Use of Monte Carlo simulation to investigate uncertainty in exposure modeling

OBJECTIVES: This study used Monte Carlo (MC) simulation to examine the influence of uncertainty on an exposure model and to determine whether a difference exists between two worker groups in a ceramic fiber manufacturing plant. METHODS: Data on work practices and conditions were gathered in interviews with long-serving employees. With the use of previously developed deterministic modeling techniques and likely distributions for model parameters, MC simulations generated exposure profiles for the two job titles. RESULTS: The exposure profiles overlapped considerably, although the average estimated exposure for one job was approximately double that of the other. However, when the correlation between the model parameters in the two jobs was considered, it was concluded that there was a significant difference in the two estimates. CONCLUSIONS: Models are increasingly being used to estimate exposure. Different work situations inevitably result in different exposure estimates. However, it is difficult to determine whether such differences in estimated exposure between worker groups are simply the result of uncertainty with respect to the model parameters or whether they reflect real differences between occupational groups. This study demonstrates the value of MC simulation in helping define the uncertainty in deterministic model estimates.     

Using the economics of certification to improve the safety and quality of male circumcision in developing countries

Although male circumcision (MC) has been a widespread practice in some regions, while relatively new in others, it has recently ascended in popularity as a HIV-reduction intervention, particularly in areas with high rates of HIV but low rates of MC. However, the uptake and potential effectiveness of MC may be hampered by uneven levels of provider training and procedure skill within developing country settings. Indeed, this procedure that is otherwise considered simple and safe has witnessed complication rates as high as 25-35% in some areas, leaving some men with irreversible injuries. To improve the transparency of procedure quality for prospective patients, I borrow from a classic economics approach and advocate a new application in the form of provider certification. Building on some experience in the healthcare systems and economic rationale of high-income counties, I explore the potential for certifying providers of MC in low-income countries and compare and contrast three models of implementation: government agency, private certifiers and private MC device manufacturers. The hope is that increased transparency of provider quality through any or all three types of certifying programmes can better assist local men as they navigate this otherwise complex and unclear medical care market. As more resources are being devoted to MC scale up, I argue that certification should be considered for incorporation as a means of complementing both current and future efforts in order to enhance the effectiveness of MC campaigns. The two models based on privatized certification, as opposed to having the local government underwrite the intervention, may prove most useful when public or philanthropic funding is volatile or incomplete for a given location. The timing for MC campaign adoption and desired speed of scale up may vary across countries in ways that international assistance efforts cannot always immediately and flexibly adapt to. As such, the role of the diverse MC provider marketplace and accompanying quality-revelation mechanisms may take on different levels of importance and expediency across settings as individual countries move forward with their respective HIV prevention campaigns. The subsequent challenge is to creatively design solutions that are sustainable and applicable within diverse host-country environments and expectations. This is where I believe some economic insights are currently lacking in the MC dialogue. Although I believe the three certification models exhibit much potential for enhancing medical care delivery in developing countries, they are not without their challenges, and implementation would not necessarily be a simple process. Local levels of medical knowledge, public and private resource constraints and the integrity of local business transactions and government practices would likely influence the nature and success of a certification intervention. However, with sufficient model adaptability and partnerships across public and private sectors, I argue that many of these implementation issues could be proactively addressed. Creative and careful certification structures should ultimately improve the MC circumstances across a variety of developing countries.     

Age dependence of cohort phenomena in breast cancer mortality in the United States

Breast cancer mortality has increased in most parts of the world, and many explanations have been postulated. In this paper, the authors examined the evolution of mortality rates for white and nonwhite females in the United States from 1950-1979. Using both graphic techniques and Poisson regression models, they found that there has been strong modification of apparent cohort effects by age. For both white and nonwhite females, they observed an increase in mortality rates limited to the postmenopausal ages.     

Replication in Prevention Science

Replication research is essential for the advancement of any scientific field. In this paper, we argue that prevention science will be better positioned to help improve public health if (a) more replications are conducted; (b) those replications are systematic, thoughtful, and conducted with full knowledge of the trials that have preceded them; and (c) state-of-the art techniques are used to summarize the body of evidence on the effects of the interventions. Under real-world demands it is often not feasible to wait for multiple replications to accumulate before making decisions about intervention adoption. To help individuals and agencies make better decisions about intervention utility, we outline strategies that can be used to help understand the likely direction, size, and range of intervention effects as suggested by the current knowledge base. We also suggest structural changes that could increase the amount and quality of replication research, such as the provision of incentives and a more vigorous pursuit of prospective research registers. Finally, we discuss methods for integrating replications into the roll-out of a program and suggest that strong partnerships with local decision makers are a key component of success in replication research. Our hope is that this paper can highlight the importance of replication and stimulate more discussion of the important elements of the replication process. We are confident that, armed with more and better replications and state-of-the-art review methods, prevention science will be in a better position to positively impact public health.     

