Objective

The costs of medical care for people with HIV/AIDS (PWH) vary substantially across demographic groups, stages of disease progression and regionally across the United States. We aimed to estimate medical costs for PWH and examine the heterogeneity in costs within key patient groups typically distinguished in cost-effectiveness analyses.

Design

Retrospective cohort study using health administrative databases for diagnosed PWH in care at 17 HIV Research Network sites across the United States.

Methods

We estimated mean quarterly costs for key patient groups using multivariable generalized linear mixed effects models. We used quantile regression to highlight differences in the effect of covariates within each patient group (difference between covariate estimates at the mean versus the 90th percentile of quarterly costs), identifying covariates with a larger effect among the highest cost PWH, or generating greater uncertainty in mean cost estimates.

Results

Our sample included 40 022 patients with a median age of 39 years. Mean quarterly costs were highest for people who inject drugs with advanced disease progression and for PWH on antiretroviral treatment (ART). Within patient groups, we found the most heterogeneity at different levels of resource use for PWH on ART and PWH off ART with CD4 cell counts less than 200 cells/μl, people who inject drugs, as well as PWH in the South.

Conclusion

The study quantifies heterogeneity in costs both across and within key PWH patient groups. Our results highlight the need for sensitivity analysis on cost estimates and may inform decisions on model structure in cost-effectiveness analyses on HIV/AIDS treatment and prevention strategies.

Objectives

Illegal drug use continues to be a major threat to community health and safety. We used international drug surveillance databases to assess the relationship between multiple long-term estimates of illegal drug price and purity.

Design

We systematically searched for longitudinal measures of illegal drug supply indicators to assess the long-term impact of enforcement-based supply reduction interventions.

Setting

Data from identified illegal drug surveillance systems were analysed using an a priori defined protocol in which we sought to present annual estimates beginning in 1990. Data were then subjected to trend analyses.

Main outcome measures

Data were obtained from government surveillance systems assessing price, purity and/or seizure quantities of illegal drugs; systems with at least 10 years of longitudinal data assessing price, purity/potency or seizures were included.

Results

We identified seven regional/international metasurveillance systems with longitudinal measures of price or purity/potency that met eligibility criteria. In the USA, the average inflation-adjusted and purity-adjusted prices of heroin, cocaine and cannabis decreased by 81%, 80% and 86%, respectively, between 1990 and 2007, whereas average purity increased by 60%, 11% and 161%, respectively. Similar trends were observed in Europe, where during the same period the average inflation-adjusted price of opiates and cocaine decreased by 74% and 51%, respectively. In Australia, the average inflation-adjusted price of cocaine decreased 14%, while the inflation-adjusted price of heroin and cannabis both decreased 49% between 2000 and 2010. During this time, seizures of these drugs in major production regions and major domestic markets generally increased.

Conclusions

With few exceptions and despite increasing investments in enforcement-based supply reduction efforts aimed at disrupting global drug supply, illegal drug prices have generally decreased while drug purity has generally increased since 1990. These findings suggest that expanding efforts at controlling the global illegal drug market through law enforcement are failing.

Background. Heterogeneity in HIV microepidemics across US cities necessitates locally oriented, combination implementation strategies to prioritize resources. We calibrated and validated a dynamic, compartmental HIV transmission model to establish a status quo treatment scenario, holding constant current levels of care for 6 US cities. Methods. Built off a comprehensive evidence synthesis, we adapted and extended a previously published model to replicate the transmission, progression, and clinical care for each microepidemic. We identified a common set of 17 calibration targets between 2012 and 2015 and used the Morris method to select the most influential parameters for calibration. We then applied the Nelder-Mead algorithm to iteratively calibrate the model to generate 2000 best-fitting parameter sets. Finally, model projections were internally validated with a series of robustness checks and externally validated against published estimates of HIV incidence, while the face validity of 25-year projections was assessed by a Scientific Advisory Committee (SAC). Results. We documented our process for model development, calibration, and validation to maximize its transparency and reproducibility. The projected outcomes demonstrated a good fit to calibration targets, with a mean goodness-of-fit ranging from 0.0174 (New York City [NYC]) to 0.0861 (Atlanta). Most of the incidence predictions were within the uncertainty range for 5 of the 6 cities (ranging from 21% [Miami] to 100% [NYC]), demonstrating good external validity. The face validity of the long-term projections was confirmed by our SAC, showing that the incidence would decrease or remain stable in Atlanta, Los Angeles, NYC, and Seattle while increasing in Baltimore and Miami. Discussion. This exercise provides a basis for assessing the incremental value of further investments in HIV combination implementation strategies tailored to urban HIV microepidemics.

