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BackgroundUnderstanding advances in the care and treatment of adults with HIV as well as remaining gaps requires comparing differences in mortality between persons entering care for HIV and the general population.ObjectiveTo assess the extent to which mortality among persons entering HIV care in the United States is elevated over mortality among matched persons in the general U.S. population and trends in this difference over time.DesignObservational cohort study.SettingThirteen sites from the U.S. North American AIDS Cohort Collaboration on Research and Design.Participants82 766 adults entering HIV clinical care between 1999 and 2017 and a subset of the U.S. population matched on calendar time, age, sex, race/ethnicity, and county using U.S. mortality and population data compiled by the National Center for Health Statistics.MeasurementsFive-year all-cause mortality, estimated using the Kaplan-Meier estimator of the survival function.ResultsOverall 5-year mortality among persons entering HIV care was 10.6%, and mortality among the matched U.S. population was 2.9%, for a difference of 7.7 (95% CI, 7.4 to 7.9) percentage points. This difference decreased over time, from 11.1 percentage points among those entering care between 1999 and 2004 to 2.7 percentage points among those entering care between 2011 and 2017.LimitationMatching on available covariates may have failed to account for differences in mortality that were due to sociodemographic factors rather than consequences of HIV infection and other modifiable factors.ConclusionMortality among persons entering HIV care decreased dramatically between 1999 and 2017, although those entering care remained at modestly higher risk for death in the years after starting care than comparable persons in the general U.S. population.Primary funding sourceNational Institutes of Health.

Multimorbidity, defined as having 2 or more chronic conditions, is a growing public health concern, but research in this area is complicated by the fact that multimorbidity is a highly heterogenous outcome. Individuals in a sample may have a differing number and varied combinations of conditions. Clustering methods, such as unsupervised machine learning algorithms, may allow us to tease out the unique multimorbidity phenotypes. However, many clustering methods exist, and choosing which to use is challenging because we do not know the true underlying clusters. Here, we demonstrate the use of 3 individual algorithms (partition around medoids, hierarchical clustering, and probabilistic clustering) and a clustering ensemble approach (which pools different clustering approaches) to identify multimorbidity clusters in the AIDS Linked to the Intravenous Experience cohort study. We show how the clusters can be compared based on cluster quality, interpretability, and predictive ability. In practice, it is critical to compare the clustering results from multiple algorithms and to choose the approach that performs best in the domain(s) that aligns with plans to use the clusters in future analyses. [ABSTRACT FROM AUTHOR]

Heavy drinking is prevalent among people living with HIV. Studies use tools like patient-reported outcomes (PROs) to quantify alcohol use in a detailed, timely manner. However, if alcohol misuse influences PRO completion, selection bias may result. Our study included 14,145 adult HIV patients (133,036 visits) from CNICS who were eligible to complete PROs at an HIV primary care visit. We compared PRO completion proportions between patients with and without a clinical diagnosis of at-risk alcohol use in the prior year. We accounted for confounding by baseline and visit-specific covariates. PROs were completed at 20.8% of assessed visits. The adjusted difference in PRO completion proportions was -3.2% (95% CI -5.6 to -0.8%). The small association between receipt of an at-risk alcohol use diagnosis and decreased PRO completion suggests there could be modest selection bias in studies using the PRO alcohol measure.