Ordinal regression models for epidemiologic data

Health status is often measured in epidemiologic studies on an ordinal scale, but data of this type are generally reduced for analysis to a single dichotomy. Several statistical models have been developed to make full use of information in ordinal response data, but have not been much used in analyzing epidemiologic studies. The authors discuss two of these statistical models--the cumulative odds model and the continuation ratio model. They may be interpreted in terms of odds ratios, can account for confounding variables, have clear and testable assumptions, and have parameters that may be estimated and hypotheses that may be tested using available statistical packages. However, calculations of asymptotic relative efficiency and results of simulations showed that simple logistic regression applied to dichotomized responses can in some realistic situations have more than 75% of the efficiency of ordinal regression models, but only if the ordinal scale is collapsed into a dichotomy close to the optimal point. The application of the proposed models to data from a study of chest x-rays of workers exposed to mineral fibers confirmed that they are easy to use and interpret, but gave results quite similar to those obtained using simple logistic regression after dichotomizing outcome in the conventional way.     

Loss to Follow-Up in Cohort Studies: How Much is Too Much?

Loss to follow-up is problematic in most cohort studies and often leads to bias. Although guidelines suggest acceptable follow-up rates, the authors are unaware of studies that test the validity of these recommendations. The objective of this study was to determine whether the recommended follow-up thresholds of 60-80% are associated with biased effects in cohort studies. A simulation study was conducted using 1000 computer replications of a cohort of 500 observations. The logistic regression model included a binary exposure and three confounders. Varied correlation structures of the data represented various levels of confounding. Differing levels of loss to follow-up were generated through three mechanisms: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). The authors found no important bias with levels of loss that varied from 5 to 60% when loss to follow-up was related to MCAR or MAR mechanisms. However, when observations were lost to follow-up based on a MNAR mechanism, the authors found seriously biased estimates of the odds ratios with low levels of loss to follow-up. Loss to follow-up in cohort studies rarely occurs randomly. Therefore, when planning a cohort study, one should assume that loss to follow-up is MNAR and attempt to achieve the maximum follow-up rate possible.     

Internationale Kohortenstudien

Among observational studies, cohort studies, i.e. longitudinal observations of selected population groups, provide the highest possible evidence of a causal association between specific risk factors (exposure) and the occurrence of disease in populations. Besides the fact that many exposures cannot be investigated in experimental designs, cohort studies have the advantage over randomized clinical trials that they are conducted in free living populations and not in restrictive, clinical settings. In this paper we describe the aims and features of international cohorts that have been selected because of their impact, their size or their endpoints. We do not only present the study designs and survey instruments used but we also highlight some of the most important results gained by these studies. Most of these prospective studies investigated common chronic diseases in the elderly, such as cancer, diabetes, cardiovascular or neurodegenerative diseases, osteoporosis and ophthalmologic disorders. Newer cohorts and recent reassessments of existing cohorts almost always include the collection and storage of biological samples. In recent years technological developments allowed the implementation of cutting edge measurement procedures, such as imaging techniques for phenotyping. Finally, we discuss on the one hand whether these designs can be transferred to the German situation and on the other hand to what degree the results obtained from foreign cohorts can be generalized for the German population. We conclude with recommendations for future cohort studies.     

Implementation of G-computation on a simulated data set: demonstration of a causal inference technique

The growing body of work in the epidemiology literature focused on G-computation includes theoretical explanations of the method but very few simulations or examples of application. The small number of G-computation analyses in the epidemiology literature relative to other causal inference approaches may be partially due to a lack of didactic explanations of the method targeted toward an epidemiology audience. The authors provide a step-by-step demonstration of G-computation that is intended to familiarize the reader with this procedure. The authors simulate a data set and then demonstrate both G-computation and traditional regression to draw connections and illustrate contrasts between their implementation and interpretation relative to the truth of the simulation protocol. A marginal structural model is used for effect estimation in the G-computation example. The authors conclude by answering a series of questions to emphasize the key characteristics of causal inference techniques and the G-computation procedure in particular.     

