Warnings in Manufacturing: ImprovingHazard-Mitigation Messaging throughAudience AnalysisRichard C. Goldsworthy,1 Christopher B. Mayhorn,2 and Adam W. Meade2

1 Academic Edge, Inc., Bloomington, Indiana2 Department of Psychology, North Carolina State University, Raleigh

Abstract

Hazard mitigation, including warning development, validation, and dissemination, is an importantaspect of product safety and workplace and consumer protection. Understanding our audiences—workers and consumers—is an especially important, often overlooked, aspect of risk and harm reduc-tion efforts. In this article, particular attention is paid to audience analysis in hazard communicationand warning messaging, with a focus on the potential role of latent class analysis (LCA). We providean example of using LCA to analyze a hazardous behavior: prescription medicine sharing and bor-rowing. Four distinct groups of people—ranging from abstainers to at-risk sharers—are identifiedand discussed. Building better warnings and risk communication techniques is essential to promotingoccupational and consumer safety. Audience analysis is a vital component of these efforts. LCA appearsto be a worthwhile addition to our analytical toolbox by allowing risk reduction and hazard-mitigationefforts to tailor interventions to a diverse target audience. C© 2010 Wiley Periodicals, Inc.

Keywords: Hazard mitigation; Warnings; Audience analysis; Latent class analysis

1. INTRODUCTION

1.1. Hazard Mitigation and Warnings

Warning: Causes Birth Defects! Do not operate heavy machinery while using this medication. Only wear tight-fitting clothing when using this tool. Wash your hands before returning to work. Do not allow clothing or hairto become entangled. Hard hat area! Do not eat these packets!

Correspondence to: Richard C. Goldsworthy, Research &Development, Academic Edge, Inc., P.O. Box 5307,Bloomington, IN 47407-5307. Phone: 812-333-9543; e-mail:rick@academicedge.com

Received: 24 November 2008; revised 24 March 2009; accepted27 March 2009

View this article online at wileyonlinelibrary.com.

DOI: 10.1002/hfm.20163

Hazard mitigation, including warning development,validation, and dissemination, is an important aspectof workplace and consumer protection and productsafety. Manufacturing presents a large array of possi-ble hazards, with consequences ranging from incon-venience and inefficiency to bodily harm and death.Both the processes of manufacturing—how we makethings and the tools we use to make them—and theproducts manufactured can be hazardous. These haz-ards extend from the workers who make products tothe consumers who use them. Hazard mitigation is an

484 Human Factors and Ergonomics in Manufacturing & Service Industries 20 (6) 484–499 (2010) c© 2010 Wiley Periodicals, Inc.

Goldsworthy, Mayhorn, and Meade Warnings in Manufacturing

important aspect of the design of products, manufac-turing processes, and workplace environments.

Reducing risks in manufacturing can be framedwithin a three-level hazard-mitigation hierarchy(Sanders & McCormick, 1993). The levels map fairlywell to how early in the product, process, and envi-ronment design process hazards are identified and ad-dressed. In the first level, we try to “design out” thehazard. If a feature of a product is identified as dan-gerous, can that feature be removed? If a step in amanufacturing process has a risk of harm, can otherprocedures be used? If a specific tool is itself poten-tially dangerous or if the design of the manufactur-ing environment is such that it may lead to increasedrisks, can the tools and environments be altered? At thisfirst level, we address risk by attempting to remove itthrough redesign of products, processes, and environ-ments. This is, of course, the ideal approach becauseit entirely removes components of risk; however, somehazards simply cannot be designed out of products andprocesses without affecting important target outcomesassociated with those products and processes (e.g., thetherapeutic effects of a drug might be reduced if thechemical formula is altered to remove hazardous sideeffects; some manufacturing tools are dangerous be-cause the tasks they are employed to perform cannotcurrently be accomplished through any other means).At other times, the costs of redesigning or retrofittinga product or process or reimplementing an environ-ment may be substantial, and a cost–benefit analysismay render changes economically infeasible, especiallywhen the changes would be implemented late in thedesign cycle. When products and processes cannot bechanged to remove hazards early in the design cycle,then mitigation efforts turn to the second and thirdlevels of the hierarchy.

In the second level, processes are put into place toreduce the likelihood that a hazard that is present ina product, process, or environment will lead to neg-ative outcomes. That is, although a hazard may exist,Level 2 implements active steps to reduce the occur-rence and/or severity of consequences of the hazard.An example from the pharmaceutical industry is thedevelopment and dissemination of gate-keeping pro-cedures: Certain hazardous prescription drugs can beobtained only after meeting specific criteria for use,with both the pharmacist and the health care providertaking specific scripted actions to ensure appropriatedispensation. Specific procedures for entering partic-ular work areas, for working with specific tools, for

transitioning from one worker to another, and for de-contamination or other immediate intervention fol-lowing incidents and exposures are frequently put inplace in manufacturing environments. Therefore, atthis second level, the hazard remains but organizationsembed procedures to avoid or lessen the severity ofadverse outcomes associated with the hazard.

The third level of the mitigation hierarchy involveseducational efforts that include training and commu-nication with those who may encounter the hazard.The term “training” is more frequently used in manu-facturing to refer to processes put into play to improveworker performance and safety, whereas the term “ed-ucation” is more frequently used when such processesare applied to consumers. Thus education and train-ing within this level of the hierarchy can be consideredsynonymous and can go hand in hand with the secondlevel: Gate-keeping, risk reduction, and adverse out-come mitigation processes are put in place (Level 2),and educational and warning efforts are implemented(Level 3). Frequently, however, the only available re-course in reducing the impact of hazards is an educa-tional effort or use of warning messages.

Moreover, in many cases hazard mitigation devolvessolely to the latter: The hazard exists, it could not bedesigned around, processes could not be developed toreduce incidence and/or harm, educational opportuni-ties are not available (or can be avoided), and workersor consumers can only be warned by messaging lo-cated within the products, processes, or environmentsthemselves. To illustrate such an example, consider thehidden fall hazard associated with the lack of safety beltuse by young children in shopping carts. Although ithas long been recognized that shopping cart design canbe modified and that awareness campaigns can be im-plemented to reduce the incidence of harm to this vul-nerable population, these actions have not occurred,and these preventable cart-related injuries continue tooccur (Wright, Griffin, MacLennan, Rue, & McGwin,2008). In this situation, warning messages that informpeople of the fall hazard are essential to combatingparents’ erroneously low perceptions of risk, therebyreducing the likelihood of a child’s injury (Barker,Bailey, & Lee, 2004; Harrell, 2003; Laughery, 2006).

