Hepatitis C Treatment in People With HIV: Potential to Eliminate Disease and Disparity.

Access to direct acting antivirals (DAAs) may be associated with reductions in hepatitis C virus (HCV) viremia prevalence among people with human immunodeficiency virus (PWH). Among 3755 PWH, estimated HCV viremia prevalence decreased by 94.0% from 36% (95% confidence interval [CI], 27%-46%) in 2009 (pre-DAA era) to 2% (95% CI, 0%-4%) in 2021 (DAA era). Male sex, black race, and older age were associated with HCV viremia in 2009 but not in 2021. Injection drug use remained associated with HCV viremia in 2009 and 2021. Targeted interventions are needed to meet the HCV care needs of PWH who use drugs.

Spatio-temporal patterns of excess mortality from non-COVID causes of death in the United States, march-December 2020.

Objectives To estimate the burden of excess mortality from 17 underlying causes of death between March-December 2020 in the United States, and to compare trends in excess deaths from non-COVID causes vs. from COVID-19. Methods Using time series models, we estimated monthly counts of all-cause and cause-specific excess deaths. We stratified by geographic region and compared temporal trends in excess deaths from non-COVID causes to trends in deaths attributed to COVID-19. Results Of approximately 500,000 excess deaths, 70% were attributed to COVID-19. We observed increases in several underlying causes of death, ranging from a 3% increase in kidney disease deaths to a 24% increase in homicides, as well as decreases in deaths from cancer (-0.3%), influenza and pneumonia (-2%), chronic lower respiratory disease (-3%), and suicide (-7%). Trends in excess deaths from cardiovascular disease, diabetes, and Alzheimer's disease closely mirrored trends in deaths from COVID-19. Trends in excess liver disease, homicide, suicide, and motor vehicle accident deaths were negatively correlated with trends in deaths from COVID-19. There was wide regional variation in excess death rates for some causes of death, including a disproportionate increase in homicide and motor vehicle accident deaths in the Great Lakes, and a sustained reduction in cancer deaths in the Mideast and New England. Conclusions Increases in cardiovascular disease, diabetes, and Alzheimer's disease deaths from March-December 2020 likely reflect healthcare system disruptions or acute complications of COVID-19. Excess deaths from homicide and liver disease are more likely to reflect social and economic effects of the emerging pandemic, or other separate causes.

An evolved interpretation of Austin Bradford Hill's causal viewpoints and their influence on epidemiologic methods.

In 1965, Sir Austin Bradford Hill articulated nine viewpoints for evaluating whether a body of evidence about the relationship between an exposure and outcome should be interpreted causally. In this commentary, we highlight a selection of the ways in which these viewpoints have had an impact on the field of epidemiology in terms of methods development, study design, and interpretation of results. Additionally, we opine on how the viewpoints relate to our understanding of basic epidemiologic concepts - for example, our choice of absolute or relative measures of effect, our evolving understanding of the role of context in the generalizability of study results, and modern epistemologies for causal inference (i.e., the potential outcomes framework and graphical causal models). Hill cautioned his audience that evidence should be weighed according to the policy choice it would inform and the context in which that policy would be implemented. We root our remarks in considerations of the public health impact of our conclusions about the causal nature of an observed relationship.

Association of Alcohol Use with COVID-19 Infection and Hospitalization Among People Living with HIV in the United States, 2020.

