Background The total population health benefits and costs of HIV preexposure prophylaxis (PrEP) for people who inject drugs (PWID) in the United States are unclear. Objective To evaluate the cost-effectiveness and optimal delivery conditions of PrEP for PWID. Design Empirically calibrated dynamic compartmental model. Data Sources Published literature and expert opinion. Target Population Adult U.S. PWID. Time Horizon 20 years and lifetime. Intervention PrEP alone, PrEP with frequent screening (PrEP+screen), and PrEP+screen with enhanced provision of antiretroviral therapy (ART) for individuals who become infected (PrEP+screen+ART). All scenarios are considered at 25% coverage. Outcome Measures Infections averted, deaths averted, change in HIV prevalence, discounted costs (in 2015 U.S. dollars), discounted quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. Results of Base-Case Analysis PrEP+screen+ART dominates other strategies, averting 26 700 infections and reducing HIV prevalence among PWID by 14% compared with the status quo. Achieving these benefits costs $253 000 per QALY gained. At current drug prices, total expenditures for PrEP+screen+ART could be as high as $44 billion over 20 years. Results of Sensitivity Analysis Cost-effectiveness of the intervention is linear in the annual cost of PrEP and is dependent on PrEP drug adherence, individual transmission risks, and community HIV prevalence. Limitation Data on risk stratification and achievable PrEP efficacy levels for U.S. PWID are limited. Conclusion PrEP with frequent screening and prompt treatment for those who become infected can reduce HIV burden among PWID and provide health benefits for the entire U.S. population, but, at current drug prices, it remains an expensive intervention both in absolute terms and in cost per QALY gained. Primary Funding Source National Institute on Drug Abuse.
BackgroundRising atmospheric carbon dioxide concentrations are anticipated to decrease the zinc and iron concentrations of crops. The associated disease burden and optimal mitigation strategies remain unknown. We sought to understand where and to what extent increasing carbon dioxide concentrations may increase the global burden of nutritional deficiencies through changes in crop nutrient concentrations, and the effects of potential mitigation strategies.Methods and findingsFor each of 137 countries, we incorporated estimates of climate change, crop nutrient concentrations, dietary patterns, and disease risk into a microsimulation model of zinc and iron deficiency. These estimates were obtained from the Intergovernmental Panel on Climate Change, US Department of Agriculture, Statistics Division of the Food and Agriculture Organization of the United Nations, and Global Burden of Disease Project, respectively. In the absence of increasing carbon dioxide concentrations, we estimated that zinc and iron deficiencies would induce 1,072.9 million disability-adjusted life years (DALYs) globally over the period 2015 to 2050 (95% credible interval [CrI]: 971.1–1,167.7). In the presence of increasing carbon dioxide concentrations, we estimated that decreasing zinc and iron concentrations of crops would induce an additional 125.8 million DALYs globally over the same period (95% CrI: 113.6–138.9). This carbon-dioxide-induced disease burden is projected to disproportionately affect nations in the World Health Organization’s South-East Asia and African Regions (44.0 and 28.5 million DALYs, respectively), which already have high existing disease burdens from zinc and iron deficiencies (364.3 and 299.5 million DALYs, respectively), increasing global nutritional inequalities. A climate mitigation strategy such as the Paris Agreement (an international agreement to keep global temperatures within 2°C of pre-industrial levels) would be expected to avert 48.2% of this burden (95% CrI: 47.8%–48.5%), while traditional public health interventions including nutrient supplementation and disease control programs would be expected to avert 26.6% of the burden (95% CrI: 23.8%–29.6%). Of the traditional public health interventions, zinc supplementation would be expected to avert 5.5%, iron supplementation 15.7%, malaria mitigation 3.2%, pneumonia mitigation 1.6%, and diarrhea mitigation 0.5%. The primary limitations of the analysis include uncertainty regarding how food consumption patterns may change with climate, how disease mortality rates will change over time, and how crop zinc and iron concentrations will decline from those at present to those in 2050.ConclusionsEffects of increased carbon dioxide on crop nutrient concentrations are anticipated to exacerbate inequalities in zinc and iron deficiencies by 2050. Proposed Paris Agreement strategies are expected to be more effective than traditional public health measures to avert the increased inequality.
Background. Network meta-analyses (NMAs) that compare treatments for a given condition allow physicians to identify which treatments have higher or lower probabilities of reducing the risks of disease complications or increasing the risks of treatment side effects. Translating these data into personalized treatment plans requires integration of NMA data with patient-specific pretreatment risk estimates and preferences regarding treatment objectives and acceptable risks. Methods. We introduce a modeling framework to integrate data probabilistically from NMAs with data on individualized patient risk estimates for disease outcomes, treatment preferences (such as willingness to incur greater side effects for increased life expectancy), and risk preferences. We illustrate the modeling framework by creating personalized plans for antipsychotic drug treatment and evaluating their effectiveness and cost-effectiveness. Results. Compared with treating all patients with the drug that yields the greatest quality-adjusted life-years (QALYs) on average (amisulpride), personalizing the selection of antipsychotic drugs for schizophrenia patients over the next 5 years would be expected to yield 0.33 QALYs (95% credible interval [crI]: 0.30–0.37) per patient at an incremental cost of $4849/QALY gained (95% crI: dominant–$12,357), versus 0.29 and 0.04 QALYs per patient when accounting for only risks or preferences, respectively, but not both. Limitations. The analysis uses a linear, additive utility function to reflect patient treatment preferences and does not consider potential variations in patient time discounting. Conclusions. Our modeling framework rigorously computes what physicians normally have to do mentally. By integrating 3 key components of personalized medicine—evidence on efficacy, patient risks, and patient preferences—the modeling framework can provide personalized treatment decisions to improve patient health outcomes.
Background Personalizing medical treatments based on patient-specific risks and preferences can improve patient health. However, models to support personalized treatment decisions are often complex and difficult to interpret, limiting their clinical application. Methods We present a new method, using machine learning to create meta-models, for simplifying complex models for personalizing medical treatment decisions. We consider simple interpretable models, interpretable ensemble models, and noninterpretable ensemble models. We use variable selection with a penalty for patient-specific risks and/or preferences that are difficult, risky, or costly to obtain. We interpret the meta-models to the extent permitted by their model architectures. We illustrate our method by applying it to simplify a previously developed model for personalized selection of antipsychotic drugs for patients with schizophrenia. Results The best simplified interpretable, interpretable ensemble, and noninterpretable ensemble models contained at most half the number of patient-specific risks and preferences compared with the original model. The simplified models achieved 60.5% (95% credible interval [crI]: 55.2–65.4), 60.8% (95% crI: 55.5–65.7), and 83.8% (95% crI: 80.8–86.6), respectively, of the net health benefit of the original model (quality-adjusted life-years gained). Important variables in all models were similar and made intuitive sense. Computation time for the meta-models was orders of magnitude less than for the original model. Limitations The simplified models share the limitations of the original model (e.g., potential biases). Conclusions Our meta-modeling method is disease- and model- agnostic and can be used to simplify complex models for personalization, allowing for variable selection in addition to improved model interpretability and computational performance. Simplified models may be more likely to be adopted in clinical settings and can help improve equity in patient outcomes.
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