Across these three levels, there are systematic waysto address hazard mitigation. Methodological and the-oretical frameworks (see Edworthy & Adams, 1996;Wogalter, 2006 for comprehensive reviews), tools(Mayhorn, Wogalter, & Bell, 2004; Wogalter, Vigilante,& Conzola, 1999), protocols (Goldsworthy & Kaplan,

Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm 485

Warnings in Manufacturing Goldsworthy, Mayhorn, and Meade

2006a; Wolff & Wogalter, 1993, 1998), and technologies(Wogalter & Mayhorn, 2005) have evolved to providea firm foundation for hazard mitigation and warningsresearch activities.

1.2. Audience Analysis

Here, we focus on the third level, specifically on warn-ing development, and in particular on ways to involveworkers and consumers in message development andon tools for better understanding these target audi-ences. We have reported elsewhere on the systematicdesign and development of warnings for teratogenicpharmaceuticals—drugs that are associated with thedevelopment of birth defects protocols (Goldsworthy& Kaplan, 2006b). Some of these drugs can lead to ad-verse pregnancy outcomes when taken a month beforebecoming pregnant. In that effort, a six stage, iterative,user-centered process was used to guide developmentof warnings from design through evaluation and dis-semination. One particularly significant aspect of thatdevelopment is the importance of understanding ouraudiences.

The quest for such understanding is often referredto as audience analysis (for reviews, see Kaiser, 1958;Massy, 1965; Westley & Lynch, 1962), and it is equallyimportant to safeguarding our production processesand workplace environments as it is to protecting con-sumers. If we do not fully understand whom we aresafeguarding, our efforts to reduce risk and mitigate ad-verse outcomes will be less effective than they otherwisecould be. Audience analysis is, therefore, a key researchand development activity during the establishment ofa mitigation framework for any particular hazard. Au-dience analysis and any resulting hazard-mitigationframework and/or message tailoring that emerge fromsuch analysis is a complex endeavor. Somewhere be-tween an audience of one individual (the ultimate in“tailoring”) and a general undifferentiated audiencemodel (no tailoring), those involved in hazard miti-gation must find a middle ground that is often boththeoretically and practically difficult to identify in arigorous manner.

Audiences vary by a wide range of characteristics—some obvious, others not. It has become increasinglycommon to examine message interpretation not onlyby whether audiences get it right, but by who is get-ting it more or less right. For instance, risk percep-tions associated with pesticide warning labels appearto vary by ethnicity such that the likelihood of warn-

ing compliance is higher for European-American farmworkers than for Latino farm workers (Smith-Jackson,Wogalter, & Quintela, 2008). Similarly, in a study thatexamined several possible birth defect warning labelsamong a diverse group of women of child-bearingage, both accuracy of warning interpretation andwarning preference—the warning participants thoughtwould be most effective—varied significantly by par-ticipant characteristics (Goldsworthy & Kaplan, 2006a;Mayhorn & Goldsworthy, 2007). These analyses typi-cally examine common audience characteristics, suchas age, gender, and/or ethnicity, by using a relativelyunsophisticated statistical analytical tool, such as chi-square or Fisher’s Exact Test, to determine whether“correctness” or rates of particular responses vary bythose demographic characteristics.

Such analytical approaches are standard and aremore useful in providing more information to haz-ard researchers than are simple descriptive statisticsregarding percentages of correctness or types of re-sponses across a sample. Other statistical tools, how-ever, can provide a richer picture of audience segmen-tation, especially, but not only, when the hazardoussituation involves multiple informational or behavioralcomponents, when a sizable number of beliefs mightbe implicated in engagement (or disengagement) in aparticular hazardous action, or when a complex set ofdemographic characteristics is suggested by previousresearch or previous researcher experience. For theserich situations, we suggest latent class analysis (LCA)as one methodological approach that may prove to beuseful in identifying pertinent receiver characteristics(for a review, see Green, 1951).

1.3. LCA

LCA is part of a broad class of analyses that also in-cludes latent profile analysis, latent class growth anal-ysis, latent transition analysis, growth mixture model-ing, and general growth mixture modeling (Muthen,2001). The common denominator in these analysesis that respondents are assumed to come from differ-ent populations or subpopulations rather than from asingle uniform population; accordingly, this family ofanalyses attempts to estimate and account for groupmembership as part of the analytic process. In prac-tice, LCA is a method of grouping respondents intohomogenous subgroups based on their responses to ameasure of interest.

486 Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm

Goldsworthy, Mayhorn, and Meade Warnings in Manufacturing

Conceptually, the purpose of LCA is most similar tothat of cluster analysis in which participants’ observedresponses are used to empirically group participantsinto one of several groups. Unlike cluster analysis, LCAachieves this goal by using a latent categorical vari-able that represents group membership. Latent vari-ables are unobserved mathematical representations ofconstructs defined by the indicator variables. By us-ing latent variables, LCA fits into the broad categoryof covariance structure analysis, which includes struc-tural equation modeling. The analogy between clusteranalysis and LCA is like that of path analysis to struc-tural equation modeling in that the latter uses latentvariables to represent observed data patterns. TypicallyLCA involves the use of a series of dichotomous (e.g.,yes/no) items to make these classifications. Althoughsimilar types of analyses can be undertaken with othertypes of response data (e.g., Likert scale data), suchanalyses are typically known as latent profile analyseseven though they belong to the same family of latentvariable models.

LCA is similar to cluster analysis with respect to pur-pose; however, there are several important differencesbetween LCA and cluster analysis. First, LCA employsmodel-based measurement that assumes different un-derlying populations. Model-based measurement hasthe advantage of a priori theoretical structure and in-dices of model fit that are available to examine how wellthe model accounts for the observed relationships. Sec-ond, LCA uses maximum likelihood estimation to de-termine class membership. Maximum likelihood esti-mators have the advantage of being robust and efficientwith known mathematical properties; this estimationtechnique also underlies analyses such as structuralequation modeling and some types of factor analy-sis. As a result, the appropriate number of classes canbe determined via indices with known mathematicalproperties; this type of determination is more objectivethan the cluster analysis grouping techniques (Nylund,Asparouhov, & Muthen, 2007). Third, if desired, re-strictions can be imposed on the model (i.e., a confir-matory LCA) so that only certain indicators are usedto define groups or that the effect of some indicatorson definition of group membership is limited. Thisis conceptually similar to confirmatory factor analy-sis. Fourth, in LCA, no decisions need to be madeabout scaling (creating z scores) of observed variables.This issue is common and important in cluster analysis(Vermunt & Magidson, 2002) as group membershipprofiles can differ greatly in appearance based on

whether raw variables or standardized variables areused as indicators. Fifth, LCA provides class member-ship probabilities for each respondent, which allowsfor the creation of categorical or dichotomous groupmembership variables or for use of class probabilitiesas continuous variables. This outcome is much likea person-level index of discordance with the rest ofthe class. Conversely, cluster analysis provides a simpledichotomous yes/no membership outcome for each re-spondent and each cluster. The cluster analytic yes/no isan oversimplification of the true state of affairs whereasthe probability of group membership provides a moreaccurate picture of how typical the respondent is of thegroup as a whole.