Alcohol use was associated with elevated COVID-19 risk in the general population. People with HIV (PWH) have high prevalences of alcohol use. To evaluate the effect of alcohol use on COVID-19 risks among PWH, we estimated the risk of COVID-19 diagnosis and COVID-19-related hospitalization among PWH in routine care at 8 HIV primary care centers that contributed data to the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort according to their alcohol use just prior to the COVID-19 pandemic. The CNICS data repository includes demographic characteristics, clinical diagnoses, and laboratory test results from electronic medical records and other sources. Alcohol use, substance use, and mental health symptoms were self-reported on tablet-based standardized surveys. Alcohol use was categorized according to standard, sex-specific Alcohol Use Disorder Identification Test-Consumption instrument cut-offs. We followed 5,496 PWH (79% male, 48% Black race, median age = 53 years) from March 1, 2020 to December 31, 2020. Relative to PWH with no baseline alcohol use, the adjusted hazard ratio (aHR) of COVID-19 diagnosis was 1.09 (95% confidence interval [CI]: 0.78, 1.51) for lower-risk drinking and 1.19 (95%CI: 0.81, 1.73) for unhealthy drinking. The aHR of COVID-19-related hospitalization was 0.82 (95%CI: 0.33, 1.99) for lower-risk drinking and 1.25 (95%CI: 0.50, 3.09) for unhealthy drinking. Results were not modified by recent cocaine or non-prescribed opioid use, depressive symptoms, or diagnoses of alcohol use disorder. The study suggested a slightly increased, but not statistically significant risk of COVID-19 diagnosis and hospitalization associated with unhealthy alcohol use.

Investigating the impact of suboptimal prescription of preoperative antiplatelets and statins on race and ethnicity-related disparities in major limb amputation.

BACKGROUND: Non-Hispanic Black and Hispanic patients with symptomatic PAD may receive different treatments than White patients with symptomatic PAD. The delivery of guideline-directed medical treatment may be a modifiable upstream driver of race and ethnicity-related disparities in outcomes such as limb amputation. The purpose of our study was to investigate the prescription of preoperative antiplatelets and statins in producing disparities in the risk of amputation following revascularization for symptomatic peripheral artery disease (PAD). METHODS: We used data from the Vascular Quality Initiative, a vascular procedure-based registry in the United States (2011-2018). We estimated the probability of preoperative antiplatelet and statin prescriptions and 1-year incidence of amputation. We then estimated the amputation risk difference between race/ethnicity groups that could be eliminated under a hypothetical intervention. RESULTS: Across 100,579 revascularizations, the 1-year amputation risk was 2.5% (2.4%, 2.6%) in White patients, 5.3% (4.9%, 5.6%) in Black patients, and 5.3% (4.7%, 5.9%) in Hispanic patients. Black (57.5%) and Hispanic patients (58.7%) were only slightly less likely than White patients (60.9%) to receive antiplatelet and statin therapy. However, the effect of antiplatelets and statins was greater in Black and Hispanic patients such that, had all patients received these medications, the estimated risk difference comparing Black to White patients would have reduced by 8.9% (-2.9%, 21.9%) and the risk difference comparing Hispanic to White patients would have been reduced by 17.6% (-0.7%, 38.6%). CONCLUSION: Even though guideline-directed care appeared evenly distributed by race/ethnicity, increasing access to such care may decrease health care disparities in major limb amputation.

Cohort profile update: the Johns Hopkins HIV clinical cohort, 1989-2023.

The Johns Hopkins HIV Clinical Cohort, established in 1989, links comprehensive, longitudinal clinical data for adults with HIV receiving care in the Johns Hopkins John G. Bartlett Specialty Practice in Baltimore, Maryland, USA, to aid in understanding HIV care and treatment outcomes. Data include demographics, laboratory results, inpatient and outpatient visit information and clinical diagnoses, and prescribed and dispensed medications abstracted from medical records. A subset of patients separately consents to self-report patient-centric outcomes on standardized instruments approximately every 6 months, and another subset separately consents to contribute plasma and peripheral blood mononuclear cells to a linked specimen repository approximately annually. The cohort has cumulatively enrolled over 8000 people, with just under 2000 on average attending ≥ 1 HIV primary care visit in any given year. The cohort reflects the HIV epidemic in Baltimore: in 2021, median age was 57, 64% of participants were male, 77% were non-Hispanic Black, and 37% acquired HIV through injection drug use. This update to the cohort profile of the Johns Hopkins HIV Clinical Cohort illustrates both how the population of people with HIV in Baltimore, Maryland, USA has changed over three decades, and we have adapted data collection procedures over three decades to ensure this long-running cohort remains responsive to patient characteristics and research gaps in the provision of care to people with HIV and substance use.

