Existing methods to estimate the prevalence of chronic hepatitis C (HCV) in New York City (NYC) are limited in scope and fail to assess hard-to-reach subpopulations with highest risk such as injecting drug users (IDUs). To address these limitations, we employ a Bayesian multi-parameter evidence synthesis model to systematically combine multiple sources of data, account for bias in certain data sources, and provide unbiased HCV prevalence estimates with associated uncertainty. Our approach improves on previous estimates by explicitly accounting for injecting drug use and including data from high-risk subpopulations such as the incarcerated, and is more inclusive, utilizing ten NYC data sources. In addition, we derive two new equations to allow age at first injecting drug use data for former and current IDUs to be incorporated into the Bayesian evidence synthesis, a first for this type of model. Our estimated overall HCV prevalence as of 2012 among NYC adults aged 20-59 years is 2.78% (95% CI 2.61-2.94%), which represents between 124,900 and 140,000 chronic HCV cases. These estimates suggest that HCV prevalence in NYC is higher than previously indicated from household surveys (2.2%) and the surveillance system (2.37%), and that HCV transmission is increasing among young injecting adults in NYC. An ancillary benefit from our results is an estimate of current IDUs aged 20-59 in NYC: 0.58% or 27,600 individuals.
Generalized additive models (GAMs) have become a leading model class for interpretable machine learning. However, there are many algorithms for training GAMs, and these can learn different or even contradictory models, while being equally accurate. Which GAM should we trust? In this paper, we quantitatively and qualitatively investigate a variety of GAM algorithms on real and simulated datasets. We find that GAMs with high feature sparsity (only using a few variables to make predictions) can miss patterns in the data and be unfair to rare subpopulations. Our results suggest that inductive bias plays a crucial role in what interpretable models learn and that tree-based GAMs represent the best balance of sparsity, fidelity and accuracy and thus appear to be the most trustworthy GAM models. CCS CONCEPTS• Computing methodologies → Model verification and validation.
Understanding how racial information impacts human decision making in online systems is critical in today's world. Prior work revealed that race information of criminal defendants, when presented as a text field, had no significant impact on users' judgements of recidivism [13]. We replicated and extended this work to explore how and when race information influences users' judgements, with respect to the saliency of presentation. Our results showed that adding photos to the race labels had a significant impact on recidivism predictions for users who identified as female, but not for those who identified as male. The race of the defendant also impacted these results, with black defendants being less likely to be predicted to recidivate compared to white defendants. These results have strong implications for how systemdesigners choose to display race information, and cautions researchers to be aware of gender and race effects when using Amazon Mechanical Turk workers.
This economic evaluation analyzes the cost-effectiveness of screening and prevention strategies by genotype among women with Lynch syndrome.
PURPOSE Cancer incidence is rising in low- and middle-income countries, where resource constraints often complicate therapeutic decisions. Here, we perform a cost-effectiveness analysis to identify the optimal adjuvant chemotherapy strategy for patients with stage III colon cancer treated in South African (ZA) public hospitals. METHODS A decision-analytic Markov model was developed to compare lifetime costs and outcomes for patients with stage III colon cancer treated with six adjuvant chemotherapy regimens in ZA public hospitals: fluorouracil, leucovorin, and oxaliplatin for 3 and 6 months; capecitabine and oxaliplatin (CAPOX) for 3 and 6 months; capecitabine for 6 months; and fluorouracil/leucovorin for 6 months. Transition probabilities were derived from clinical trials to estimate risks of toxicity, disease recurrence, and survival. Societal costs and utilities were obtained from literature. The primary outcome was the incremental cost-effectiveness ratio in international dollars (I$) per disability-adjusted life-year (DALY) averted, compared with no therapy, at a willingness-to-pay (WTP) threshold of I$13,006.56. RESULTS CAPOX for 3 months was cost-effective (I$5,381.17 and 5.74 DALYs averted) compared with no adjuvant chemotherapy. Fluorouracil, leucovorin, and oxaliplatin for 6 months was on the efficiency frontier with 5.91 DALYs averted but, with an incremental cost-effectiveness ratio of I$99,021.36/DALY averted, exceeded the WTP threshold. CONCLUSION In ZA public hospitals, CAPOX for 3 months is the cost-effective adjuvant treatment for stage III colon cancer. The optimal strategy in other settings may change according to local WTP thresholds. Decision analytic tools can play a vital role in selecting cost-effective cancer therapeutics in resource-constrained settings.
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