Although LCA is conceptually similar to cluster anal-ysis in purpose, mathematically it is more closely re-lated to factor analysis. Both factor analysis and LCA at-tempt to account for patterns of observed responses viaone or more latent variables, although some of the out-put associated with these analyses differs (e.g., there areno eigenvalues or scree plots in LCA; however, LCA out-put indicating the association between indicators andgroup membership can be interpreted much like fac-tor loadings). Although the mathematics behind fac-tor analysis and LCA is similar, the purposes for whichthese methods are employed are significantly different.Typically, factor analysis involves identifying a numberof latent factors that are indicated by a number of items.In common uses, such as scale development, the goal isto classify scale items into homogeneous groups said toreflect the common factor. In contrast, the purpose ofa LCA is typically to classify persons into homogeneousgroups.

LCA is not a new analytic technique; the original ideawas proposed by Lazarsfeld in 1955 (Lazarsfeld, 1955).It was not until the development of new maximumlikelihood–based algorithms and computer programsin the 1970s that LCA was feasible. Shortly thereafter,LCA was quickly adopted in marketing research as thepotential of “consumer segmentation” was quickly re-alized (e.g., Green, Carmone, & Wachspress, 1976).Consumer segmentation in marketing research allowsfor a number of different advertisements geared at dif-ferent segments of the broader consumer audience. Theparallel between marketing and risk communication isclear. In both instances, the warning/advertisement ismeant to be readily noticed, readily understood, andconvincing to the viewer to take some type of action (orpotentially to avoid some type of behavior for warningsresearch).

Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm 487

Warnings in Manufacturing Goldsworthy, Mayhorn, and Meade

1.4. Use of LCA in Warnings Research

The key to creating an effective warning is to maxi-mize the likelihood that the right persons receive theright safety information and that those persons no-tice, understand, and comply with the warning mate-rial (for a comprehensive review, see Wogalter, 2006).Although traditional warnings research has attemptedto determine a single warning that can maximize thepotential of the warning for all viewers, LCA providesa valuable tool for better understanding and differenti-ating audience segments by determining whether rea-sonably homogeneous segments exist within a broadaudience. More importantly, LCA allows hazard re-searchers to consider whether those segments are suffi-ciently distinct as to merit different hazard-mitigationapproaches or whether some of the segments merit anymitigation at all.

Current warnings research has explored tailoredmessages by examining the extent to which warningsare effective across gender, ethnicity, age, socioeco-nomic status, and other demographic groups (e.g.,Lindell & Perry, 2004; Mayhorn & Podany, 2006;Turner, Nigg, & Heller-Paz, 1986). Although demo-graphically based analyses are effective at tailoring cer-tain warnings for demographic groups, these broadcategorical classifications are still quite diverse. In otherwords, although persons of Hispanic ethnicity may bea more homogenous group than the general popula-tion, there remains a great deal of heterogeneity withinthe broad classification of the Hispanic ethnicity (seePortes & Truelove, 1987, or Sommers, 1991, for a dis-cussion on the many specific ethnic identities that arecommonly lumped into the broad Hispanic/Latino cat-egory). There is no a priori reason to believe that thishomogeneity maps to a particular hazardous situation–person nexus or to interpretation and compliance witha warning regarding that particular situation. Is it likelythat workers will orient to, ignore, or misunderstand awarning based on their gender and/or ethnicity aloneor based on a complex interplay of these and otherpersonal characteristics?

Instead of focusing solely on singular characteris-tics, regardless of how many single characteristics, LCAidentifies homogeneous subsets according to latentclasses based on observed responses to a given measureor set of measures. Latent groups are much more sim-ilar to one another than are groups defined by a singleobserved categorical variable such as gender, ethnicity,and so forth. Perhaps more importantly, groups can

be defined based on any observed responses desired,such as attitudes about risky behaviors or intentionsto engage or not engage in a hazardous situation—factors which may be more relevant than genderor ethnicity in determining the efficacy of differentwarnings.

Moreover, with LCA, differences across typical de-mographic characteristics can be tested either post hocor as part of the LCA itself. To conduct post hoc anal-yses, different classes are identified based on the vari-ables in the main analysis, then the probability of be-longing to each latent group can be coded for eachrespondent, and these variables can be tested for differ-ences across traditional demographic groups via anal-ysis of variance (ANOVA). Conversely, demographiccharacteristics can be embedded within the main LCAanalysis itself by incorporating demographic informa-tion as one of the indicators used to form groups. Theeffect of membership in the latter case is the weightgiven to the demographic variable in determining thelatent classes. As an example, observed responses aboutrisk adversity can be combined with demographic mea-sures such as gender, ethnicity, and so forth, to identifylatent groups.

1.5. An Example for Warnings Research

We believe that there is a wide range of potential ap-plications of LCA to hazard mitigation. To provide abetter sense of the utility of LCA as part of hazard-mitigation research, we will discuss a particular hazard(prescription medication sharing) and describe the ap-plication of LCA as part of audience analysis regardingthat hazard. In particular, we will describe the use ofLCA to identify audience segments in relation to shar-ing prescription medication.