计划性剖宫产临床路径应用的回顾性分析

目的：探讨临床路径在我院计划性剖宫产中的应用效果。方法：通过非同期队列研究,比较实施临床路径前后的计划性剖宫产患者的住院时间、住院费用、住院并发症指标。结果：路径组平均住院时间与对照组相比,呈下降趋势,但无显著性差异。平均住院费用两组比较,差异无显著性,对于住院费用各项比较,路径组药费、诊疗费明显比对照组低,差异具有显著性。结论：实施临床路径后,患者药费和诊疗费用均有下降。提示临床路径具有一定规范医疗行为的作用。     

Medical self-care programs

Self-care has its roots outside of the traditional or formal medical delivery system. However, as potential advantages of such efforts are considered, self-care projects are increasingly being integrated within the delivery system. The authors comment on the rationale behind the self-care movement, report examples of successful models and discuss planning, organization and implementation issues pertinent to the development of a cost-effective, targeted program.     

Models of Health and Models of Interaction in the Practitioner–Client Relationship in Acupuncture

The doctor–patient relationship has been widely studied in biomedicine. However, little research has focused on similar provider–client relationships in holistic healthcare forms. Based on ethnographic research with acupuncture clients and practitioners, the authors found that participants used specific models of health to understand and develop subsequent models of interaction, and in doing so, provided a clear critique of biomedicine. This article offers a brief overview of major models of healthcare, including biomedical, biopsychosocial, and holistic. The authors present current models of interaction that have been used to understand the biomedical doctor–patient relationship, and discuss the utility of both sets of models as they relate to the ethnographic observations. Although a particular model of health (biomedical or holistic) does not necessitate a particular model of health interaction (paternalism, consumerism, or collaboration), participants' attempts to tie these 2 realms together are important to understanding practitioner–patient relationships in all healthcare situations.     

Pseudo-likelihood estimates of the cumulative risk of an autosomal dominant disease from a kin-cohort study

Wacholder et al. [1998: Am J Epidemiol 148:623-629] and Struewing et al. [1997: N Engl J Med 336:1401-1408] have recently proposed a design called the kin-cohort design to estimate the probability of developing disease (penetrance) associated with an autosomal dominant gene. In this design, volunteers (probands) agree to be genotyped and one also determines the disease history (phenotype) of first-degree relatives of the proband. They used this design to estimate that the chance of developing breast cancer by age 70 in Ashkenazi Jewish women who carried mutations of the genes BRCA1 or BRCA2 was 0.56, a figure that was lower than previously estimated from highly affected families. The method that they used to estimate the cumulative risk of breast cancer, while asymptotically correct, does not necessarily produce monotone estimates in small samples. To obtain monotone, weakly parametric estimates, we consider separate piecewise exponential models for carriers and non-carriers. As the number of intervals on which constant hazards are assumed increases, however, the maximum likelihood score equations become unstable and difficult to solve. We, therefore, developed alternative pseudo-likelihood procedures that are readily solvable for piecewise exponential models with many intervals. We study these techniques through simulations and a re-analysis of a portion of the data used by Struewing et al. [1997] and discuss possible extensions.     

On the use of structural equation modeling in health communication research

Structural equation modeling (SEM) is a multivariate technique suited for testing proposed relations between variables. In this article, the authors discuss the potential for SEM as a tool to advance health communication research both statistically and conceptually. Specifically, the authors discuss the advantages that latent variable modeling in SEM affords researchers by extracting measurement error. In addition, they argue that SEM is useful in understanding communication as a complex set of relations between variables. Moreover, the authors articulate the possibility for examining communication as an agent, mediator, and an outcome. Finally, they review the application of SEM to recursive models, interactions, and confirmatory factor analysis.     

