Characterizing multimorbidity in ALIVE: Comparing single and ensemble clustering methods.

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.

Longitudinal patterns of use of stimulants and opioids in the AIDS linked to the IntraVenous experience cohort, 2005-2019.

BACKGROUND: Overdoses involving opioids and stimulants are on the rise, yet few studies have examined longitudinal trends in use of both substances. We sought to describe use and co-use of opioids and stimulants, 2005-2019, in the AIDS Linked to the Intravenous Experience (ALIVE) cohort - a community-based cohort of people with a history of injection drug use living in or near Baltimore, MD. METHODS: We included 2083 ALIVE participants, who had at least two visits during the study period. Our outcome was based on self-reported use of opioids and stimulants in the prior 6 months. We estimated prevalence of 4 categories of use (neither stimulants nor opioids, only stimulants, only opioids, stimulants and opioids), using a non-parametric multi-state model, accounting for the competing event of death and weighting for informative loss to follow-up. All analyses were stratified by enrollment cohort, with the main analysis including participants who enrolled prior to 2015 and a sub-analysis including participants who enrolled 2015-2018. RESULTS: In the main analysis, prevalence of using stimulants and opioids decreased from 38 % in 2005 to 12 % 2013 but stabilized from 2014 onwards (13-19 %). The prevalence of using only stimulants (7-11 %) and only opioids (5-10 %) was stable across time. Participants who reported using both were more likely to report homelessness, depression, and other substance use (e.g., marijuana and heavy alcohol use) than participants in the other use categories. On average, 65 % of visits with use of both were followed by a subsequent visit with use of both; of participants transitioning out of using both, 13% transitioned to using neither. CONCLUSIONS: While use of stimulants and opioids declined in the cohort through 2013, a meaningful proportion of participants persistently used both. More research is needed to understand and develop strategies to mitigate harms associated with persistent use of both stimulants and opioids.

Lower endoscopy, early-onset, and average-onset colon cancer among Medicaid beneficiaries with and without HIV.

BACKGROUND: Studies suggest a lower colorectal cancer (CRC) risk and lower or similar CRC screening among people with HIV (PWH) compared with the general population. We evaluated the incidence of lower endoscopy and average-onset (diagnosed at ≥50) and early-onset (diagnosed at <50) colon cancer by HIV status among Medicaid beneficiares with comparable sociodemographic factors and access to care. METHODS: We obtained Medicaid Analytic eXtract (MAX) data from 2001 to 2015 for 14 states. We included 41 727 243 and 42 062 552 unique individuals with at least 7 months of continuous eligibility for the endoscopy and colon cancer analysis, respectively. HIV and colon cancer diagnoses and endoscopy procedures were identified from inpatient and other nondrug claims. We used Cox proportional hazards regression models to assess endoscopy and colon cancer incidence, controlling for age, sex, race/ethnicity, calendar year and state of enrollment, and comorbidities conditions. RESULTS: Endoscopy and colon cancer incidence increased with age in both groups. Compared with beneficiaries without HIV, PWH had an increased hazard of endoscopy; this association was strongest among those 18-39 years [hazard ratio: 1.85, 95% confidence interval (95% CI) 1.77-1.92] and attenuated with age. PWH 18-39 years also had increased hazard of early-onset colon cancer (hazard ratio: 1.66, 95% CI:1.05-2.62); this association was attenuated after comorbidity adjustment. Hazard ratios were null among all beneficiaries less than 50 years of age. PWH had a lower hazard of average-onset colon cancer compared with those without HIV (hazard ratio: 0.79, 95% CI: 0.66-0.94). CONCLUSION: PWH had a higher hazard of endoscopy, particularly at younger ages. PWH had a lower hazard of average-onset colon cancer. Early-onset colon cancer was higher among the youngest PWH but not associated with HIV overall.

Comparing Cancer Incidence in an Observational Cohort of Medicaid Beneficiaries With and Without HIV, 2001-2015.