Medication usage is an integral component of thehealth care system in the United States. Each year,more than 3 billion prescriptions are filled, totalingmore than $200 billion in sales. Fully 64% of Americanhouseholds report the presence of at least one regularuser of prescription medications (Fox, 2004). A varietyof behaviors have been classified as unsafe medica-tion practices (Institute of Medicine, 2007). Althoughsome behaviors, such as prescription and delivery er-rors committed by health care providers (Ghaleb et al.,2006; Lesar, Briceland, & Stein, 1997; Mager, 2007),have been well-documented, others have received lessattention. One such practice is medication sharing, de-fined as lending medications to someone else or taking

488 Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm

Goldsworthy, Mayhorn, and Meade Warnings in Manufacturing

someone else’s medication (Daniel, Honein, & Moore,2003). Medication sharing is a poorly understood, so-cially complex phenomenon that occurs for a varietyof reasons, including consumers’ inadequate under-standing of critical drug-related information such astoxicity or side effects (Huott & Storrow, 1997), so-cioeconomic factors such as medication costs (An-glin & White, 1999; Bedell et al., 2000; Saradamma,Higginbotham, & Nichter, 2000), differences in cul-tural values and beliefs (Deschepper et al., 2008), easeof access from family members and friends (Daniel etal., 2003; Hurwitz, 2005), and the allure of elicit recre-ational usage (White, Becker-Blease, & Grace-Bishop,2006).

Current recommendations from the Institute ofMedicine (2007) focus on the development of poli-cies to improve provider–patient communication. Bycommunicating to the public the risks and hazardsassociated with prescription medication sharing, it ishoped that patients will avoid this potentially dan-gerous behavior. One lesson from the risk commu-nication field, however, is that the public is not ahomogenous entity: It varies widely in terms of re-ceiver characteristics such as ethnicity, age, gender, andhealth literacy, which might influence how individualsmake decisions (Lindell & Perry, 2004). In fact, Boyd,McCabe, Cranford, and Young (2007) postulated thatthere may be distinct types of prescription medicationsharers/borrowers who are defined by various demo-graphic characteristics. Subsequent research has foundthat females are more likely than males to share/borrowmedicine (Daniel et al., 2003; Simoni-Wastila, Ritter,& Strickler, 2004).

Although demographic differences are interesting,it is more important to determine whether there aredistinct classes of medication sharers and borrowersbased on the likelihood of loaning or borrowing andon situations in which someone would loan or borrow.Conditional differences such as these have been foundto distinguish classes of individuals in relation to otherhealth-related behaviors, such as smoking (Furberget al., 2005). The current investigation sampled a di-verse group of respondents within the general popula-tion and queried their sharing/borrowing behaviors fora number of different prescription medication types.The prevalence and nature of prescription medicationloaning and borrowing overall was identified and usedin an LCA to place individuals into distinct medication-sharing subgroups. From the processes and outcomesof this audience analysis, we then drew implications

for warning messaging for manufacturing processes,products, and environments.

2. METHOD

2.1. Participants

Participants were 700 adults and adolescents from10 diverse locations in the United States: Atlanta,Georgia; Cleveland, Ohio; Dallas, Texas; Greenville,South Carolina; Los Angeles, California; Miami,Florida; Philadelphia, Pennsylvania; Phoenix, Arizona;South Bend, Indiana; and Tacoma, Washington. Par-ticipants were asked to participate in a brief structuredinterview that was iteratively pilot tested to create aprocess that was valid for all age groups. They werecompensated $20 for the roughly half-hour interview.The average refusal rate across locations was approxi-mately 19%, with all participating agencies reportingtheir refusal rates as typical. Although the data werecollected from 10 locations, it should be noted thatthe majority of these sites were heavily populated ur-ban areas; thus, the generalizability of the data to ruralsettings has not been demonstrated.

Age of participants was restricted to those 12 to44 years because these data were collected in con-junction with another project (reported in Mayhorn& Goldsworthy, 2007) that assessed the comprehensi-bility of birth defects warnings; thus, consistent withprevious research in that particular domain, the ageof the sample was restricted to those of child-bearingage. Seventy-three percent of the participants were fe-male. Adolescents made up 19.7% of the sample; theremainder of the sample was evenly distributed acrossage ranges. The sample was 48.3% White, 24.3% Black,34.4% Hispanic, and 1% Asian. Twenty-four percentof the participants reported Spanish as their primarylanguage; interviews for these individuals were con-ducted in Spanish, and questionnaire items were back-translated and validated.

2.2. Measures

A structured questionnaire was used to elicit informa-tion concerning 1) prescription medication history, 2)previous prescription loaning and borrowing behav-iors, 3) endorsement of several hypothetical sharing sit-uations, and 4) additional participant characteristics.Recent prescription medication history was assessed byasking, “How many different prescription medications

Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm 489

Warnings in Manufacturing Goldsworthy, Mayhorn, and Meade

of your own have you taken in the past year?” Medica-tion loaning/borrowing history was determined by twoquestions, each with yes/no response options: “Haveyou ever shared your prescription medicine with some-one else?” (loaning) and “Has anyone ever shared theirprescription medicine with you?” (borrowing). Like-lihood of loaning/borrowing medications was assessedby having participants answer “Yes” or “No” to eachof 13 hypothetical medication loaning/borrowing sce-narios listed in the first column of Table 1. All par-ticipants were asked whether they would be likely toloan/borrow medications under each of these circum-stances, regardless of whether they had, in fact, loanedor borrowed prescription medication in the past. Last,information about participant characteristics, includ-ing demographic information, was collected.

2.3. Analysis

A common approach to determining the characteris-tics of medication loaners and borrowers might be tocompare sharers and nonsharers on a variety of itemsto determine which items best differentiate the peo-ple engaging in the behavior from those not engagingin it. As discussed earlier in this article, however, amore detailed classification can be attained by exam-ining prescription medication loaning and borrowinghistory and the 13 hypothetical sharing scenarios viaLCA using Mplus (Muthen & Muthen, 1998–2007).

Recent evidence suggests that the best way to deter-mine the number of latent classes to retain in an LCA isby examining the Bayesian Information Criterion (BIC;Nylund et al., 2007). The BIC is a parsimony-adjustedindex that can increase or decrease when additionalclasses are extracted, resulting in an inverse curve withthe lowest value usually representing the best fit. Ex-amining BIC revealed that a four-class model best fitthe data for the current study (BIC values for one to sixclasses: 11,759; 9,489; 9,238; 9,215; 9,253; and 9,294).Labeling the derived classes is accomplished by con-sidering the items endorsed by identified latent classes(i.e., the groups), those items not endorsed by mem-bers of the groups, and the relative rates of endorse-ment across the groups. To do this, we relied primarilyon two pieces of information: the probability valuesof endorsing each of the 15 items and class pair-wiseodds ratios, which indicated the degree of differencebetween two classes.