Disparities in HIV care by socioeconomic status, place of residence, and race/ethnicity prevent progress toward epidemic control. No study has comprehensively characterized the HIV care cascade among people with HIV enrolled in Medicaid – an insurance source for low-income individuals in the US. We analyzed data from 246,127 people with HIV enrolled in Medicaid 2001–2015, aged 18–64, living in 14 US states. We estimated the monthly prevalence of four steps of the care cascade: retained in care/adherent to ART; retained/not adherent; not retained/adherent; not retained/not adherent. Beneficiaries were retained in care if they had an outpatient care encounter every six months. Adherence was based on medication possession ratio. We estimated prevalence using a non-parametric multi-state approach, accounting for death as a competing event and for Medicaid disenrollment using inverse probability of censoring weights. Across 2001–2015, the proportion of beneficiaries with HIV who were retained/ART adherent increased, overall and in all subgroups. By 2015, approximately half of beneficiaries were retained in care, and 42% of beneficiaries were ART adherent. We saw meaningful differences by race/ethnicity and region. Our work highlights an important disparity in the HIV care cascade by insurance status during this time period. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) incidence is reportedly lower in men with HIV compared to men without HIV for unknown reasons. We describe PCa incidence by HIV status in Medicaid beneficiaries, allowing for comparison of men with and without HIV who are similar with respect to socioeconomic characteristics and access to healthcare. Men (N = 15,167,636) aged 18–64 with ≥7 months of continuous enrollment during 2001–2015 in 14 US states were retained for analysis. Diagnoses of HIV and PCa were identified using non-drug claims. We estimated cause-specific (csHR) comparing incidence of PCa by HIV status, adjusted for age, race-ethnicity, state of residence, year of enrollment, and comorbid conditions, and stratified by age and race-ethnicity. Hazard of PCa was lower in men with HIV than men without HIV (csHR = 0.89; 95% CI: 0.80, 0.99), but varied by race-ethnicity, with similar observations among non-Hispanic Black (csHR = 0.79; 95% CI: 0.69, 0.91) and Hispanic (csHR = 0.85; 95% CI: 0.67, 1.09), but not non-Hispanic white men (csHR = 1.17; 95% CI: 0.91, 1.50). Findings were similar in models restricted to men aged 50–64 and 40–49, but not in men aged 18–39. Reported deficits in PCa incidence by HIV status may be restricted to specific groups defined by age and race ethnicity. [ABSTRACT FROM AUTHOR]

Background People with human immunodeficiency virus (HIV; PWH) in the United States have a lower incidence of colon cancer than the general population. The lower incidence may be explained by differences in receipt of screening. Thus, we sought to estimate colon cancer incidence under scenarios in which Medicaid beneficiaries, with or without HIV, followed the same screening protocols. Methods We used data from 1.5 million Medicaid beneficiaries who were enrolled in 14 US states in 2001–2015 and aged 50–64 years; 72 747 beneficiaries had HIV. We estimated risks of colon cancer and death by age, censoring beneficiaries when they deviated from 3 screening protocols, which were based on Medicaid's coverage policy for endoscopies during the time period, with endoscopy once every 2, 4, or 10 years. We used inverse probability weights to control for baseline and time-varying confounding and informative loss to follow-up. Analyses were performed overall, by sex, and by race/ethnicity. Results PWH had a lower incidence of colon cancer than beneficiaries without HIV. Compared with beneficiaries without HIV, the risk difference at age 65 years was −1.6% lower (95% confidence interval, −2.3% to −.7%) among PWH with the 2-year protocol and −0.8% lower (−1.3% to −.3%) with the 10-year protocol. Results were consistent across subgroup and sensitivity analyses. Conclusions Our findings suggest that the lower risk of colon cancer that has been observed among PWH aged 50–64 years compared with those without HIV is not due to differences in receipt of lower endoscopy. Keywords: colon cancer, colorectal cancer screening, endoscopy, Medicaid, human immunodeficiency virus [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text. Background: Life expectancy among people with HIV (PWH) is increasing, making chronic conditions—including cancer—increasingly relevant. Among PWH, cancer burden has shifted from AIDS-defining cancers (ADCs) toward non–AIDS-defining cancers (NADCs). Setting: We described incidence of cancer in a claims-based cohort of Medicaid beneficiaries. We included 43,426,043 Medicaid beneficiaries (180,058 with HIV) from 14 US states, aged 18–64, with >6 months of enrollment (with no dual enrollment in another insurance) and no evidence of a prveious cancer. Methods: We estimated cumulative incidence of site-specific cancers, NADCs, and ADCs, by baseline HIV status, using age as the time scale and accounting for death as a competing risk. We compared cumulative incidence across HIV status to estimate risk differences. We examined cancer incidence overall and by sex, race/ethnicity, and calendar period. Results: PWH had a higher incidence of ADCs, infection-related NADCs, and death. For NADCs such as breast, prostate, and colon cancer, incidence was similar or higher among PWH below age 50, but higher among those without HIV by age 65. Incidence of lung and head and neck cancer was always higher for female beneficiaries with HIV, whereas the curves crossed for male beneficiaries. We saw only small differences in incidence trends by race/ethnicity. Conclusion: Our findings suggest an increased risk of certain NADCs at younger ages among PWH, even when compared against other Medicaid beneficiaries, and highlight the importance of monitoring PWH for ADCs and NADCs. Future work should explore possible mechanisms explaining the differences in incidence for specific cancer types. [ABSTRACT FROM AUTHOR]