When Does Differential Outcome Misclassification Matter for Estimating Prevalence?

BACKGROUND: When accounting for misclassification, investigators make assumptions about whether misclassification is "differential" or "nondifferential." Most guidance on differential misclassification considers settings where outcome misclassification varies across levels of exposure, or vice versa. Here, we examine when covariate-differential misclassification must be considered when estimating overall outcome prevalence. METHODS: We generated datasets with outcome misclassification under five data generating mechanisms. In each, we estimated prevalence using estimators that (a) ignored misclassification, (b) assumed misclassification was nondifferential, and (c) allowed misclassification to vary across levels of a covariate. We compared bias and precision in estimated prevalence in the study sample and an external target population using different sources of validation data to account for misclassification. We illustrated use of each approach to estimate HIV prevalence using self-reported HIV status among people in East Africa cross-border areas. RESULTS: The estimator that allowed misclassification to vary across levels of the covariate produced results with little bias for both populations in all scenarios but had higher variability when the validation study contained sparse strata. Estimators that assumed nondifferential misclassification produced results with little bias when the covariate distribution in the validation data matched the covariate distribution in the target population; otherwise estimates assuming nondifferential misclassification were biased. CONCLUSIONS: If validation data are a simple random sample from the target population, assuming nondifferential outcome misclassification will yield prevalence estimates with little bias regardless of whether misclassification varies across covariates. Otherwise, obtaining valid prevalence estimates requires incorporating covariates into the estimators used to account for misclassification.

A Framework for Descriptive Epidemiology.

In this paper, we propose a framework for thinking through the design and conduct of descriptive epidemiologic studies. A well-defined descriptive question aims to quantify and characterize some feature of the health of a population and must clearly state: 1) the target population, characterized by person and place, and anchored in time; 2) the outcome, event, or health state or characteristic; and 3) the measure of occurrence that will be used to summarize the outcome (e.g., incidence, prevalence, average time to event, etc.). Additionally, 4) any auxiliary variables will be prespecified and their roles as stratification factors (to characterize the outcome distribution) or nuisance variables (to be standardized over) will be stated. We illustrate application of this framework to describe the prevalence of viral suppression on December 31, 2019, among people living with human immunodeficiency virus (HIV) who had been linked to HIV care in the United States. Application of this framework highlights biases that may arise from missing data, especially 1) differences between the target population and the analytical sample; 2) measurement error; 3) competing events, late entries, loss to follow-up, and inappropriate interpretation of the chosen measure of outcome occurrence; and 4) inappropriate adjustment.

On the Need to Revitalize Descriptive Epidemiology.

Nearly every introductory epidemiology course begins with a focus on person, place, and time, the key components of descriptive epidemiology. And yet in our experience, introductory epidemiology courses were the last time we spent any significant amount of training time focused on descriptive epidemiology. This gave us the impression that descriptive epidemiology does not suffer from bias and is less impactful than causal epidemiology. Descriptive epidemiology may also suffer from a lack of prestige in academia and may be more difficult to fund. We believe this does a disservice to the field and slows progress towards goals of improving population health and ensuring equity in health. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak and subsequent coronavirus disease 2019 pandemic have highlighted the importance of descriptive epidemiology in responding to serious public health crises. In this commentary, we make the case for renewed focus on the importance of descriptive epidemiology in the epidemiology curriculum using SARS-CoV-2 as a motivating example. The framework for error we use in etiological research can be applied in descriptive research to focus on both systematic and random error. We use the current pandemic to illustrate differences between causal and descriptive epidemiology and areas where descriptive epidemiology can have an important impact.

Alcohol Use Disorder and Recent Alcohol Use and HIV Viral Non-Suppression Among People Engaged in HIV Care in an Urban Clinic, 2014-2018.