Once the classes were identified and labeled, we ex-amined differences in demographic and medication-

sharing characteristics across the groups. For the con-tinuous study variables (health literacy and medica-tion history), one-way ANOVAs were conducted us-ing Tukey post hoc comparisons to indicate whethergroups determined by the LCA differed significantlyfrom one another. Chi-square tests were used to ex-amine categorical and ordinal differences across thefour groups. When differences were found, pair-wisefollow-up chi-square tests were conducted using Bon-ferroni corrections to control for Type I errors.

3. RESULTS

3.1. Prescription Medication History

The mean number of prescriptions reported by par-ticipants during the last 12 months was 1.7 (standarddeviation [SD] = 2.3; median [M] = 1.0). The num-ber of prescriptions was highly skewed (skewness =3.62; kurtosis = 23.86) with just over one third of theparticipants reporting no prescriptions but some re-porting 20 or more. Persons reporting more than fiveprescriptions were grouped into a single category forsubsequent analyses. Specifically, 262 (37.4%) had noprescription medications, 138 (19.7%) had one pre-scription medication, 123 (17.6%) had two, 86 (12.3%)had three, 38 (5.4%) had four, and the remaining 7.6%had five or more.

3.2. Prescription MedicationLoaning/Borrowing History

Of the 700 respondents, 160 (22.9%) loaned their med-ication on at least one occasion and 188 (26.9%) bor-rowed medication at least once. The phi-correlationcoefficient between the sharing and the borrowing itemwas .53 (p < 0.001), which indicates that the overlapbetween loaning and borrowing was not uniform. Fur-thermore, 236 respondents (33.7%) reported havingeither loaned or borrowed (“sharers”), of which 112(16.0%) reported having both loaned and borrowed,and 436 (66.3%) reported having neither loaned norborrowed (“non-sharers”).

3.3. Likelihood of Sharing/Borrowing Medications

Table 1 provides frequencies and percentages for thelikelihood of sharing or borrowing medicine in eachof 13 scenarios. There were significant differences

490 Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm

Goldsworthy, Mayhorn, and Meade Warnings in Manufacturing

TAB

LE1.

Hyp

othe

tical

Shar

ing/

Borr

owin

gSi

tuat

ions

bySe

lf-Re

port

edM

edic

atio

nSh

arin

g/Bo

rrow

ing

Beha

vior

Nei

ther

Shar

eSh

arer

s/Bo

rrow

ers

Tota

lSh

are

orBo

rrow

nor

Borr

owN

on-S

hare

rs

Item

Freq

%Ye

s%

Col

Freq

%Ye

s%

Col

Freq

%Ye

s%

Col

χ2

p

Had

the

sam

epr

oble

mas

the

pers

on22

933

%10

%16

369

%11

%66

14%

7%21

3.8

<0.

001

who

has

the

med

icin

eA

lread

yha

da

pres

crip

tion

for

that

med

icin

e,27

039

%11

%16

470

%11

%10

623

%11

%14

3.7

<0.

001

but

ran

out

ordi

dno

tha

veit

with

you

Got

itfr

oma

fam

ilym

embe

r27

639

%11

%19

081

%13

%86

19%

9%25

1.6

<0.

001

Wan

ted

som

ethi

ngst

rong

for

pain

orhe

adac

he22

332

%9%

142

60%

10%

8117

%9%

131.

5<

0.00

1W

ante

dso

met

hing

stro

ngfo

rpi

mpl

esor

oily

skin

8212

%3%

3113

%2%

5111

%5%

0.70

0.4

Wan

ted

tore

lax

orfe

elgo

od11

416

%5%

6728

%5%

4710

%5%

38.3

<0.

001

Nee

ded

som

ethi

ngto

help

you

slee

p13

720

%6%

9440

%6%

439%

5%92

.8<

0.00

1G

otit

from

som

eone

who

know

s17

525

%7%

104

44%

7%71

15%

8%69

.0<

0.00

1so

met

hing

abou

tm

edic

ines

Had

anem

erge

ncy

265

38%

11%

130

55%

9%13

529

%14

%44

.9<

0.00

1H

adhe

ard

alo

tab

out

the

med

icin

e10

515

%4%

5724

%4%

4810

%5%

23.3

<0.

001

from

ads

orco

mm

erci

als

Cou

ldno

taf

ford

tobu

yth

em

edic

ine

216

31%

9%12

854

%9%

8819

%9%

91.2

<0.

001

but

you

need

edit

Had

left

over

med

icin

eth

atw

ould

bew

aste

d13

019

%5%

8134

%6%

4911

%5%

58.4

<0.

001

Had

som

eof

your

own

med

icat

ion

that

179

26%

7%11

649

%8%

6314

%7%

104

<0.

001

you

thou

ght

coul

dhe

lpa

frie

nd

Tota

lfor

colu

mn

2401

100%

100%

1467

934

100%

N70

023

646

4

Not

e:In

resp

onse

to:“

Wou

ldyo

ush

are

your

med

icin

eif

you.

..?”

Freq

:fre

quen

cyco

unts

;%ye

s:%

yes

vs.n

ow

ithin

grou

p;%

Col

:%re

spon

ses

for

colu

mn.

Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm 491

Warnings in Manufacturing Goldsworthy, Mayhorn, and Meade

between sharers and non-sharers for nearly all of thescenarios, such that persons reporting previously shar-ing or borrowing medicine were typically much morelikely to report that they would engage in sharing be-haviors under the hypothetical conditions. Exactly halfof the respondents who had never loaned or borrowedindicated that they would, under at least one scenario,share medications. The mean number of scenarios en-dorsed by the 464 non-sharer respondents was 2.01(SD = 2.83).

3.4. Classes of Medication Sharersand Borrowers

Table 2 and Table 3 report the item endorsement proba-bilities and odds ratios for all 15 items of the LCA. Classfrequency counts are also provided. Class 1 membershad extremely low probabilities of ever having loanedor borrowed medicine (items 1 and 2) and were veryunlikely to share or borrow medicine under any hypo-thetical circumstance (items 3–15). For this reason, werefer to this class as “Abstainers.” Class 1 was the largestlatent class with 279 (39.8%) respondents.