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.

Trajectories of drug treatment and illicit opioid use in the AIDS Linked to the IntraVenous Experience cohort, 2014-2019.

BACKGROUND: Medication for opioid use disorder (MOUD) is an effective intervention to combat opioid use disorder and overdose, yet there is limited understanding of engagement in treatment over time in the community, contextualized by ongoing substance use. We aimed to identify concurrent trajectories of methadone prescriptions, buprenorphine prescriptions, and illicit opioid use among older adults with a history of injection drug use. METHODS: We used data on 887 participants from the AIDS Linked to the IntraVenous Experience cohort, who were engaged in the study in 2013 and attended ≥1 visit during follow-up (2014-2019). Outcomes were self-reported MOUD prescription and illicit opioid use in the last 6 months. To identify concurrent trajectories in all 3 outcomes, we used group-based multi-trajectory modeling. We examined participant characteristics, including sociodemographics, HIV status, and other substance use, overall and by cluster. RESULTS: We identified 4 trajectory clusters: (1) no MOUD and no illicit opioid use (43%); (2) buprenorphine and some illicit opioid use (11%); (3) methadone and no illicit opioid use (28%); and (4) some methadone and illicit opioid use (18%). While prevalence of each outcome was stable across time, transitions on/off treatment or on/off illicit opioid use occurred, with the rate of transition varying by cluster. The rate of transition was highest in Cluster 3 (0.74/person-year) and lowest in Cluster 1 (0.18/person-year). We saw differences in participant characteristics by cluster, including that the buprenorphine cluster had the highest proportion of people with HIV and participants who identified as non-Hispanic Black. CONCLUSIONS: Most participants had discontinued illicit opioid use and were also not accessing MOUD. Trajectories defined by engagement with buprenorphine or methadone had distinct sociodemographic and behavioral characteristics, indicating that tailored interventions to expand access to both types of treatment are likely needed to reduce harms associated with untreated opioid use disorder.

Defining representativeness of study samples in medical and population health research.

Medical and population health science researchers frequently make ambiguous statements about whether they believe their study sample or results are representative of some (implicit or explicit) target population. This article provides a comprehensive definition of representativeness, with the goal of capturing the different ways in which a study can be representative of a target population. It is proposed that a study is representative if the estimate obtained in the study sample is generalisable to the target population (owing to representative sampling, estimation of stratum specific effects, or quantitative methods to generalise or transport estimates) or the interpretation of the results is generalisable to the target population (based on fundamental scientific premises and substantive background knowledge). This definition is explored in the context of four covid-19 studies, ranging from laboratory science to descriptive epidemiology. All statements regarding representativeness should make clear the way in which the study results generalise, the target population the results are being generalised to, and the assumptions that must hold for that generalisation to be scientifically or statistically justifiable.

Temporal association of pre-pandemic perceived social support with psychological resilience and mental well-being during the COVID-19 pandemic among people with a history of injection drug use.

BACKGROUND: There are limited data on whether modifiable social factors foster psychological resilience and mental well-being among people who use drugs following Big Events. We examined the temporal association of pre-pandemic perceived social support with psychological resilience and negative mental health symptoms during the COVID-19 pandemic among people with a history of injection drug use. METHODS: Between June and September 2020, we conducted a telephone survey among 545 participants in the AIDS Linked to the IntraVenous Experience (ALIVE) study: a community-based cohort of adults with a history of injection drug use. Leveraging data from study visits in 2018-early 2020, associations of pre-pandemic perceived social support with psychological resilience scores (range=1-5) and the probability of negative mental health symptoms during the pandemic were assessed using multivariable linear and modified Poisson regression models, respectively. RESULTS: Participants' median age was 58 years, 38.2% were female, 83.3% identified as Black, and 30.3% were living with HIV. During the pandemic, 14.5% had low (<3) resilience scores, 36.1% experienced anxiety, and 35.8% reported increased loneliness. Compared to participants in the lowest tertile of pre-pandemic social support, participants in the highest tertile had higher mean resilience scores (ß = 0.27 [95% CI = 0.12, 0.43]), a lower probability of anxiety (prevalence ratio [PR] = 0.71 [95% CI = 0.52, 0.96]), and a lower probability of increased loneliness (PR = 0.62 [95% CI = 0.45, 0.84]). CONCLUSIONS: Pre-pandemic perceived social support was associated with greater psychological resilience and generally better mental well-being during the pandemic. Interventions that improve social support may foster psychological resilience and protect the mental well-being of people who use drugs, especially during periods of social disruption.

