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Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model, which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find that accurate estimation of individual treatment effects is possible even in complex heterogenous settings but that the type of RF approach plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk.

Understanding patterns of multimorbidity in the US older adult population and their relationship with mortality is important for reducing healthcare utilization and improving health. Previous investigations measured multimorbidity as counts of conditions rather than specific combination of conditions. This cross-sectional study with longitudinal mortality follow-up employed latent class analysis (LCA) to develop clinically meaningful subgroups of participants aged 50 and older with different combinations of 13 chronic conditions from the National Health Interview Survey 2002-2014. Mortality linkage with National Death Index was performed through December 2015 for 166,126 participants. Survival analyses were conducted to assess the relationships between LCA classes and all-cause mortality and cause specific mortalities. LCA identified five multimorbidity groups with primary characteristics: \"healthy\" (51.5%), \"age-associated chronic conditions\" (33.6%), \"respiratory conditions\" (7.3%), \"cognitively impaired\" (4.3%) and \"complex cardiometabolic\" (3.2%). Covariate-adjusted survival analysis indicated \"complex cardiometabolic\" class had the highest mortality with a Hazard Ratio (HR) of 5.30, 99.5% CI [4.52, 6.22]; followed by \"cognitively impaired\" class (3.34 [2.93, 3.81]); \"respiratory condition\" class (2.14 [1.87, 2.46]); and \"age-associated chronic conditions\" class (1.81 [1.66, 1.98]). Patterns of multimorbidity classes were strongly associated with the primary underlying cause of death. The \"cognitively impaired\" class reported similar number of conditions compared to the \"respiratory condition\" class but had significantly higher mortality (3.8 vs 3.7 conditions, HR = 1.56 [1.32, 1.85]). We demonstrated that LCA method is effective in classifying clinically meaningful multimorbidity subgroup. Specific combinations of conditions including cognitive impairment and depressive symptoms have a substantial detrimental impact on the mortality of older adults. The numbers of chronic conditions experienced by older adults is not always proportional to mortality risk. Our findings provide valuable information for identifying high risk older adults with multimorbidity to facilitate early intervention to treat chronic conditions and reduce mortality.

Florida had the third greatest number of new HIV diagnoses in the United States in 2020. This cross-sectional study aimed to assess HIV education and perceptions among diverse populations in South Florida to enhance public health community outreach efforts. Specifically, it investigated how HIV knowledge and perceptions vary based on race, primary language, and country of origin. Cross-sectional surveys were administered at five South Florida health fair locations to evaluate understanding of HIV transmission, strategies for prevention and treatment, and stigma among those who accepted and declined free HIV testing. We analyzed survey data using chi-square tests with an alpha level of 0.05. Of the 173 respondents, 149 underwent HIV testing, while 24 declined. Out of all respondents, 20.8% identified as Black (n = 36), 29.5% White non-Hispanic (n = 51), and 43.9% White Hispanic (n = 76). Over half of all respondents were foreign born (59%). Most participants knew HIV can be spread by injection drug usage (98.8%) and unprotected sex (97.7%). Incorrect answers included that HIV could be spread by mosquito bites (27.2%), kissing a person living with HIV (26.6%), and sharing a drink with a person living with HIV (19.7%). Transmission knowledge was significantly associated with race (χ²(2, N = 163) = 8.78, p = .012), with 26.3% Black (n = 10), 18.7% White Hispanic (n = 14), and 4.0% of White non-Hispanic participants (n = 2) reporting inadequate transmission knowledge. Familiarity with PrEP and/or PEP was also associated with race (χ²(2, N = 163) = 13.27, p = .001), as White Hispanic participants reported the highest lack of familiarity (84.2%), and Spanish-speaking participants reported half the PrEP/PEP familiarity as their English-speaking counterparts (p < 0.0001). Transmission knowledge was significantly low among Black and White Hispanic participants, while PrEP/PEP knowledge was uniquely low among White Hispanic and Spanish-speaking participants, reinforcing the need for improved education among these populations.

Several large-scale, pragmatic clinical trials on opioid use disorder (OUD) have been completed in the National Drug Abuse Treatment Clinical Trials Network (CTN). However, the resulting data have not been harmonized between the studies to compare the patient characteristics. This paper provides lessons learned from a large-scale harmonization process that are critical for all biomedical researchers collecting new data and those tasked with combining datasets. We harmonized data from multiple domains from CTN-0027 (N = 1269), which compared methadone and buprenorphine at federally licensed methadone treatment programs; CTN-0030 (N = 653), which recruited patients who used predominantly prescription opioids and were treated with buprenorphine; and CTN-0051 (N = 570), which compared buprenorphine and extended-release naltrexone (XR-NTX) and recruited from inpatient treatment facilities. Patient-level data were harmonized and a total of 23 database tables, with meticulous documentation, covering more than 110 variables, along with three tables with “meta-data” about the study design and treatment arms, were created. Domains included: social and demographic characteristics, medical and psychiatric history, self-reported drug use details and urine drug screening results, withdrawal, and treatment drug details. Here, we summarize the numerous issues with the organization and fidelity of the publicly available data which were noted and resolved, and present results on patient characteristics across the three trials and the harmonized domains, respectively. A systematic harmonization of OUD clinical trial data can be accomplished, despite heterogeneous data coding and classification procedures, by standardizing commonly assessed characteristics. Similar methods, embracing database normalization and/or “tidy” data, should be used for future datasets in other substance use disorder clinical trials.