We estimated joint associations between having history of alcohol use disorder (AUD) (based on prior ICD-9/ICD-10 codes) and recent self-reported alcohol use and viral non-suppression (≥ 1 viral load measurement > 20 copies/mL in the same calendar year as alcohol consumption was reported) among patients on ART enrolled in routine care, 2014-2018, in an urban specialty clinic. Among 1690 patients, 26% had an AUD, 21% reported high-risk alcohol use, and 39% had viral non-suppression. Relative to person-years in which people without AUD reported not drinking, prevalence of viral non-suppression was higher in person-years when people with AUD reported drinking at any level; prevalence of viral non-suppression was not significantly higher in person-years when people with AUD reported not drinking or person-years when people without AUD reported drinking at any level. No level of alcohol use may be "safe" for people with a prior AUD with regard to maintaining viral suppression.

The Effect of Buprenorphine on Human Immunodeficiency Virus Viral Suppression.

BACKGROUND: Opioid use is prevalent among people living with human immunodeficiency virus (HIV; PLWH) and adversely affects HIV outcomes. We assessed the effect of buprenorphine (BUP) initiation on subsequent HIV viral loads. METHODS: We identified PLWH from the Johns Hopkins HIV Clinical Cohort who initiated BUP between 2002 and 2017. Poisson regression with robust variance was used to estimate the prevalence of viral suppression (<200 copies/mL) before and after BUP initiation. We matched individuals who initiated BUP with controls based on viral load measurement dates and used prior event rate ratio (PERR) methods to estimate the effect of BUP initiation on viral suppression. PERR methods account for unmeasured confounders. RESULTS: We identified 279 PLWH who initiated BUP. After BUP initiation, PLWH were more likely to be virally suppressed (prevalence ratio [PR], 1.19; 95% confidence interval [CI], 1.03-1.37). After matching PLWH who initiated BUP to controls and accounting for measured and unmeasured confounders, BUP initiation increased viral suppression for both those on antiretroviral therapy (ART) at baseline (PERR PR, 1.08; 95% CI, 1.00-1.18) and those not on ART at baseline (PR, 1.31; 95% CI, 1.10-1.61). CONCLUSIONS: Our results indicate that the initiation of BUP results in an increase in the probability of being virally suppressed after accounting for both measured and unmeasured confounders. Persons with opioid use disorder should initiate BUP to not only treat substance use but also to increase viral suppression allowing for treatment as prevention.

HIV and COVID-19: Intersecting Epidemics With Many Unknowns.

As of July 2020, approximately 6 months into the pandemic of novel coronavirus disease 2019 (COVID-19), whether people living with human immunodeficiency virus (HIV; PLWH) are disproportionately affected remains an unanswered question. Thus far, risk of COVID-19 in people with and without HIV appears similar, but data are sometimes contradictory. Some uncertainty is due to the recency of the emergence of COVID-19 and sparsity of data; some is due to imprecision about what it means for HIV to be a "risk factor" for COVID-19. Forthcoming studies on the risk of COVID-19 to PLWH should differentiate between 1) the unadjusted, excess burden of disease among PLWH to inform surveillance efforts and 2) any excess risk of COVID-19 among PLWH due to biological effects of HIV, independent of comorbidities that confound rather than mediate this effect. PLWH bear a disproportionate burden of alcohol, other drug use, and mental health disorders, as well as other structural vulnerabilities, which might increase their risk of COVID-19. In addition to any direct effects of COVID-19 on the health of PLWH, we need to understand how physical distancing restrictions affect secondary health outcomes and the need for, accessibility of, and impact of alternative modalities of providing ongoing medical, mental health, and substance use treatment that comply with physical distancing restrictions (e.g., telemedicine).

Teaching Epidemiology Online (Pandemic Edition).

In response to the threat posed by the coronavirus disease 2019 (COVID-19) pandemic, many universities are encouraging or requiring online instruction. Teaching an epidemiology course online is different in many respects from teaching in person. In this article, we review specific approaches and strategies related to teaching epidemiology online during the pandemic and beyond, including a discussion of options for course format, grading and assessment approaches, pandemic-related contingencies, and the use of technology. Throughout this article we present practical, epidemiology-specific teaching examples. Moreover, we also examine 1) how the lessons learned about the practice of epidemiology during the pandemic can be integrated into the didactic content of epidemiology training programs and 2) whether epidemiologic pedagogy and teaching strategies should change in the long term, beyond the COVID-19 pandemic. The pandemic has served to heighten our awareness of concerns related to student health and safety, as well as issues of accessibility, equity, and inclusion. Our goal is to present a practical overview connecting pandemic-era online teaching with thoughts about the future of epidemiologic instruction.