Estimation of the Time-Varying Incremental Effect of Low-dose Aspirin on Incidence of Pregnancy.

BACKGROUND: In many research settings, the intervention implied by the average causal effect of a time-varying exposure is impractical or unrealistic, and we might instead prefer a more realistic target estimand. Instead of requiring all individuals to be always exposed versus unexposed, incremental effects quantify the impact of merely shifting each individual's probability of being exposed. METHODS: We demonstrate the estimation of incremental effects in the time-varying setting, using data from the Effects of Aspirin in Gestation and Reproduction trial, which assessed the effect of preconception low-dose aspirin on pregnancy outcomes. Compliance to aspirin or placebo was summarized weekly and was affected by time-varying confounders such as bleeding or nausea. We sought to estimate what the incidence of pregnancy by 26 weeks postrandomization would have been if we shifted each participant's probability of taking aspirin or placebo each week by odds ratios (OR) between 0.30 and 3.00. RESULTS: Under no intervention (OR = 1), the incidence of pregnancy was 77% (95% CI: 74%, 80%). Decreasing women's probability of complying with aspirin had little estimated effect on pregnancy incidence. When we increased women's probability of taking aspirin, estimated incidence of pregnancy increased, from 83% (95% confidence interval [CI] = 79%, 87%) for OR = 2 to 89% (95% CI = 84%, 93%) for OR=3. We observed similar results when we shifted women's probability of complying with a placebo. CONCLUSIONS: These results estimated that realistic interventions to increase women's probability of taking aspirin would have yielded little to no impact on the incidence of pregnancy, relative to similar interventions on placebo.

A Simulation Study Comparing the Performance of Time-Varying Inverse Probability Weighting and G-Computation in Survival Analysis.

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.

Mortality by cause of death during year 1 of the COVID-19 pandemic in a cohort of older adults from Baltimore Maryland who have injected drugs.

BACKGROUND: In 2020, the first year of the COVID-19 pandemic, overdose deaths increased. However, no studies have characterized changes in mortality during the pandemic in a well-characterized cohort of people who use drugs in active follow-up at the time of pandemic onset. DESIGN: We compared all-cause and cause-specific mortality in the first year of the pandemic (Mar-Dec 2020) to the five years preceding (Jan 2015-Feb 2020), among participants in the AIDS Linked to the IntraVenous Experience (ALIVE) study: a community-recruited cohort of adults from Baltimore who have injected drugs. 3510 participants contributed 17,498 person-years [py] of follow-up time. Cause and dates of death were ascertained through the National Death Index. Comparisons were made for the full cohort and within subgroups with potentially differential levels of vulnerability. RESULTS: All-cause mortality in 2020 was 39.6 per 1000 py, as compared to 37.2 per 1000 py pre- pandemic (Adjusted Incidence Rate Ratio = 1.09, 95%: confidence interval: 0.84-1.41). Increases were mostly attributable to chronic disease deaths; injury/poisoning deaths did not increase. No pre-post differences were statistically significant. CONCLUSION: In this exploratory analysis of an older cohort of urban-dwelling adults who have injected drugs, mortality changes during the first year of the pandemic differed from national trends and varied across potentially vulnerable subgroups. More research is needed to understand determinants of increased risk of mortality during the pandemic among subgroups of people who use drugs.