We evaluated geographic heterogeneity in hepatitis C virus (HCV) treatment penetration among people who inject drug (PWID) across Baltimore, MD since the advent of direct‐acting antivirals (DAAs) using space–time clusters of HCV viraemia. Using data from a community‐based cohort of PWID, the AIDS Linked to the IntraVenous Experience (ALIVE) study, we identified space–time clusters with higher‐than‐expected rates of HCV viraemia between 2015 and 2019 using scan statistics. We used Poisson regression to identify covariates associated with HCV viraemia and used the regression‐fitted values to detect adjusted space–time clusters of HCV viraemia in Baltimore city. Overall, in the cohort, HCV viraemia fell from 77% in 2015 to 64%, 49%, 39% and 36% from 2016 to 2019. In Baltimore city, the percentage of census tracts where prevalence of HCV viraemia was ≥85% dropped from 57% to 34%, 25%, 22% and 10% from 2015 to 2019. We identified two clusters of higher‐than‐expected HCV viraemia in the unadjusted analysis that lasted from 2015 to 2017 in East and West Baltimore and one adjusted cluster of HCV viraemia in West Baltimore from 2015 to 2016. Neither differences in age, sex, race, HIV status, nor neighbourhood deprivation were able to explain the significant space–time clusters. However, residing in a cluster with higher‐than‐expected viraemia was associated with age, sex, educational attainment and higher levels of neighbourhood deprivation. Nearly 4 years after DAAs became available, HCV treatment has penetrated all PWID communities across Baltimore city. While nearly all census tracts experienced improvements, change was more gradual in areas with higher levels of poverty. [ABSTRACT FROM AUTHOR]

Inverse probability weighting (IPW) and g-computation are commonly used in time-varying analyses. To inform decisions on which to use, we compared these methods using a plasmode simulation based on data from the Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial (June 15, 2007–July 15, 2011). In our main analysis, we simulated a cohort study of 1,226 individuals followed for up to 10 weeks. The exposure was weekly exercise, and the outcome was time to pregnancy. We controlled for 6 confounding factors: 4 baseline confounders (race, ever smoking, age, and body mass index) and 2 time-varying confounders (compliance with assigned treatment and nausea). We sought to estimate the average causal risk difference by 10 weeks, using IPW and g-computation implemented using a Monte Carlo estimator and iterated conditional expectations (ICE). Across 500 simulations, we compared the bias, empirical standard error (ESE), average standard error, standard error ratio, and 95% confidence interval coverage of each approach. IPW (bias = 0.02; ESE = 0.04; coverage = 92.6%) and Monte Carlo g-computation (bias = −0.01; ESE = 0.03; coverage = 94.2%) performed similarly. ICE g-computation was the least biased but least precise estimator (bias = 0.01; ESE = 0.06; coverage = 93.4%). When choosing an estimator, one should consider factors like the research question, the prevalences of the exposure and outcome, and the number of time points being analyzed. [ABSTRACT FROM AUTHOR]

Simulation methods are a powerful set of tools that can allow researchers to better characterize phenomena from the real world. As such, the ability to simulate data represents a critical set of skills that epidemiologists should use to better understand epidemiologic concepts and ensure that they have the tools to continue to self-teach even when their formal instruction ends. Simulation methods are not always taught in epidemiology methods courses, whereas causal directed acyclic graphs (DAGs) often are. Therefore, this paper details an approach to building simulations from DAGs and provides examples and code for learning to perform simulations. We recommend using very simple DAGs to learn the procedures and code necessary to set up a simulation that builds on key concepts frequently of interest to epidemiologists (e.g. mediation, confounding bias, M bias). We believe that following this approach will allow epidemiologists to gain confidence with a critical skill set that may in turn have a positive impact on how they conduct future epidemiologic studies. [ABSTRACT FROM AUTHOR]