Born out of a necessity for fiscal sustainability, simulation modeling is playing an increasingly prominent role in setting priorities for combination implementation strategies for HIV treatment and prevention globally. The design of a model and the data inputted into it are central factors in ensuring credible inferences. We executed a narrative review of a set of dynamic HIV transmission models to comprehensively synthesize and compare the structural design and the quality of evidence used to support each model. We included 19 models representing both generalized and concentrated epidemics, classified as compartmental, agent-based, individual-based microsimulation or hybrid in our review. We focused on four structural components (population construction; model entry, exit and HIV care engagement; HIV disease progression; and the force of HIV infection), and two analytical components (model calibration/validation; and health economic evaluation, including uncertainty analysis). While the models we reviewed focused on a variety of individual interventions and their combinations, their structural designs were relatively homogenous across three of the four focal components, with key structural elements influenced by model type and epidemiological context. In contrast, model entry, exit and HIV care engagement tended to differ most across models, with some health system interactions-particularly HIV testing-not modeled explicitly in many contexts. The quality of data used in the models and the transparency with which the data was presented differed substantially across model components. Representative and high-quality data on health service delivery were most commonly not accessed or were unavailable. The structure of an HIV model should ideally fit its epidemiological context and be able to capture all efficacious treatment and prevention services relevant to a robust combination implementation strategy. Developing standardized guidelines on evidence syntheses for health economic evaluation would improve transparency and help prioritize data collection to reduce decision uncertainty.

Background

California treats the largest population of opioid dependent individuals in the USA and is among a small group of states that applies regulations for opioid treatment that are more stringent than existing federal regulations. We aim to characterize changes in patient characteristics and treatment utilization over time, and identify determinants of successful completion of detoxification and MMT retention in repeated attempts.

Methods

State-wide administrative data was obtained from California Outcome Measurement System during the period: January 1st, 1991-March 31st, 2012. Short-term detoxification treatment and long-term maintenance treatment, primarily with methadone, was available to study participants. Mixed effects regression models were used to define determinants of successful completion of the detoxification treatment protocol (as classified by treatment staff) and duration of maintenance treatment.

Results

The study sample consisted of 237,709 unique individuals and 885,971 treatment episodes; 837% were detoxification treatment episodes in 1994, dropping to 40.5% in 2010. Among individuals accessing only detoxification, the adjusted odds of success declined with each successive attempt (vs. 1st attempt: 2nd: OR: 0.679; 95% CI (0.610, 0.755); 3rd: 0.557 (0.484, 0.641); 4th: 0.526 (0.445, 0.622); 5th: 0.407 (0.334, 0.497); ≥6th: 0.339 (0.288, 0.399). For those ever accessing maintenance treatment, later subsequent attempts were longer in duration, and those with two or more prior attempts at detoxification had marginally longer subsequent maintenance episodes (hazard ratio: 0.97; 95% CI: 0.95, 0.99). Finally, only 10.9% of all detoxification episodes were followed by admission into maintenance treatment within 14 days.

Conclusions

This study has revealed high rates of detoxification treatment for opioid dependence in California throughout the study period, and decreasing odds of success in repeated attempts at detoxification.

Purpose

We aimed to estimate health state utility values (HSUVs) for the key health states found in opioid use disorder (OUD) cost-effectiveness models in the published literature.

Methods

Data obtained from six trials representing 1,777 individuals with OUD. We implemented mapping algorithms to harmonize data from different measures of quality of life (the SF-12 Versions 1 and 2 and the EQ-5D-3 L). We performed a regression analysis to quantify the relationship between HSUVs and the following variables: days of extra-medical opioid use in the past 30 days, injecting behaviors, treatment with medications for OUD, HIV status, and age. A secondary analysis explored the impact of opioid withdrawal symptoms.

Results

There were statistically significant reductions in HSUVs associated with extra-medical opioid use (-0.002 (95% CI [-0.003,-0.0001]) to -0.003 (95% CI [-0.005,-0.002]) per additional day of heroin or other opiate use, respectively), drug injecting compared to not injecting (-0.043 (95% CI [-0.079,-0.006])), HIV-positive diagnosis compared to no diagnosis (-0.074 (95% CI [-0.143,-0.005])), and age (-0.001 per year (95% CI [-0.003,-0.0002])). Parameters associated with medications for OUD treatment were not statistically significant after controlling for extra-medical opioid use (0.0131 (95% CI [-0.0479,0.0769])), in line with prior studies. The secondary analysis revealed that withdrawal symptoms are a fundamental driver of HSUVs, with predictions of 0.817 (95% CI [0.768, 0.858]), 0.705 (95% CI [0.607, 0.786]), and 0.367 (95% CI [0.180, 0.575]) for moderate, severe, and worst level of symptoms, respectively.