Estimation of the Average Causal Effect in Longitudinal Data With Time-Varying Exposures: The Challenge of Nonpositivity and the Impact of Model Flexibility.

There are important challenges to the estimation and identification of average causal effects in longitudinal data with time-varying exposures. Here, we discuss the difficulty in meeting the positivity condition. Our motivating example is the per-protocol analysis of the Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial. We estimated the average causal effect comparing the incidence of pregnancy by 26 weeks that would have occurred if all women had been assigned to aspirin and complied versus the incidence if all women had been assigned to placebo and complied. Using flexible targeted minimum loss-based estimation, we estimated a risk difference of 1.27% (95% CI: -9.83, 12.38). Using a less flexible inverse probability weighting approach, the risk difference was 5.77% (95% CI: -1.13, 13.05). However, the cumulative probability of compliance conditional on covariates approached 0 as follow-up accrued, indicating a practical violation of the positivity assumption, which limited our ability to make causal interpretations. The effects of nonpositivity were more apparent when using a more flexible estimator, as indicated by the greater imprecision. When faced with nonpositivity, one can use a flexible approach and be transparent about the uncertainty, use a parametric approach and smooth over gaps in the data, or target a different estimand that will be less vulnerable to positivity violations.

Impact of Hepatitis C Treatment Uptake on Cirrhosis and Mortality in Persons Who Inject Drugs : A Longitudinal, Community-Based Cohort Study.

BACKGROUND: Hepatitis C virus (HCV) infection can be cured, and the United States has joined the World Health Organization in calling for HCV elimination by 2030. However, historically low uptake of HCV treatment among people who inject drugs (PWID) threatens HCV elimination and exacerbates social and racial health disparities. OBJECTIVE: To assess whether all-oral HCV treatments were accessed by PWID and reduced liver disease burden and mortality. DESIGN: Community-based, longitudinal cohort study of persons with a history of injection drug use. SETTING: Baltimore, Maryland. PARTICIPANTS: 1323 participants enrolled in the ALIVE (AIDS Linked to the IntraVenous Experience) study from 2006 to 2019 and chronically infected with HCV. MEASUREMENTS: Liver stiffness measures (LSMs) by transient elastography, HCV RNA, and mortality from the National Death Index. RESULTS: Among 1323 persons with evidence of chronic HCV infection at baseline, the median age was 49 years. Most were Black (82%), male (71%), and HIV-negative (66%). The proportion in whom HCV RNA was detected decreased from 100% (by definition) in 2006 to 48% in 2019. Across 10 350 valid LSMs, cirrhosis was detected in 15% of participants in 2006, 19% in 2015, and 8% in 2019. Undetectable HCV RNA was significantly associated with reduced odds of cirrhosis (adjusted odds ratio, 0.28 [95% CI, 0.17 to 0.45]) and reduced all-cause mortality risk (adjusted hazard ratio, 0.54 [CI, 0.38 to 0.77]). LIMITATION: Noninvasive markers of liver fibrosis have not been validated in persons with sustained virologic response. CONCLUSION: Many community-based PWID in Baltimore are receiving HCV treatment, which is associated with sharp decreases in liver disease and mortality. Additional efforts will be needed to reduce residual barriers to treatment and to eliminate HCV as a public health threat for PWID. PRIMARY FUNDING SOURCE: National Institutes of Health.

Illustrating How to Simulate Data From Directed Acyclic Graphs to Understand Epidemiologic Concepts.

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.

The Role of the Natural Course in Causal Analysis.

