy (ART) for HIV-infected persons who inject drugs (PWID) and persons who do not inject drugs.
RMST to death was determined by integrating the Kaplan-Meier survival curve to 5 years of follow-up. To account for the competing risks of death and loss-to-clinic when estimating time to ART, we calculated RMST to ART initiation by estimating the area between the survival curve for ART initiation and the cumulative incidence curve for death or loss-to-clinic. We standardized all curves using inverse probability of exposure weights.
We followed 3044 HIV-positive, ART-naive persons from enrollment into the Johns Hopkins HIV Clinical Cohort from 1996 to 2014. PWID had a - 0.19 year (95% confidence interval (CI): - 0.29, - 0.10) difference in survival over 5 years of follow-up compared to persons who did not inject drugs. There was no difference between the two groups in time not on ART while alive and in clinic (RMST difference = 0.08, 95% CI: -0.10, 0.36).
PWID have similar expected time to ART initiation after properly accounting for their greater risk of death and loss-to-clinic.
Abstract
There has been much debate about the relative emphasis of the field of epidemiology on causal inference. We believe this debate does short shrift to the breadth of the field. Epidemiologists ...answer myriad questions that are not causal and hypothesize about and investigate causal relationships without estimating causal effects. Descriptive studies face significant and often overlooked inferential and interpretational challenges; we briefly articulate some of them and argue that a more detailed treatment of biases that affect single-sample estimation problems would benefit all types of epidemiologic studies. Lumping all questions about causality creates ambiguity about the utility of different conceptual models and causal frameworks; 2 distinct types of causal questions include 1) hypothesis generation and theorization about causal structures and 2) hypothesis-driven causal effect estimation. The potential outcomes framework and causal graph theory help efficiently and reliably guide epidemiologic studies designed to estimate a causal effect to best leverage prior data, avoid cognitive fallacies, minimize biases, and understand heterogeneity in treatment effects. Appropriate matching of theoretical frameworks to research questions can increase the rigor of epidemiologic research and increase the utility of such research to improve public health.
PDF