Combining Effect Estimates Across Cohorts and Sufficient Adjustment Sets for Collaborative Research: A Simulation Study.

BACKGROUND: Collaborative research often combines findings across multiple, independent studies via meta-analysis. Ideally, all study estimates that contribute to the meta-analysis will be equally unbiased. Many meta-analyses require all studies to measure the same covariates. We explored whether differing minimally sufficient sets of confounders identified by a directed acyclic graph (DAG) ensures comparability of individual study estimates. Our analysis applied four statistical estimators to multiple minimally sufficient adjustment sets identified in a single DAG. METHODS: We compared estimates obtained via linear, log-binomial, and logistic regression and inverse probability weighting, and data were simulated based on a previously published DAG. RESULTS: Our results show that linear, log-binomial, and inverse probability weighting estimators generally provide the same estimate of effect for different estimands that are equally sufficient to adjust confounding bias, with modest differences in random error. In contrast, logistic regression often performed poorly, with notable differences in effect estimates obtained from unique minimally sufficient adjustment sets, and larger standard errors than other estimators. CONCLUSIONS: Our findings do not support the reliance of collaborative research on logistic regression results for meta-analyses. Use of DAGs to identify potentially differing minimally sufficient adjustment sets can allow meta-analyses without requiring the exact same covariates.

Generalizing randomized trial findings to a target population using complex survey population data.

Randomized trials are considered the gold standard for estimating causal effects. Trial findings are often used to inform policy and programming efforts, yet their results may not generalize well to a relevant target population due to potential differences in effect moderators between the trial and population. Statistical methods have been developed to improve generalizability by combining trials and population data, and weighting the trial to resemble the population on baseline covariates. Large-scale surveys in fields such as health and education with complex survey designs are a logical source for population data; however, there is currently no best practice for incorporating survey weights when generalizing trial findings to a complex survey. We propose and investigate ways to incorporate survey weights in this context. We examine the performance of our proposed estimator through simulations in comparison to estimators that ignore the complex survey design. We then apply the methods to generalize findings from two trials-a lifestyle intervention for blood pressure reduction and a web-based intervention to treat substance use disorders-to their respective target populations using population data from complex surveys. The work highlights the importance in properly accounting for the complex survey design when generalizing trial findings to a population represented by a complex survey sample.

Decreased Alcohol Consumption in an Implementation Study of Computerized Brief Intervention among HIV Patients in Clinical Care.

This prospective, nonrandomized implementation study evaluated a computerized brief intervention (CBI) for persons with HIV (PWH) and heavy/hazardous alcohol use. CBI was integrated into two HIV primary care clinics. Eligible patients were engaged in care, ≥ 18 years old, English speaking, endorsed heavy/hazardous alcohol use on the Alcohol Use Disorders Identification Test-C (AUDIT-C). Two 20-min computerized sessions using cognitive behavioral techniques were delivered by a 3-D avatar on touch screen tablets. Of 816 eligible AUDIT-C scores, 537 (66%) resulted in CBI invitation, 226 (42%) of invited patients enrolled, and 176 (78%) of enrolled patients watched at least one session. CBI enrollment was associated with a significant average reduction of 9.1 drinks/week (95% CI - 14.5, - 3.6) 4-12 months post-enrollment. Among those who participated in one or both sessions, average reduction in drinks/week was 11.7 drinks/week (95% CI - 18.8, - 4.6). There was corresponding improvement in AUDIT-C scores. Overall patients reported high levels of intervention satisfaction, particularly among older and Black patients. These promising results point to a practical intervention for alcohol reduction in this vulnerable patient population with elevated rates of heavy/hazardous drinking. Future research should examine strategies to increase initial engagement, strengthen intervention effects to increase the number of patients who achieve non-hazardous drinking, and examine the duration of therapeutic effects.

