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Individual results may vary: Inequality-probability bounds for some health-outcome treatment effects.

ABSTRACT: While many results from the treatment-effect and related literatures are familiar and have been applied productively in health economics evaluations, other potentially useful results from those literatures have had little influence on health economics practice. With the intent of demonstrating the value and use of some of these results in health economics applications, this paper focuses on one particular class of parameters that describe probabilities that one outcome is larger or smaller than other outcomes ("inequality probabilities"). While the properties of such parameters have been exposited in the technical literature, they have scarcely been considered in informing practical questions in health evaluations. This paper shows how such probabilities can be used informatively, and describes how they might be identified or bounded informatively given standard sampling assumptions and information only on marginal distributions of outcomes. The logic of these results and the empirical implementation thereof-sampling, estimation, and inference-are straightforward. Derivations are provided and several health-related applications are presented.

SUBMITTER: Mullahy J

PROVIDER: S-EPMC6588285 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Individual results may vary: Inequality-probability bounds for some health-outcome treatment effects.

Mullahy John J

Journal of health economics 20180704

While many results from the treatment-effect and related literatures are familiar and have been applied productively in health economics evaluations, other potentially useful results from those literatures have had little influence on health economics practice. With the intent of demonstrating the value and use of some of these results in health economics applications, this paper focuses on one particular class of parameters that describe probabilities that one outcome is larger or smaller than ...[more]

PMID: 30149246

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Project description:BackgroundHistorically, the receipt of prescription opioids has differed among racial groups in the United States. Research has not sufficiently explored the contribution of individual health systems to these differences by examining within-system prescription opioid receipt according to race.MethodsWe used 2016 and 2017 Medicare claims data from a random 40% national sample of fee-for-service, Black and White beneficiaries 18 to 64 years of age who were attributed to health systems. We identified 310 racially diverse systems (defined as systems with ≥200 person-years each for Black and White patients). To test representativeness, we compared patient characteristics and opioid receipt among the patients in these 310 systems with those in the national sample. Within the 310 systems, regression models were used to explore the difference between Black and White patients in the following annual opioid measures: any prescription filled, short-term receipt of opioids, long-term receipt of opioids (one or more filled opioid prescriptions in all four calendar quarters of a year), and the opioid dose in morphine milligram equivalents (MME); models controlled for patient characteristics, state, and system.ResultsThe national sample included 2,197,153 person-years, and the sample served by 310 racially diverse systems included 896,807 person-years (representing 47.4% of all patients and 56.1% of Black patients in the national sample). The national sample and 310-systems sample differed meaningfully only in the percent of person-years contributed by Black patients (21.3% vs. 25.9%). In the 310-systems sample, the crude annual prevalence of any opioid receipt differed slightly between Black and White patients (50.2% vs. 52.2%), whereas the mean annual dose was 36% lower among Black patients than among White patients (5190 MME vs. 8082 MME). Within systems, the adjusted race differences in measures paralleled the population trends: the annual prevalence of opioid receipt differed little, but the mean annual dose was higher among White patients than among Black patients in 91% of the systems, and at least 15% higher in 75% of the systems.ConclusionsWithin individual health systems, Black and White patients received markedly different opioid doses. These system-specific findings could facilitate exploration of the causes and consequences of these differences. (Funded by the National Institute on Aging and the Agency for Healthcare Research and Quality.).

Project description:ObjectivesTo predict treatment effects for individual patients based on data from randomised trials, taking rosuvastatin treatment in the primary prevention of cardiovascular disease as an example, and to evaluate the net benefit of making treatment decisions for individual patients based on a predicted absolute treatment effect.SettingAs an example, data were used from the Justification for the Use of Statins in Prevention (JUPITER) trial, a randomised controlled trial evaluating the effect of rosuvastatin 20 mg daily versus placebo on the occurrence of cardiovascular events (myocardial infarction, stroke, arterial revascularisation, admission to hospital for unstable angina, or death from cardiovascular causes). Population 17,802 healthy men and women who had low density lipoprotein cholesterol levels of less than 3.4 mmol/L and high sensitivity C reactive protein levels of 2.0 mg/L or more.MethodsData from the Justification for the Use of Statins in Prevention trial were used to predict rosuvastatin treatment effect for individual patients based on existing risk scores (Framingham and Reynolds) and on a newly developed prediction model. We compared the net benefit of prediction based rosuvastatin treatment (selective treatment of patients whose predicted treatment effect exceeds a decision threshold) with the net benefit of treating either everyone or no one.ResultsThe median predicted 10 year absolute risk reduction for cardiovascular events was 4.4% (interquartile range 2.6-7.0%) based on the Framingham risk score, 4.2% (2.5-7.1%) based on the Reynolds score, and 3.9% (2.5-6.1%) based on the newly developed model (optimal fit model). Prediction based treatment was associated with more net benefit than treating everyone or no one, provided that the decision threshold was between 2% and 7%, and thus that the number willing to treat (NWT) to prevent one cardiovascular event over 10 years was between 15 and 50.ConclusionsData from randomised trials can be used to predict treatment effect in terms of absolute risk reduction for individual patients, based on a newly developed model or, if available, existing risk scores. The value of such prediction of treatment effect for medical decision making is conditional on the NWT to prevent one outcome event. Trial registration number Clinicaltrials.gov NCT00239681.

Project description:IntroductionHealth-income inequality has been the focus of many studies. The relationship between economic conditions and health has also been widely studied. However, not much is known about how changes in aggregate economic conditions relate to health-income inequality. Nevertheless, such knowledge would have both scientific and practical value as substantial public expenditures are used to decrease such inequalities and opportunities to do so may differ over the business cycle. For this reason we examine the effect of the Icelandic economic collapse in 2008 on health-income inequality.MethodsThe data used come from a health and lifestyle survey carried out by the Public Health Institute of Iceland in 2007 and 2009. A stratified random sample of 9,807 individuals 18-79 years old received questionnaires and a total of 42.1% answered in both years. As measures of health-income inequality, health-income concentration indices are calculated and decomposed into individual-level determinants. Self-assessed health is used as the health measure in the analyses, but three different measures of income are used: individual income, household income, and equivalized household income.ResultsIn both years there is evidence of health-income inequality favoring the better off. However, changes are apparent between years. For males health-income inequality increases after the crisis while it remains fairly stable for females or slightly decreases. The decomposition analyses show that income itself and disability constitute the most substantial determinants of inequality. The largest increases in contributions between years for males come from being a student, having low education and being obese, as well as age and income but those changes are sensitive to the income measure used.ConclusionsChanges in health and income over the business cycle can differ across socioeconomic strata, resulting in cyclicality of income-related health distributions. As substantial fiscal expenditures go to limiting the relationship between income and health, the business-cycle effect on equality, which has up until now not received much attention, needs to be considered.

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Individual results may vary: Inequality-probability bounds for some health-outcome treatment effects.

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Individual results may vary: Inequality-probability bounds for some health-outcome treatment effects.

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