Increasing prevalence of cirrhosis among insured adults in the United States, 2012-2018.

BACKGROUND: Liver cirrhosis is a chronic disease that is known as a "silent killer" and its true prevalence is difficult to describe. It is imperative to accurately characterize the prevalence of cirrhosis because of its increasing healthcare burden. METHODS: In this retrospective cohort study, trends in cirrhosis prevalence were evaluated using administrative data from one of the largest national health insurance providers in the US. (2011-2018). Enrolled adult (≥18-years-old) patients with cirrhosis defined by ICD-9 and ICD-10 were included in the study. The primary outcome measured in the study was the prevalence of cirrhosis 2011-2018. RESULTS: Among the 371,482 patients with cirrhosis, the mean age was 62.2 (±13.7) years; 53.3% had commercial insurance and 46.4% had Medicare Advantage. The most frequent cirrhosis etiologies were alcohol-related (26.0%), NASH (20.9%) and HCV (20.0%). Mean time of follow-up was 725 (±732.3) days. The observed cirrhosis prevalence was 0.71% in 2018, a 2-fold increase from 2012 (0.34%). The highest prevalence observed was among patients with Medicare Advantage insurance (1.67%) in 2018. Prevalence increased in each US. state, with Southern states having the most rapid rise (2.3-fold). The most significant increases were observed in patients with NASH (3.9-fold) and alcohol-related (2-fold) cirrhosis. CONCLUSION: Between 2012-2018, the prevalence of liver cirrhosis doubled among insured patients. Alcohol-related and NASH cirrhosis were the most significant contributors to this increase. Patients living in the South, and those insured by Medicare Advantage also have disproportionately higher prevalence of cirrhosis. Public health interventions are important to mitigate this concerning trajectory of strain to the health system.

Using measures of race to make clinical predictions: Decision making, patient health, and fairness.

The use of race measures in clinical prediction models is contentious. We seek to inform the discourse by evaluating the inclusion of race in probabilistic predictions of illness that support clinical decision making. Adopting a static utilitarian framework to formalize social welfare, we show that patients of all races benefit when clinical decisions are jointly guided by patient race and other observable covariates. Similar conclusions emerge when the model is extended to a two-period setting where prevention activities target systemic drivers of disease. We also discuss non-utilitarian concepts that have been proposed to guide allocation of health care resources.

Patient-centered appraisal of race-free clinical risk assessment.

Until recently, there has been a consensus that clinicians seeking to assess patient risks of illness should condition risk assessments on all observed patient covariates with predictive power. The broad idea is that knowing more about patients enables more accurate predictions of their health risks and, hence, better clinical decisions. This consensus has recently unraveled with respect to a specific covariate, namely race. There have been increasing calls for race-free risk assessment, arguing that using race to predict health risks contributes to racial disparities and inequities in health care. In some medical fields, leading institutions have recommended race-free risk assessment. An important open question is how race-free risk assessment would affect the quality of clinical decisions. Considering the matter from the patient-centered perspective of medical economics yields a disturbing conclusion: Race-free risk assessment would harm patients of all races.

Embracing Uncertainty: The Value of Partial Identification in Public Health and Clinical Research.

INTRODUCTION: This paper describes the methodology of partial identification and its applicability to empirical research in preventive medicine and public health. METHODS: The authors summarize findings from the methodologic literature on partial identification. The analysis was conducted in 2020-2021. RESULTS: The applicability of partial identification methods is demonstrated using 3 empirical examples drawn from published literature. CONCLUSIONS: Partial identification methods are likely to be of considerable interest to clinicians and others engaged in preventive medicine and public health research.

Communicating uncertainty in policy analysis.

The term "policy analysis" describes scientific evaluations of the impacts of past public policies and predictions of the outcomes of potential future policies. A prevalent practice has been to report policy analysis with incredible certitude. That is, exact predictions of policy outcomes are routine, while expressions of uncertainty are rare. However, predictions and estimates often are fragile, resting on unsupported assumptions and limited data. Therefore, the expressed certitude is not credible. This paper summarizes my work documenting incredible certitude and calling for transparent communication of uncertainty. I present a typology of practices that contribute to incredible certitude, give illustrative examples, and offer suggestions on how to communicate uncertainty.

Assessing benefits, costs, and disparate racial impacts of confrontational proactive policing.

Effective policing in a democratic society must balance the sometime conflicting objectives of public safety and community trust. This paper uses a formal model of optimal policing to explore how society might reasonably resolve the tension between these two objectives as well as evaluate disparate racial impacts. We do so by considering the social benefits and costs of confrontational types of proactive policing, such as stop, question, and frisk. Three features of the optimum that are particularly relevant to policy choices are explored: (i) the cost of enforcement against the innocent, (ii) the baseline level of crime rate without confrontational enforcement, and (iii) differences across demographic groups in the optimal rate of enforcement.

Diagnostic testing and treatment under ambiguity: using decision analysis to inform clinical practice.

Partial knowledge of patient health status and treatment response is a pervasive concern in medical decision making. Clinical practice guidelines (CPGs) make recommendations intended to optimize patient care, but optimization typically is infeasible with partial knowledge. Decision analysis shows that a clinician's objective, knowledge, and decision criterion should jointly determine the care he prescribes. To demonstrate, this paper studies a common scenario regarding diagnostic testing and treatment. A patient presents to a clinician, who obtains initial evidence on health status. The clinician can prescribe a treatment immediately or he can order a test yielding further evidence that may be useful in predicting treatment response. In the latter case, he prescribes a treatment after observation of the test result. I analyze this scenario in three steps. The first poses a welfare function and characterizes optimal care. The second describes partial knowledge of response to testing and treatment that might realistically be available. The third considers decision criteria. I conclude with reconsideration of clinical practice guidelines.

Using performance standards to evaluate social programs with incomplete outcome data. General issues and application to a higher education block grant program.

The idea of program evaluation is both simple and appealing. Program outcomes are measured and compared to some minimum performance standard or threshold. In practice, however, evaluation is difficult. Two fundamental problems of outcome measurement must be addressed. The first, which we call the problem of auxiliary outcomes, is that we do not observe outcome of interest. The second, which we call the problem of counterfactual outcomes, is that we do not observe the threshold standard. This article examines how performance standard should be set and applied in the face of these problems in measuring outcomes. The central message is that the proper way to implement standards varies with the prior information an evaluator can credibly bring to bear to compensate for incomplete outcome data. By combining available data with credible assumptions on treatments and outcomes, the performance of a program may be deemed acceptable, unacceptable, or indeterminate.