Effect of Social Networks on Mammography Rates
Effect of Social Networks on Mammography Rates
Objectives We estimated the effect of anecdotes of early-stage, screen-detected cancer for which screening was not lifesaving on the demand for mammography.
Methods We constructed an agent-based model of mammography decisions, in which 10 000 agents that represent women aged 40 to 100 years were linked together on a social network, which was parameterized with a survey of 716 women conducted through the RAND American Life Panel. Our model represents a population in equilibrium, with demographics reflecting the current US population based on the most recent available census data.
Results The aggregate effect of women learning about 1 category of cancers—those that would be detected but would not be lethal in the absence of screening—was a 13.8 percentage point increase in annual screening rates.
Conclusions Anecdotes of detection of early-stage cancers relayed through social networks may substantially increase demand for a screening test even when the detection through screening was nonlifesaving.
Women often overestimate the mortality reduction from mammograms, and anecdotes of women with early-stage breast cancer being diagnosed through mammography can serve as particularly powerful motivators in encouraging other women to have screening mammograms. Such diagnoses, which are often shared through discussions between friends, family, coworkers, and other acquaintances, are also often viewed by patients as lifesaving. These discussions can be viewed as the transmission of information through a social network. Typically, social networks are defined as sets of individuals with links between pairs of individuals. In this article, we define 2 individuals as linked if they discuss their health history and outcomes (such as breast cancer diagnosis and treatment) with each other.
Although women often view the detection of early-stage breast cancer through mammography as lifesaving, a recent study estimated that more than three quarters of women with screen-detected cancer have not actually had their lives saved by early detection. Rather, these women have cancers that (1) would never have been detected clinically if not detected through screening (thus, they were overdiagnosed), (2) eventually would have been detected clinically but would not be lethal, or (3) eventually would be lethal despite being detected by screening. Physicians and epidemiologists have long understood that high rates of early-stage diagnosis through screening do not necessarily indicate that the screening reduces mortality. Furthermore, when issuing cancer screening recommendations, professional organizations generally use mortality reduction as a primary measure of the screening's efficacy.
Understanding how nonlifesaving early detection of breast cancer through screening drives patient demand for screening has several important implications. First, professional organizations currently disagree about when and how frequently women should be screened for breast cancer. Those who believe that some women are screened too early and too often may find demand for mammograms driven by such nonlifesaving diagnoses problematic. Our analysis estimated how much demand for mammograms may be driven by women's incorrect assumptions about whether breast cancer screening was lifesaving for individuals in their social networks. More generally, the fact that patients often use frequency of early detection as a proxy for the screening efficacy means that patients with limited time and resources might prioritize a screening test that is relatively ineffective at reducing mortality over a test that more successfully reduces mortality if the former has higher early detection rates than the latter. We examined the potential patient-driven demand for screening that is motivated by nonlifesaving screen-detected cancers discussed in a social network.
It is challenging to quantify this demand through observation or experiment because it is often not possible to determine at the individual level whether early detection through screening affected mortality. If a woman with early-stage, screen-detected breast cancer does not die of breast cancer, it is usually not possible to know with certainty if she would have died from breast cancer had she not been screened. If a woman with early-stage, screen-detected breast cancer ultimately dies of breast cancer, we know that screening was not lifesaving, but there may be many years between her initial diagnosis and death from breast cancer. Therefore, we used an agent-based simulation model to estimate how nonlifesaving early-stage breast cancer detection relayed through a social network might influence mammography rates. A major advantage of using a simulation model to study this problem is that, within the model, we can distinguish between agents (individual women) whose early detection of breast cancer through screening was lifesaving and those whose early detection was not lifesaving.
In our analysis, we explored how population-level mammography rates might change if individuals were able to account for the fact that not all early-stage, screen-detected cancers are lifesaving. We used the results of a customized survey conducted through RAND's American Life Panel to inform and parameterize our behavioral model. Many individuals assume that detection of early-stage breast cancer through mammography is lifesaving, and we assumed that if those diagnoses were known to be nonlifesaving, they would not serve as such powerful motivators in encouraging others to screen. Mammography is lifesaving only when a cancer that would have been lethal when detected clinically is not lethal when detected by mammography. We focused our analysis on categories of cancers whose detection by screening was not lifesaving. First, we simulated changing the incidence of cancers that would not have been detected in the absence of mammography (i.e., were overdiagnosed). Second, we simulated hypothetical interventions that allowed individuals in our model to know that mammography was nonlifesaving for (1) cancers that would be detected but would not have been lethal in the absence of mammography (never-lethal cancers) and (2) cancers that were ultimately lethal despite being screen detected (always-lethal cancers).