The efficacy of treatments for substance use disorders (SUD) is tested in clinical trials in which participants typically provide urine samples to detect whether the person has used certain substances via urine drug screenings (UDS). UDS data form the foundation of treatment outcome assessment in the vast majority of SUD clinical trials. However, existing methods to calculate treatment outcomes are not standardized, impeding comparability between studies and prohibiting reproducibility of results. We extended the concept of a binary UDS variable to multiple categories: \"+\" [positive for substance(s) of interest], \"-\" [negative for substance(s)], \"o\" [patient failed to provide sample], \"*\" [inconclusive or mixed results], and \"_\" [no specimens required per study design]. This construct can be used to create a standardized and sufficient representation of UDS datastreams and sufficiently collapses longitudinal records into a single, compact \"word\", which preserves all information contained in the original data. We developed the R software package CTNote (available on CRAN) as a tool to enable computers to parse these \"words\". The software package contains five groups of routines: detect a substance use pattern, account for a specific trial protocol, handle missing UDS data, measure the longest period of consecutive behavior, and count substance use events. Executing permutations of these routines result in algorithms which can define SUD clinical trial endpoints. As examples, we provide three algorithms to define primary endpoints from seminal SUD clinical trials. Representing substance use patterns as a \"word\" allows researchers and clinicians an \"at a glance\" assessment of participants' responses to treatment over time. Further, machine readable use pattern summaries are a standardized method to calculate treatment outcomes and are therefore useful to all future SUD clinical trials. We discuss some caveats when applying this data summarization technique in practice and areas of future study.

Background The accessibility of pharmacies has been associated with overall health and wellbeing. Past studies have suggested that low income and racial minority communities are underserved by pharmacies. However, the literature is inconsistent in finding links between area-level income or racial and ethnic composition and access to pharmacies. Here we aim to assess area-level spatial access to pharmacies across New York State (NYS), hypothesizing that Census Tracts with higher poverty rates and higher percentages of Black and Hispanic residents would have lower spatial access. Methods The population weighted mean shortest road network distance (PWMSD) to a pharmacy in 2018 was calculated for each Census Tract in NYS. This statistic was calculated from the shortest road network distance to a pharmacy from the centroid of each Census block within a tract, with the mean across census blocks weighted by the population of the census block. Cross-sectional analyses were conducted to assess links between Tract-level socio demographic characteristics and Tract-level PWMSD to a pharmacy. Results Overall the mean PWMSD to a pharmacy across Census tracts in NYS was 2.07 Km (SD = 3.35, median 0.85 Km). Shorter PWMSD to a pharmacy were associated with higher Tract-level % poverty, % Black/African American (AA) residents, and % Hispanic/Latino residents and with lower Tract-level % of residents with a college degree. Compared to tracts in the lowest quartile of % Black/AA residents, tracts in the highest quartile had a 70.7% (95% CI 68.3–72.9%) shorter PWMSD to a pharmacy. Similarly, tracts in the highest quartile of % poverty had a 61.3% (95% CI 58.0-64.4%) shorter PWMSD to a pharmacy than tracts in the lowest quartile. Conclusion The analyses show that tracts in NYS with higher racial and ethnic minority populations and higher poverty rates have higher spatial access to pharmacies.

The interrelatedness of mental health status and HIV-related outcomes is well-documented. However, the long-term relationship between psychological distress and health outcomes among persons with HIV, co-diagnosed with substance use disorders (SUD), is understudied. We measured psychological distress among men and women with HIV who use drugs, using a low-burden instrument, and tested its effect, longitudinally, on HIV and substance use-related outcomes. Recently hospitalized, adult men and women co-diagnosed with HIV and SUD were surveyed for psychological distress, using the 18-item Brief Symptom Inventory (BSI-18). We tested the short-term (6 months) and long-term (12 months) effect of psychological distress on HIV-related and substance use-related outcomes. Psychological distress predicted higher engagement with SUD treatment and higher substance use, which decreased rapidly, and significantly, over time. No significant relationship was found between psychological distress and HIV viral load suppression. Using brief and easy to administer measures, early detection of psychological distress among persons with HIV and SUD, could avert negative, long-term health consequences—warranting further investigation of interventions that address mental health challenges faced by this population.