Changing Patterns of Alcohol Use and Probability of Unsuppressed Viral Load Among Treated Patients with HIV Engaged in Routine Care in the United States.

We examined HIV viral load non-suppression ([Formula: see text] 200 copies/mL) subsequent to person-periods (3-18 months) bookended by two self-reports of alcohol use on a standardized patient reported outcome assessment among adults in routine HIV care. We examined the relative risk (RR) of non-suppression associated with increases and decreases in alcohol use (relative to stable use), stratified by use at the start of the person-period. Increases in drinking from abstinence were associated with higher risk of viral non-suppression (low-risk without binge: RR 1.16, 95% CI 1.03, 1.32; low-risk with binge: RR 1.35, 95% CI 1.11, 1.63; high-risk: RR 1.89, 95% CI 1.16, 3.08). Decreases in drinking from high-risk drinking were weakly, and not statistically significantly associated with lower risk of viral non-suppression. Other changes in alcohol use were not associated with viral load non-suppression. Most changes in alcohol consumption among people using alcohol at baseline were not strongly associated with viral non-suppression.

The Epidemiologic Toolbox: Identifying, Honing, and Using the Right Tools for the Job.

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.

A comparison of cancer stage at diagnosis and treatment initiation between enrollees in an urban HIV clinic and SEER.

PURPOSE: A comparison of stage at cancer diagnosis and cancer treatment rates between people with HIV (PWH) and the general US population is needed to identify any disparities by HIV status. METHODS: We compared 236 PWH in clinical care diagnosed with cancer from 1997 to 2014 to a sample from NCI's Surveillance, Epidemiology and End Results (SEER) Program, presumed to be HIV negative. We performed G-computation using random forest methods to estimate stage and treatment percent differences (PD) by HIV. We conducted sensitivity analyses among non-AIDS-defining cancers (NADC), by sex and by CD4 ≤ 200 or > 200 cells/mm3. RESULTS: PWH were less likely to be diagnosed at localized stage (PD = - 16%; 95% CI - 21, - 11) and more likely to be diagnosed at regional stage (PD = 14%; 95% CI 8, 19) than those in SEER. Cancer treatment rates were 13% lower among PWH as compared to SEER (95% CI - 18, - 8). The difference in percent receiving cancer treatment was more pronounced for those with lower CD4 at cancer diagnosis (PD -15%; 95% CI - 27, - 6). Lower treatment rates were observed among NADC, males, and women with CD4 ≤ 200. CONCLUSION: Cancer care for PWH could be improved by diagnosis at earlier stages and increasing rates of cancer treatment.

Gone But Not Lost: Implications for Estimating HIV Care Outcomes When Loss to Clinic Is Not Loss to Care.

BACKGROUND: In some time-to-event analyses, it is unclear whether loss to follow up should be treated as a censoring event or competing event. Such ambiguity is particularly common in HIV research that uses routinely collected clinical data to report the timing of key milestones along the HIV care continuum. In this setting, loss to follow up may be viewed as a censoring event, under the assumption that patients who are "lost" from a study clinic immediately enroll in care elsewhere, or a competing event, under the assumption that people "lost" are out of care all together. METHODS: We illustrate an approach to address this ambiguity when estimating the 2-year risk of antiretroviral treatment initiation among 19,506 people living with HIV who enrolled in the IeDEA Central Africa cohort between 2006 and 2017, along with published estimates from tracing studies in Africa. We also assessed the finite sample properties of the proposed approach using simulation experiments. RESULTS: The estimated 2-year risk of treatment initiation was 69% if patients were censored at loss to follow up or 59% if losses to follow up were treated as competing events. Using the proposed approach, we estimated that the 2-year risk of antiretroviral therapy initiation was 62% (95% confidence interval: 61, 62). The proposed approach had little bias and appropriate confidence interval coverage under scenarios examined in the simulation experiments. CONCLUSIONS: The proposed approach relaxes the assumptions inherent in treating loss to follow up as a censoring or competing event in clinical HIV cohort studies.