Class 2 respondents were likely to have loaned(p = 0.79) or borrowed (p = 0.86) prescriptionmedicines. All Class 2 members indicated that theywould share a medicine if they received it from a fam-ily member (p = 1.00). Members of this class were alsohighly likely to share when they had the same problemas the person with the medicine (p = 0.91) or alreadyhad a prescription but ran out or did not have it withthem (p = 0.88). They would also be likely to shareor borrow if they had an emergency (p = 0.63), couldnot afford to buy the medicine (p = 0.62), or wantedto help a friend (p = 0.63). Conversely, respondents inthis class were far less likely to share or borrow medicinewhen they wanted to relax or feel good (p = 0.20),had heard a lot about the medicine from commercials(p = 0.20), or wanted something to help them sleep(p = 0.32). They were evenly split on whether theywould share or borrow medicine for pain (p = 0.51).Because medication history indicated a high probabil-ity of having previously loaned or borrowed medicine,and the pattern of endorsement indicated that shar-ing likely occurred (or would occur) for pragmatic,situation-specific reasons, we refer to this group as“Pragmatic Frequent Sharers.” Class 2 was the thirdlargest class with 112 (16%) of the sample.

Class 3 respondents were evenly split in theirprobability of having loaned (p = 0.45) or borrowed

(p = 0.55) medicine. The probabilities of endorsinghypothetical situations under which they would shareor borrow were high, however. That is, althoughClass 3 respondents were somewhat less likely thanwere Class 2 respondents to indicate previous loaningor borrowing, they were more likely than were mem-bers of all other classes to say that they would share ineach situation (with the exception of “got it from a fam-ily member”). Across items, the probabilities for Class3 ranged from 0.45 (pimples or oily skin) to 0.89 (help-ing a friend). Class 3 respondents were not only likelyto endorse pragmatic reasons for loaning/borrowing,but they were also likely to endorse sharing situationsthat have little to do with access: They would borrowmedicine to relax or feel good (p = 0.66), help themsleep (p = 0.66), or for pain (p = 0.88). The proba-bility of endorsing these items was much higher forClass 3 than for any other class. Members of Class 3were also far more likely than members of other classesto indicate that they would share or borrow a pre-scription medication that they had heard about fromadvertisements (p = 0.63). Given the somewhat lowerfrequency of actual reported loaning/borrowing butthe high probability of loaning or borrowing in the fu-ture in both pragmatic and outcome-based situations,we labeled this group as “At-Risk Sharers.” This classwas the smallest at 73 members (10.4% of the sample).

Finally, Class 4 respondents were unlikely to haveloaned (p = 0.17) or borrowed (p = 0.22) medicine inthe past and were generally unlikely to share or bor-row in the future (p < 0.50 for all situations). Thelow probability of having previously loaned clearly dif-ferentiates this class from Class 2, as do the generallylower probabilities of future sharing associated with thehypothetical scenarios. Unlike Class 1 Abstainers, how-ever, this group would be somewhat likely to share un-der some circumstances (e.g., emergencies, p = 0.48).Class 4 was labeled “Emergency Sharers.” This classwas the second largest with 236 members (33.7% ofthe sample).

3.5. Class and ParticipantCharacteristics

There were significant differences in the number ofprescriptions between the four classes (F = 93.2, df =3,696, p < 0.001) with Tukey post hoc tests indicatingthat Pragmatic Frequent Sharers had significantly moreprescriptions than the other classes (M = 2.29, SD =1.72), with no differences among the other classes:

492 Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm

Goldsworthy, Mayhorn, and Meade Warnings in Manufacturing

TAB

LE2.

Item

Prob

abili

ties,

byLa

tent

Cla

ss

Cla

ss1

Cla

ss2

Cla

ss3

Cla

ss4

Item

p95

%C

Ip

95%

CI

p95

%C

Ip

95%

CI

Hav

eyo

uev

ersh

ared

your

pres

crip

tion

med

icin

ew

ithso

meo

neel

se?

0.00

(−0.

01,0

.01)

0.79

(0.6

5,0.

92)

0.45

(0.1

1,0.

79)

0.17

(0.0

9,0.

24)

Has

anyo

neel

seev

ersh

ared

thei

rpr

escr

iptio

nm

edic

ine

with

you?

0.00

(−0.

01,0

.01)

0.86

(0.7

4,0.

98)

0.55

(0.2

9,0.

81)

0.22

(0.1

1,0.

32)

Had

the

sam

epr

oble

mas

the

pers

onw

hoha

sth

em

edic

ine

0.00

(−0.

01,0

.01)

0.91

(0.7

3,1.

08)

0.79

(0.5

1,1.

06)

0.29

(0.2

2,0.

36)

Alre

ady

had

apr

escr

iptio

nfo

rth

atm

edic

ine,

but

ran

out

0.02

(0.0

0,0.

05)

0.88

(0.7

6,1.

00)

0.75

(0.5

4,0.

96)

0.46

(0.3

8,0.

54)

ordi

dno

tha

veit

with

you

Got

itfr

oma

fam

ilym

embe

r0.

02(−

0.01

,0.0

4)1.

00(1

.00,

1.00

)0.

80(0

.55,

1.05

)0.

42(0

.31,

0.53

)W

ante

dso

met

hing

stro

ngfo

rpa

inor

head

ache

0.01

(−0.

01,0

.03)

0.51

(0.3

7,0.

65)

0.88

(0.6

8,1.

08)

0.40

(0.3

1,0.

49)

Wan

ted

som

ethi

ngst

rong

for

pim

ples

oroi

lysk

in0.

01(−

0.01

,0.0

2)0.

07(0

.00,

0.14

)0.

45(0

.14,

0.76

)0.

16(0

.09,

0.23

)W

ante

dto

rela

xor

feel

good

0.00

(0.0

0,0.

00)

0.20

(0.0

6,0.

33)

0.66

(0.4

,0.9

1)0.

18(0

.10,

0.26

)N

eede

dso

met

hing

tohe

lpyo

usl

eep

0.00

(0.0

0,0.

00)

0.32

(0.1

5,0.

49)

0.66

(0.4

9,0.

83)

0.21

(0.1

4,0.

29)

Got

itfr

omso

meo

new

hokn

ows

som

ethi

ngab

out

med

icin

es0.

01(0

.00,

0.03

)0.

44(0

.27,

0.62

)0.

84(0

.7,0

.97)

0.25

(0.1

8,0.

31)

Had

anem

erge

ncy

0.07

(0.0

4,0.

11)

0.63

(0.4

6,0.

81)

0.81

(0.6

9,0.

92)

0.48

(0.3

9,0.

56)

Had

hear

da

lot

abou

tth

em

edic

ine

from

ads

orco

mm

erci

als

0.00

(0.0

0,0.

