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Using multiple risk models with preventive interventions.

ABSTRACT: An ideal preventive intervention would have negligible side effects and could be applied to the entire population, thus achieving maximal preventive impact. Unfortunately, many interventions have adverse effects and beneficial effects. For example, tamoxifen reduces the risk of breast cancer by about 50% and the risk of hip fracture by 45%, but increases the risk of stroke by about 60%; other serious adverse effects include endometrial cancer and pulmonary embolus. Hence, tamoxifen should only be given to the subset of the population with high enough risks of breast cancer and hip fracture such that the preventive benefits outweigh the risks. Recommendations for preventive use of tamoxifen have been based primarily on breast cancer risk. Age-specific and race-specific rates were considered for other health outcomes, but not risk models. In this paper, we investigate the extent to which modeling not only the risk of breast cancer, but also the risk of stroke, can improve the decision to take tamoxifen. These calculations also give insight into the relative benefits of improving the discriminatory accuracy of such risk models versus improving the preventive effectiveness or reducing the adverse risks of the intervention. Depending on the discriminatory accuracies of the risk models, there may be considerable advantage to modeling the risks of more than one health outcome.

SUBMITTER: Gail MH

PROVIDER: S-EPMC3926659 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Using multiple risk models with preventive interventions.

Gail Mitchell H MH

Statistics in medicine 20120626 23

An ideal preventive intervention would have negligible side effects and could be applied to the entire population, thus achieving maximal preventive impact. Unfortunately, many interventions have adverse effects and beneficial effects. For example, tamoxifen reduces the risk of breast cancer by about 50% and the risk of hip fracture by 45%, but increases the risk of stroke by about 60%; other serious adverse effects include endometrial cancer and pulmonary embolus. Hence, tamoxifen should only b ...[more]

PMID: 22733645

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Project description:BackgroundIn the field of implementation research, there is an increased interest in use of theory when designing implementation research studies involving behavior change. In 2003, we initiated a series of five studies to establish a scientific rationale for interventions to translate research findings into clinical practice by exploring the performance of a number of different, commonly used, overlapping behavioral theories and models. We reflect on the strengths and weaknesses of the methods, the performance of the theories, and consider where these methods sit alongside the range of methods for studying healthcare professional behavior change.MethodsThese were five studies of the theory-based cognitions and clinical behaviors (taking dental radiographs, performing dental restorations, placing fissure sealants, managing upper respiratory tract infections without prescribing antibiotics, managing low back pain without ordering lumbar spine x-rays) of random samples of primary care dentists and physicians. Measures were derived for the explanatory theoretical constructs in the Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), and Illness Representations specified by the Common Sense Self Regulation Model (CSSRM). We constructed self-report measures of two constructs from Learning Theory (LT), a measure of Implementation Intentions (II), and the Precaution Adoption Process. We collected data on theory-based cognitions (explanatory measures) and two interim outcome measures (stated behavioral intention and simulated behavior) by postal questionnaire survey during the 12-month period to which objective measures of behavior (collected from routine administrative sources) were related. Planned analyses explored the predictive value of theories in explaining variance in intention, behavioral simulation and behavior.ResultsResponse rates across the five surveys ranged from 21% to 48%; we achieved the target sample size for three of the five surveys. For the predictor variables, the mean construct scores were above the mid-point on the scale with median values across the five behaviors generally being above four out of seven and the range being from 1.53 to 6.01. Across all of the theories, the highest proportion of the variance explained was always for intention and the lowest was for behavior. The Knowledge-Attitudes-Behavior Model performed poorly across all behaviors and dependent variables; CSSRM also performed poorly. For TPB, SCT, II, and LT across the five behaviors, we predicted median R2 of 25% to 42.6% for intention, 6.2% to 16% for behavioral simulation, and 2.4% to 6.3% for behavior.ConclusionsWe operationalized multiple theories measuring across five behaviors. Continuing challenges that emerge from our work are: better specification of behaviors, better operationalization of theories; how best to appropriately extend the range of theories; further assessment of the value of theories in different settings and groups; exploring the implications of these methods for the management of chronic diseases; and moving to experimental designs to allow an understanding of behavior change.

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Using multiple risk models with preventive interventions.

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Using multiple risk models with preventive interventions.

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