David Mendez

Nondaily Smokers: Who Are They?

OBJECTIVE We sought to understand who constitutes the sizable population of nondaily, or some-day (SD), smokers. METHODS We analyzed descriptive statistics and regression results using the 1998-1999 Current Population Survey Tobacco Use Supplement to determine the prevalence of SD smokers, their sociodemographic characteristics, and the smoking patterns and histories of groups differentiated by the length and stability of their SD smoking. RESULTS SD smokers make up 19.2% of all current smokers. Among SD smokers, 44.6% have smoked less than daily for at least 1 year, no more than 14.4% are just starting to smoke, and the rest are likely in transition. Overall, SD smokers smoked a mean of 102 cigarettes per month (compared to 566.4 for daily smokers), on an average of 14.5 days out of the past 30. CONCLUSIONS SD smokers make up a substantial segment of the smoking population. They are not just beginning to smoke nor trying to quit. Many have developed a long-standing pattern of nondaily smoking, smoking relatively few cigarettes on the days when they do smoke. They are not substantially younger than daily smokers, as one might expect.

Adult Cigarette Smoking Prevalence: Declining as Expected (Not as Desired)

We compared observed smoking prevalence data for 1995-2002 with predictions derived from a previously published population dynamics model to determine whether the recent trend in smoking prevalence is consistent with the downward pattern we predicted. The observed data fit our projections closely (R 2 =.89). Consistent with the logic underlying the model, we conclude that adult smoking prevalence will continue to fall for the foreseeable future, although at a rate approximately half that of the decline experienced during the 1970s and 1980s.

Systems Science: A Revolution in Public Health Policy Research

The new systems science approaches emerging in public health research are not new at all; they have a track record earned over several decades in other disciplines, such as physics, operations research, economics, engineering, and, more recently, systems biology. At their core, systems science methodologies are designed to generate models, or simplified versions, of reality. By replicating the real world in important ways—simplifying where possible while retaining the critical aspects relevant to the problem under study—we can better understand the structural complexity of real-world problems that results from the interaction of specific phenomena and their environments. Systems science approaches have been used to address wide-ranging topics such as wildfire control, overfishing, decline of ancient civilizations, climate change, and terrorism networks. A major reason for their recent adoption in the public health arena is a growing recognition of their utility for addressing the complex problems rampant in public health generally1 and in specific domains (e.g., neighborhood effects on health and obesity).2,3 A small but growing number of studies have employed systems science methodologies to understand and address public health problems such as pandemic flu,4 HIV/AIDS,5 diabetes prevalence,6 and heroin markets.7 A few researchers have been using systems science to further tobacco control for nearly a decade.8,9

Smoking prevalence in 2010: why the healthy people goal is unattainable.

OBJECTIVES This study examined the changes in smoking initiation and cessation needed to realize the Healthy People 2010 national adult smoking prevalence objective (13%). METHODS Using data from the National Health Interview Surveys, we calculated smoking prevalence over time with a dynamic population demographics model, examining the effects of changes in smoking initiation and cessation. RESULTS The draft objective is unattainable solely through decreases in smoking initiation. It could be achieved through smoking cessation alone only if cessation rates immediately increased by a factor of more than 3.5. Assuming plausible decreases in initiation and increases in cessation, the draft objective is virtually unattainable. CONCLUSIONS The health objectives should challenge the status quo but be achievable. Formal analysis often can assist in establishing reasonable objectives.

The Use of Simulation Models for the Surveillance, Justification and Understanding of Tobacco Control Policies

Debates over national tobacco legislation and the use of state settlement funds demonstrate a need for information on the effects of tobacco control policies. Computer simulation models that are based on empirical evidence and that account for the variety of influences on tobacco use can be useful tools for informing policy makers. They can identify the effects of different policies, convey the importance of policy approaches to tobacco control, and help policy planners and researchers to better understand policies. This paper examines the role of simulation models in public policy, and discusses several recent models and limitations of those models.

The financial implications of coverage of smoking cessation treatment by managed care organizations

This paper presents results from a simulation of the financial impact and cost effectiveness of smoking cessation in a hypothetical managed care organization (MCO), using data from three large managed care organizations and from existing literature. With base-case assumptions and a market cost of capital, at five years, coverage of cessation services costs an MCO

Employing super-efficiency analysis as an alternative to DEA: An application in outpatient substance abuse treatment

.61 per member per month (PMPM). In a steady-state situation, net cost is

Radon, Smoking, and Lung Cancer: The Need to Refocus Radon Control Policy

.41 PMPM. Both values include altered medical expenditures and MCO revenue patterns attributable to coverage-induced cessation. Quitters gain an average of 7.1 years of life, with a direct coverage cost of

Setting a Challenging Yet Realistic Smoking Prevalence Target for Healthy People 2020: Learning From the California Experience

3,417 for each life-year saved. Coverage of cost-effective programs by MCOs should be strongly encouraged.

The Impact of Declining Smoking on Radon-Related Lung Cancer in the United States

A common technique for conducting efficiency analyses consists of a two-stage procedure that combines data envelopment analysis (DEA) with Tobit regression. As the DEA scores are censored at one, this method has the drawback of masking important information at the upper tail of the distribution of scores. In this paper, we present a DEA-based methodology for a two-stage efficiency analysis where the upper bound constraint of one for the efficiency scores is relaxed. This method, super-efficiency DEA, is contrasted with the two-stage approach that employs traditional, bounded DEA scores. We use data from the National Drug Abuse Treatment Survey to examine how the relative efficiency of the treatment units is affected by the organizational structures, operating characteristics and treatment modalities of a nationally representative sample of outpatient substance abuse treatment units. Our results show that the super-efficiency DEA approach offers advantages over the traditional methodology. It is easy to implement, and, for the same sample size provides more information.