00)

0.20

(0.0

0,0.

40)

0.63

(0.4

8,0.

78)

0.15

(0.0

8,0.

21)

Cou

ldno

taf

ford

tobu

yth

em

edic

ine

but

you

need

edit

0.01

(−0.

01,0

.02)

0.62

(0.4

2,0.

83)

0.83

(0.6

4,1.

02)

0.35

(0.2

7,0.

43)

Had

left

over

med

icin

eth

atw

ould

bew

aste

d0.

01(−

0.01

,0.0

2)0.

37(0

.11,

0.62

)0.

74(0

.58,

0.9)

0.14

(0.0

8,0.

20)

Had

som

eof

your

own

med

icat

ion

that

you

thou

ght

0.01

(0.0

0,0.

03)

0.63

(0.3

1,0.

95)

0.89

(0.7

3,1.

05)

0.17

(0.1

,0.2

3)co

uld

help

afr

iend

Cla

ssco

unts

(num

ber

ofre

spon

dent

spe

rcl

ass)

279

112

7323

6

Not

e:C

I:co

nfide

nce

inte

rval

.

Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm 493

Warnings in Manufacturing Goldsworthy, Mayhorn, and Meade

TAB

LE3.

Odd

sRa

tios

Cla

ssC

ompa

rison

s

1vs

.21

vs.3

1vs

.42

vs.3

2vs

.43

vs.4

Item

OR

95%

CI

OR

95%

CI

OR

95%

CI

OR

95%

CI

OR

95%

CI

OR

95%

CI

Hav

eyo

uev

ersh

ared

your

1,12

7.7

∗∗25

3.3

(−55

1,1,

057)

61.3

(−14

0,26

2)0.

2(−

0.2,

0.65

)0.

1(0

.01,

0.1)

0.2

(−0.

18,0

.66)

pres

crip

tion

med

icin

ew

ithso

meo

neel

se?

Has

anyo

neel

seev

ersh

ared

2,46

8.6

∗∗49

8.2

(−1,

458,

2,45

5)11

3.5

(−36

0,58

7)0.

2(−

0.05

,0.4

5)0.

0(−

0.02

,0.1

1)0.

2(−

0.11

,0.5

6)th

eir

pres

crip

tion

med

icin

ew

ithyo

u?H

adth

esa

me

prob

lem

asth

e4,

830.

9∗∗

1,86

9.4

∗∗20

7.6

(−93

5,1,

350)

0.4

(−0.

94,1

.71)

0.0

(−0.

04,0

.13)

0.1

(−0.

08,0

.31)

pers

onw

hoha

sth

em

edic

ine

Alre

ady

had

apr

escr

iptio

nfo

r30

6.4

(−19

1,80

4)12

7.0

(−67

,321

)36

.5(−

8,81

)0.

4(−

0.41

,1.2

4)0.

1(−

0.01

,0.2

5)0.

3(−

0.06

,0.6

4)th

atm

edic

ine,

but

ran

out

ordi

dno

tha

veit

with

you

Got

itfr

oma

fam

ilym

embe

r∗∗

∗∗∗

202.

7(−

149,

554)

37.0

(−16

,90)

0.0

(0,0

)0.

0(0

,0)

0.2

(−0.

15,0

.52)

Wan

ted

som

ethi

ngst

rong

for

77.1

(−38

,192

)56

3.4

(−77

4,1,

901)

49.4

(−23

,122

)7.

3(−

6.33

,20.

94)

0.6

(0.1

7,1.

11)

0.1

(−0.

1,0.

27)

pain

orhe

adac

heW

ante

dso

met

hing

stro

ngfo

r15

.6(−

31,6

2)17

7.1

(−34

7,70

2)41

.0(−

78,1

60)

11.3

(−8.

4,31

.07)

2.6

(−0.

44,5

.68)

0.2

(−0.

14,0

.6)

pim

ples

oroi

lysk

inW

ante

dto

rela

xor

feel

good

∗∗∗

∗∗∗∗

∗∗∗

∗∗∗

∗∗∗

7.8

(−0.

27,1

5.94

)0.

9(−

0.18

,1.9

5)0.

1(−

0.06

,0.2

8)N

eede

dso

met

hing

tohe

lp∗∗

∗∗∗

∗∗∗

∗∗∗

∗∗∗

∗∗4.

2(0

.31,

8.06

)0.

6(0

,1.1

6)0.

1(0

,0.2

8)yo

usl

eep

Got

itfr

omso

meo

new

ho55

.0(−

20,1

30)

352.

3(−

183,

887)

22.8

(−5.

33,5

0.96

)6.

4(−

2.57

,15.

38)

0.4

(0.0

8,0.

75)

0.1

(0,0

.13)

know

sso

met

hing

abou

tm

edic

ines

Had

anem

erge

ncy

21.5

(1.8

,41.

3)52

.0(4

.5,9

9.5)

11.3

(4.2

8,18

.34)

2.4

(−0.

5,5.

32)

0.5

(0.0

5,1)

0.2

(0.0

5,0.

39)

Had

hear

da

lot

abou

tth

e∗∗

∗∗∗

∗∗∗

∗∗∗∗

∗∗∗

6.9

(−2.

2,16

.09)

0.7

(−0.

42,1

.83)

0.1

(0.0

1,0.

19)

med

icin

efr

omad

sor

com

mer

cial

sC

ould

not

affo

rdto

buy

the

351.

5(−

681,

1,38

4)1,

015.

5∗∗

115.

4(−

205,

436)

2.9

(−3.

02,8

.8)

0.3

(0.0

2,0.

64)

0.1

(−0.

04,0

.27)

med

icin

ebu

tyo

une

eded

itH

adle

ftov

erm

edic

ine

that

112.

4(−

190,

415)

554.

1(−

936,

2,04

4)30

.5(−

52,1

13)

4.9

(−2.

92,1

2.77

)0.

3(−

0.12

,0.6

6)0.

1(0

.01,

0.1)

wou

ldbe

was

ted

Had

som

eof

your

own

154.

2(−

141,

450)

749.

9(−

839,

2,33

9)18

.3(−

8,45

)4.

9(−

9,19

)0.

1(−

0.08

,0.3

1)0.