Dynamic HIV transmission models can provide evidence-based guidance on optimal combination implementation strategies to treat and prevent HIV/AIDS. However, these models can be extremely data intensive, and the availability of good-quality data characterizing regional microepidemics varies substantially within and across countries. We aim to provide a comprehensive and transparent description of an evidence synthesis process and reporting framework employed to populate and calibrate a dynamic, compartmental HIV transmission model for six US cities. We executed a mixed-method evidence synthesis strategy to populate model parameters in six categories: (i) initial HIV-negative and HIV-infected populations; (ii) parameters used to calculate the probability of HIV transmission; (iii) screening, diagnosis, treatment and HIV disease progression; (iv) HIV prevention programs; (v) the costs of medical care; and (vi) health utility weights for each stage of HIV disease progression. We identified parameters that required city-specific data and stratification by gender, risk group and race/ethnicity a priori and sought out databases for primary analysis to augment our evidence synthesis. We ranked the quality of each parameter using context- and domain-specific criteria and verified sources and assumptions with our scientific advisory committee. To inform the 1,667 parameters needed to populate our model, we synthesized evidence from 59 peer-reviewed publications and 24 public health and surveillance reports and executed primary analyses using 11 data sets. Of these 1,667 parameters, 1,517 (91%) were city-specific and 150 (9%) were common for all cities. Notably, 1,074 (64%), 201 (12%) and 312 (19%) parameters corresponded to categories (i), (ii) and (iii), respectively. Parameters ranked as best- to moderate-quality evidence comprised 39% of the common parameters and ranged from 56%-60% across cities for the city-specific parameters. We identified variation in parameter values across cities as well as within cities across risk and race/ethnic groups. Better integration of modelling in decision making can be achieved by systematically reporting on the evidence synthesis process that is used to populate models, and by explicitly assessing the quality of data entered into the model. The effective communication of this process can help prioritize data collection of the most informative components of local HIV prevention and care services in order to reduce decision uncertainty and strengthen model conclusions.

Background Facilitating access to HIV prevention and treatment is imperative in Miami-Dade County (MDC), a U.S. HIV epicenter. With COVID-19, disruptions to these services have occurred, leading HIV organizations to innovate and demonstrate resilience. This study documented COVID-19 related disruptions and resilient innovations in HIV services within MDC. Methods This mixed methods cross-sectional study included HIV test counselors in MDC. In the quantitative component ( N =106), participants reported COVID-19 impacts on HIV service delivery. Data visualization examined patterns within organizations and throughout the study period. Generalized estimating equation modeling examined differences in service disruptions and innovations. In the qualitative component, participants ( N =20) completed interviews regarding COVID-19 impacts on HIV services. Rapid qualitative analysis was employed to analyze interviews. Results Quantitative data showed that innovations generally matched or outpaced disruptions, demonstrating resilience on HIV service delivery during COVID-19. HIV testing (36%, 95%CI[28%, 46%]) and STI testing (42%, 95%CI[33%, 52%]) were most likely to be disrupted. Sexual/reproductive health (45%, 95%CI[35%, 55%]), HIV testing (57%, 95%CI[47%,66%]), HIV case management (51%, 95%CI[41%, 60%]), PrEP initiation (47%, 95%CI[37%,57%]), and STI testing (47%, 95%CI[37%, 57%]) were most likely to be innovated. Qualitative analysis revealed three orthogonal themes related to 1) disruptions (with five sub-components), 2) resilient innovations (with four sub-components), and 3) emerging and ongoing health disparities. Conclusions HIV organizations faced service disruptions during COVID-19 while also meaningfully innovating. Our findings point to potential changes in policy and practice that could be maintained beyond the immediate impacts of COVID-19 to enhance the resilience of HIV services. Aligning with the US Ending the HIV Epidemic Plan and the National Strategy for HIV/AIDS, capitalizing on the observed innovations would facilitate improved HIV-related health services for people living in MDC and beyond.

Background Blood-borne viral infections, such as HIV and hepatitis C (HCV), are common infections among people who inject drugs (PWID). This study aims to determine the prevalence of HIV and HCV infection among PWID accessing the first legal syringe services program (SSP) in the state of Florida, along with examining baseline correlates of HIV and HCV infection. Methods Baseline behavioral enrollment assessments of 837 participants accessing an SSP for the first time were analyzed. Patients self-reporting or testing HIV or HCV positive at the enrollment visit were included. Socio-demographic, drug use, and injection-related risk behaviors in the last 30 days were compared across groups defined by all combinations of HIV and HCV serostatus. Bivariate and multivariable logistic regression models were used to assess correlates of baseline HCV and HIV infection independently. Results Overall prevalence for HCV and HIV infection were 44.4% and 10.2%, respectively. After adjusting for confounders, the most significant correlates of baseline HCV infection were age (aOR = 1.01), lower education level (aOR = 1.13), currently homeless (aOR = 1.16), injecting more than seven times a day (aOR = 1.14), reusing syringes (aOR = 1.18), and sharing injection equipment (aOR = 1.13). The most significant predictors of baseline HIV infection were age (aOR = 1.01), non-Hispanic Black race (aOR = 1.28), Hispanic ethnicity (aOR = 1.12), gay or bisexual orientation (aOR = 1.22), and methamphetamine injection (aOR = 1.22). In addition, heroin injection (aOR = 0.92) was significantly associated with a lower odds of HIV infection. Discussion/conclusion Baseline behavioral predictors differed between HIV infection and HCV infection among participants accessing syringe services. Understanding the risk factors associated with each infection should be considered when developing additional harm reduction interventions tailored for diverse PWID populations served at SSPs.