Bias correction by use of errors-in-variables regression models in studies with K-X-ray fluorescence bone lead measurements

In-vivo measurement of bone lead by means of K-X-ray fluorescence (KXRF) is the preferred biological marker of chronic exposure to lead. Unfortunately, considerable measurement error associated with KXRF estimations can introduce bias in estimates of the effect of bone lead when this variable is included as the exposure in a regression model. Estimates of uncertainty reported by the KXRF instrument reflect the variance of the measurement error and, although they can be used to correct the measurement error bias, they are seldom used in epidemiological statistical analyzes. Errors-in-variables regression (EIV) allows for correction of bias caused by measurement error in predictor variables, based on the knowledge of the reliability of such variables. The authors propose a way to obtain reliability coefficients for bone lead measurements from uncertainty data reported by the KXRF instrument and compare, by the use of Monte Carlo simulations, results obtained using EIV regression models vs. those obtained by the standard procedures. Results of the simulations show that Ordinary Least Square (OLS) regression models provide severely biased estimates of effect, and that EIV provides nearly unbiased estimates. Although EIV effect estimates are more imprecise, their mean squared error is much smaller than that of OLS estimates. In conclusion, EIV is a better alternative than OLS to estimate the effect of bone lead when measured by KXRF.     

Performance of disease risk scores, propensity scores, and traditional multivariable outcome regression in the presence of multiple confounders

Propensity scores are widely used in cohort studies to improve performance of regression models when considering large numbers of covariates. Another type of summary score, the disease risk score (DRS), which estimates disease probability conditional on nonexposure, has also been suggested. However, little is known about how it compares with propensity scores. Monte Carlo simulations were conducted comparing regression models using the DRS and the propensity score with models that directly adjust for all of the individual covariates. The DRS was calculated in 2 ways: from the unexposed population and from the full cohort. Compared with traditional multivariable outcome regression models, all 3 summary scores had comparable performance for moderate correlation between exposure and covariates and, for strong correlation, the full-cohort DRS and propensity score had comparable performance. When traditional methods had model misspecification, propensity scores and the full-cohort DRS had superior performance. All 4 models were affected by the number of events per covariate, with propensity scores and traditional multivariable outcome regression least affected. These data suggest that, for cohort studies for which covariates are not highly correlated with exposure, the DRS, particularly that calculated from the full cohort, is a useful tool.     

Behavioral and social sciences theories and models: are they used in unintentional injury prevention research?

Behavioral and social sciences theories and models have the potential to enhance efforts to reduce unintentional injuries. The authors reviewed the published literature on behavioral and social science theory applications to unintentional injury problems to enumerate and categorize the ways different theories and models are used in injury prevention research. The authors conducted a systematic review to evaluate the published literature from 1980 to 2001 on behavioral and social science theory applications to unintentional injury prevention and control. Electronic database searches in PubMed and PsycINFO identified articles that combined behavioral and social sciences theories and models and injury causes. The authors identified some articles that examined behavioral and social science theories and models and unintentional injury topics, but found that several important theories have never been applied to unintentional injury prevention. Among the articles identified, the PRECEDE PROCEED Model was cited most frequently, followed by the Theory of Reasoned Action/Theory of Planned Behavior and Health Belief Model. When behavioral and social sciences theories and models were applied to unintentional injury topics, they were most frequently used to guide program design, implementation or develop evaluation measures; few examples of theory testing were found. Results suggest that the use of behavioral and social sciences theories and models in unintentional injury prevention research is only marginally represented in the mainstream, peer-reviewed literature. Both the fields of injury prevention and behavioral and social sciences could benefit from greater collaborative research to enhance behavioral approaches to injury control.     

Revascularizing the Myocardium: The Saphenous Vein Bypass

A case is presented in which this most recent approach to the surgical treatment of coronary artery disease was successfully utilized. The authors discuss the procedure in the context of its advantages and of the limitations of previous techniques. The criteria they employ in the selection of appropriate patients from among the vast reservoir of patients with crippling disease are described.     

Type II translation: transporting prevention interventions from research to real-world settings

This article summarizes research on Type II translation of prevention interventions aimed at enhancing the adoption of effective programs and practices in communities. The primary goal of Type II translation is to institutionalize evidence-based programs, products, and services. First, the authors describe theoretical frameworks that are useful to guide Type II translation research. Second, research on prevention program implementation, including fidelity of implementation and factors that are associated with successful program implementation, is summarized. The authors describe interventions designed to enhance the dissemination of preventive interventions in community and public health settings. Third, they describe strategies used by prevention program developers who have taken programs to scale. Fourth, they present a case example of Project Towards No Drug Abuse (TND), an empirically validated high school-based substance abuse prevention program. They describe ongoing research on the dissemination of Project TND. Finally, they provide suggestions for future Type II translation research.