Abstract and Introduction
Abstract
Objectives We estimated the effect of anecdotes of early-stage, screen-detected cancer for which screening was not lifesaving on the demand for mammography.
Methods We constructed an agent-based model of mammography decisions, in which 10 000 agents that represent women aged 40 to 100 years were linked together on a social network, which was parameterized with a survey of 716 women conducted through the RAND American Life Panel. Our model represents a population in equilibrium, with demographics reflecting the current US population based on the most recent available census data.
Results The aggregate effect of women learning about 1 category of cancers—those that would be detected but would not be lethal in the absence of screening—was a 13.8 percentage point increase in annual screening rates.
Conclusions Anecdotes of detection of early-stage cancers relayed through social networks may substantially increase demand for a screening test even when the detection through screening was nonlifesaving.
Introduction
Women often overestimate the mortality reduction from mammograms, and anecdotes of women with early-stage breast cancer being diagnosed through mammography can serve as particularly powerful motivators in encouraging other women to have screening mammograms. Such diagnoses, which are often shared through discussions between friends, family, coworkers, and other acquaintances, are also often viewed by patients as lifesaving. These discussions can be viewed as the transmission of information through a social network. Typically, social networks are defined as sets of individuals with links between pairs of individuals. In this article, we define 2 individuals as linked if they discuss their health history and outcomes (such as breast cancer diagnosis and treatment) with each other.
Although women often view the detection of early-stage breast cancer through mammography as lifesaving, a recent study estimated that more than three quarters of women with screen-detected cancer have not actually had their lives saved by early detection. Rather, these women have cancers that (1) would never have been detected clinically if not detected through screening (thus, they were overdiagnosed), (2) eventually would have been detected clinically but would not be lethal, or (3) eventually would be lethal despite being detected by screening. Physicians and epidemiologists have long understood that high rates of early-stage diagnosis through screening do not necessarily indicate that the screening reduces mortality. Furthermore, when issuing cancer screening recommendations, professional organizations generally use mortality reduction as a primary measure of the screening's efficacy.
Understanding how nonlifesaving early detection of breast cancer through screening drives patient demand for screening has several important implications. First, professional organizations currently disagree about when and how frequently women should be screened for breast cancer. Those who believe that some women are screened too early and too often may find demand for mammograms driven by such nonlifesaving diagnoses problematic. Our analysis estimated how much demand for mammograms may be driven by women's incorrect assumptions about whether breast cancer screening was lifesaving for individuals in their social networks. More generally, the fact that patients often use frequency of early detection as a proxy for the screening efficacy means that patients with limited time and resources might prioritize a screening test that is relatively ineffective at reducing mortality over a test that more successfully reduces mortality if the former has higher early detection rates than the latter. We examined the potential patient-driven demand for screening that is motivated by nonlifesaving screen-detected cancers discussed in a social network.
It is challenging to quantify this demand through observation or experiment because it is often not possible to determine at the individual level whether early detection through screening affected mortality. If a woman with early-stage, screen-detected breast cancer does not die of breast cancer, it is usually not possible to know with certainty if she would have died from breast cancer had she not been screened. If a woman with early-stage, screen-detected breast cancer ultimately dies of breast cancer, we know that screening was not lifesaving, but there may be many years between her initial diagnosis and death from breast cancer. Therefore, we used an agent-based simulation model to estimate how nonlifesaving early-stage breast cancer detection relayed through a social network might influence mammography rates. A major advantage of using a simulation model to study this problem is that, within the model, we can distinguish between agents (individual women) whose early detection of breast cancer through screening was lifesaving and those whose early detection was not lifesaving.
In our analysis, we explored how population-level mammography rates might change if individuals were able to account for the fact that not all early-stage, screen-detected cancers are lifesaving. We used the results of a customized survey conducted through RAND's American Life Panel to inform and parameterize our behavioral model. Many individuals assume that detection of early-stage breast cancer through mammography is lifesaving, and we assumed that if those diagnoses were known to be nonlifesaving, they would not serve as such powerful motivators in encouraging others to screen. Mammography is lifesaving only when a cancer that would have been lethal when detected clinically is not lethal when detected by mammography. We focused our analysis on categories of cancers whose detection by screening was not lifesaving. First, we simulated changing the incidence of cancers that would not have been detected in the absence of mammography (i.e., were overdiagnosed). Second, we simulated hypothetical interventions that allowed individuals in our model to know that mammography was nonlifesaving for (1) cancers that would be detected but would not have been lethal in the absence of mammography (never-lethal cancers) and (2) cancers that were ultimately lethal despite being screen detected (always-lethal cancers).
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