Conclusion

We observed HSUVs for OUD that were higher than those from previous studies that had been conducted without input from people living with the condition.

In recent years, evidence has accumulated regarding the ability of HAART to prevent HIV transmission. Early supportive evidence was derived from observational, ecological and population-based studies. More recently, a randomized clinical trial showed that immediate use of HAART led to a 96% decrease in HIV transmission events within HIV serodiscordant heterosexual couples. However, the generalizability of the effect of HAART, and the population-level impact on HIV transmission continues to generate substantial debate. We, therefore, conducted a review of the evidence regarding the preventive effect of HAART on HIV transmission within the context of the Bradford Hill criteria for causality. Taken together, we find the accumulated evidence supporting HIV treatment as prevention meets each of the Bradford Hill criteria for causality. We conclude that the opportunity cost of inaction while waiting for additional evidence on the generalizability of effect in other risk groups is too high. Efforts should be redoubled to mobilize the financial capital and political will to optimize implementation of HIV Treatment as Prevention strategies on a wide scale.

Aim

The aim was to estimate transitions between periods in and out of treatment, incarceration, and legal supervision, for prescription opioid (PO) and heroin users.

Methods

We captured all individuals admitted for the first time for publicly funded treatment for opioid use disorder (OUD) in California (2006 to 2010) with linked mortality and criminal justice data. We used Cox proportional hazards and competing risks models to assess the effect of primary PO use (v. heroin) on the hazard of transitioning among 5 states: (1) opioid detoxification treatment; (2) opioid agonist treatment (OAT); (3) legal supervision (probation or parole); (4) incarceration (jail or prison); and (5) out-of-treatment. Transitions were conditional on survival, and death was modeled as an absorbing state.

Results

Both primary PO (n = 11,733) and heroin (n = 19,926) users spent most of their median 2.3 y of observation out of treatment. Primary PO users were significantly younger (median age 30 v. 34 y), and a higher percentage were female (43.1% v. 31.5%; P < 0.001), white (74.6% v. 63.1%; P < 0.001), and had completed high school (31.8% v. 18.9%; P < 0.001). When compared to primary heroin users, PO users had a higher hazard of transitioning from detoxification to OAT (Hazard Ratio (HR), 1.65; 95% CI, 1.54 to 1.77), and had a lower hazard of transitioning from out-of-treatment to either detoxification (0.75 [0.70, 0.81]) or OAT (0.90 [0.85, 0.96]).

Conclusion

Our findings can be applied directly in state transition modeling to improve the validity of health economic evaluations. Although PO users tended to remain in treatment for longer durations than heroin users, they also tended to remain out of treatment for longer after transitioning to an out-of-treatment state. Despite the proven effectiveness of time-unlimited treatment, individuals with OUD spend most of their time out of treatment.

: Using a dynamic HIV transmission model calibrated for six USA cities, we projected HIV incidence from 2020 to 2040 and estimated whether an established UNAIDS HIV epidemic control target could be met under ideal implementation of optimal combination strategies previously defined for each city. Four of six cities (Atlanta, Baltimore, New York City and Seattle) were projected to achieve epidemic control by 2040 and we identified differences in reaching epidemic control across racial/ethnic groups.

Background. Heterogeneity in HIV microepidemics across US cities necessitates locally oriented, combination implementation strategies to prioritize resources. We calibrated and validated a dynamic, compartmental HIV transmission model to establish a status quo treatment scenario, holding constant current levels of care for 6 US cities. Methods. Built off a comprehensive evidence synthesis, we adapted and extended a previously published model to replicate the transmission, progression, and clinical care for each microepidemic. We identified a common set of 17 calibration targets between 2012 and 2015 and used the Morris method to select the most influential parameters for calibration. We then applied the Nelder-Mead algorithm to iteratively calibrate the model to generate 2000 best-fitting parameter sets. Finally, model projections were internally validated with a series of robustness checks and externally validated against published estimates of HIV incidence, while the face validity of 25-year projections was assessed by a Scientific Advisory Committee (SAC). Results. We documented our process for model development, calibration, and validation to maximize its transparency and reproducibility. The projected outcomes demonstrated a good fit to calibration targets, with a mean goodness-of-fit ranging from 0.0174 (New York City [NYC]) to 0.0861 (Atlanta). Most of the incidence predictions were within the uncertainty range for 5 of the 6 cities (ranging from 21% [Miami] to 100% [NYC]), demonstrating good external validity. The face validity of the long-term projections was confirmed by our SAC, showing that the incidence would decrease or remain stable in Atlanta, Los Angeles, NYC, and Seattle while increasing in Baltimore and Miami. Discussion. This exercise provides a basis for assessing the incremental value of further investments in HIV combination implementation strategies tailored to urban HIV microepidemics.