The average causal effect compares counterfactual outcomes if everyone had been exposed versus if everyone had been unexposed, which can be an unrealistic contrast. Alternatively, we can target effects that compare counterfactual outcomes against the factual outcomes observed in the sample (i.e., we can compare against the natural course). Here, we demonstrate how the natural course can be estimated and used in causal analyses for model validation and effect estimation. Our example is an analysis assessing the impact of taking aspirin on pregnancy, 26 weeks after randomization, in the Effects of Aspirin in Gestation and Reproduction trial (United States, 2006-2012). To validate our models, we estimated the natural course using g-computation and then compared that against the observed incidence of pregnancy. We observed good agreement between the observed and model-based natural courses. We then estimated an effect that compared the natural course against the scenario in which participants assigned to aspirin always complied. If participants had always complied, there would have been 5.0 (95% confidence interval: 2.2, 7.8) more pregnancies per 100 women than was observed. It is good practice to estimate the natural course for model validation when using parametric models, but whether one should estimate a natural course contrast depends on the underlying research questions.

Estimating Associations Between Annual Concentrations of Particulate Matter and Mortality in the United States, Using Data Linkage and Bayesian Maximum Entropy.

BACKGROUND: Exposure to fine particulate matter (PM2.5) is an established risk factor for human mortality. However, previous US studies have been limited to select cities or regions or to population subsets (e.g., older adults). METHODS: Here, we demonstrate how to use the novel geostatistical method Bayesian maximum entropy to obtain estimates of PM2.5 concentrations in all contiguous US counties, 2000-2016. We then demonstrate how one could use these estimates in a traditional epidemiologic analysis examining the association between PM2.5 and rates of all-cause, cardiovascular, respiratory, and (as a negative control outcome) accidental mortality. RESULTS: We estimated that, for a 1 log(µg/m3) increase in PM2.5 concentration, the conditional all-cause mortality incidence rate ratio (IRR) was 1.029 (95% confidence interval [CI]: 1.006, 1.053). This implies that the rate of all-cause mortality at 10 µg/m3 would be 1.020 times the rate at 5 µg/m3. IRRs were larger for cardiovascular mortality than for all-cause mortality in all gender and race-ethnicity groups. We observed larger IRRs for all-cause, nonaccidental, and respiratory mortality in Black non-Hispanic Americans than White non-Hispanic Americans. However, our negative control analysis indicated the possibility for unmeasured confounding. CONCLUSION: We used a novel method that allowed us to estimate PM2.5 concentrations in all contiguous US counties and obtained estimates of the association between PM2.5 and mortality comparable to previous studies. Our analysis provides one example of how Bayesian maximum entropy could be used in epidemiologic analyses; future work could explore other ways to use this approach to inform important public health questions.

Self-Reported Sexually Transmitted Infections After Incarceration in Women with or at Risk for HIV in the United States, 2007-2017.

Background: U.S. women who have been incarcerated report high rates of sexual risk behavior and sexually transmitted infections (STIs). Materials and Methods: We estimated the effect of incarceration on the time to first incident STI in a multicenter cohort of U.S. women with or at risk for HIV. We used marginal structural models to compare time to first self-reported gonorrhea, chlamydia, or trichomonas infection for nonincarcerated women and incarcerated women. Covariates included demographic factors, HIV status, sex exchange, drug/alcohol use, and prior incarceration. Results: Three thousand hundred twenty-four women contributed a median of 4 at-risk years and experienced 213 first incident STI events. The crude incidence of STIs was 3.7 per 100 person-years for incarcerated women and 1.9 per 100 person-years for nonincarcerated women. The weighted hazard ratio for incident STIs was 4.05 (95% confidence interval: 1.61-10.19). Conclusion: Women with or at risk for HIV in the United States who have recently experienced incarceration may be at increased STI risk.

Mortality Among Persons Entering HIV Care Compared With the General U.S. Population : An Observational Study.

BACKGROUND: Understanding 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. OBJECTIVE: To 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. DESIGN: Observational cohort study. SETTING: Thirteen sites from the U.S. North American AIDS Cohort Collaboration on Research and Design. PARTICIPANTS: 82 766 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. MEASUREMENTS: Five-year all-cause mortality, estimated using the Kaplan-Meier estimator of the survival function. RESULTS: Overall 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. LIMITATION: Matching 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. CONCLUSION: Mortality 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 SOURCE: National Institutes of Health.