0(−

0.01

,0.0

6)m

edic

atio

nth

atyo

uth

ough

tco

uld

help

afr

iend

Not

es:

OR:

odds

ratio

;C

I:co

nfide

nce

inte

rval

.A

ster

isks

deno

teth

eco

mpu

tatio

nof

high

lysi

gnifi

cant

odds

ratio

sbe

twee

nth

eab

stai

ners

(Cla

ss1)

who

dem

onst

rate

da

num

ber

ofex

trem

ely

low

prob

abili

tybe

havi

ors

and

othe

rcl

asse

s.

494 Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm

Goldsworthy, Mayhorn, and Meade Warnings in Manufacturing

At-Risk Sharers (M = 1.68, SD = 1.61), EmergencySharers (M = 1.40, SD = 1.51), and Abstainers(M = 1.25, SD = 1.46).

Table 4 provides descriptive statistics for the categor-ical demographic variables. The At-Risk Sharers weresignificantly more likely than the other three classesto report making less than $25,000/year, despite show-ing no differences in employment status. The At-RiskSharers also had a higher percentage of respondentsindicating that they were Hispanic and spoke Spanishas their primary language. There were no significantdifferences in marital status, education, ethnicity, age,or gender across the four classes.

4. DISCUSSION

The identification of latent classes based on behav-iors of interest facilitates tailoring hazard-mitigationefforts to specific groups. This ability to tailor to spe-cific classes of at-risk individuals can inform process,product, and environment (re)design, gate-keeping,and educational and warnings messaging. Such tar-geting could increase the effectiveness of, for example,warnings for the particular groups that they are de-signed to reach. For example, in this study, four typesof medication sharers were identified based on patternsof endorsement: Abstainers, Pragmatic Frequent Shar-ers, At-Risk Sharers, and Emergency Sharers. Becauseeach of these groups demonstrates different medicationloaning and borrowing behaviors, they are likely to re-spond in different ways to messages about medicationsharing. Examples of ways to tailor communications tomitigate risk in each class follow.

For participants who have not shared and do not ap-pear likely to do so, such as members of the Abstainersand Emergency Sharers groups, messages should rein-force the overall risks of sharing and call attention tospecific risky sharing situations. Care should be takento prevent less risky sharing situations from becomingnormalized and to avoid diluting message importanceby increased frequency of message encounters.

For the Pragmatic Frequent Sharers, risk mitigationefforts should examine the types of medications al-ready being shared and accentuate the risks of sharinghigh-risk medicines, emphasizing that, although theseindividuals may have loaned or borrowed in the past,there are situations in which loaning or borrowing canbe hazardous. It may also be worthwhile to raise theissue of drug interactions because Pragmatic Frequent

Sharers tended to have more prescriptions than didmembers of other classes.

Because At-Risk Sharers are less likely to have pre-viously shared but are more likely to do so in a widervariety of circumstances than all other groups, theyshould be made aware of the wide range of issues as-sociated with specific types of sharing. Of particularnote, the At-Risk Sharers are most likely to be affectedby direct-to-consumer advertising (DTCA) of phar-maceuticals, a practice that has become increasinglyprevalent over the past two decades (Bell, Kravitz, &Wilkes, 1999). Although DTCA may provide increasedawareness of medical conditions and treatment op-tions, there is considerable debate regarding the overalleffect that DTCA may have on the nation’s public health(Almasi, Stafford, Kravitz, & Mansfield, 2006; Frosch,Krueger, Hornik, Cronholm, & Barg, 2007; Hind,Pilgrim, & Ward, 2007; Mintzes et al., 2003). In ad-dition to concerns that DTCA is creating an increaseddemand for (unnecessary) prescriptions, it seems rea-sonable to suggest that ubiquitous exposure may leadto increased familiarity and decreased perceptions ofrisk associated with the medicines. As such, exposureto DTCA may lead to increased likelihood of sharingprescription medications. In fact, At-Risk Sharers arefar more willing to share under most conditions, in-cluding if they had “heard about it from ads” (63%).This finding seems to indicate that DTCA may serve to“prime the pump” for sharing.

The study also confirmed previous findings that lowincome and Hispanic individuals may be dispropor-tionately at risk for engaging in risky sharing behav-iors than are other individuals. Given the high repre-sentation of low income and Hispanic individuals inthe At-Risk class and the finding that At-Risk Shar-ers are more likely to share when having heard about amedicine in advertisements, it seems important to notethat DTCA disclaimers about risks and side effects areusually presented verbally in English, without visualaccompaniment. It is reasonable to presume that suchverbal messages are not discerned, much less under-stood, by non–English speakers. Changing these mes-sages to more clearly communicate the potential sideeffects may be an important step toward mitigating riskbroadly as well as specifically within these groups. Thusalterations to packaging and warning content shouldprobably be accompanied by changes to the process ofmedication dissemination so that health care providersmust provide an individualized educational compo-nent before a drug is released to a patient.

Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm 495

Warnings in Manufacturing Goldsworthy, Mayhorn, and Meade

TABLE 4. Latent Class by Participant Characteristics

Class 1 Class 2 Class 3 Class 4Comparisons Abstainers Frequent Sharers At Risk Only When Necessary Totals

Which medicines†

Allergy∗ 6% 51%1,4 44%1 30%1 25%Pain∗ 2% 22%1 30%1,4 13%1 12%Mood 0% 4% 1% 1% 1%Antibiotics∗ 1% 9%1 5%1 8%1 5%Acne 1% 1% 1% 3% 2%Birth Control 0% 1% 0% 1% 1%None∗ 90%2,3,4 8% 18% 42%2,3 53%

Married? 37% 36% 37% 31% 35%Have children?† 48% 53% 59%4 40% 47%Education

Less than High School 26% 33% 37% 38% 33%High School Graduate 25% 28% 27% 24% 25%Some College or Technical School 30% 27% 26% 25% 27%College Graduate 13% 11% 7% 10% 11%Graduate School or Higher 6% 2% 3% 3% 4%

Annual Income†

Do Not Know 11% 16% 5% 14% 12%Less than $25K∗ 24% 22% 48%1,2,4 27% 27%$25K–$35K 19% 20% 25% 17% 19%$35K–$50K 18% 13% 5% 14% 15%$50K–$75K 14% 10% 5% 10% 11%$75K–$100K 5% 8% 5% 10% 7%>$100K 4% 9% 5% 6% 6%Did Not Respond 4% 2% 0% 3% 3%

Employment Status†

Part Time 16% 29% 26% 23% 22%Full Time∗ 51%4 40% 41% 34% 43%Not Working 33% 30% 33% 43% 36%

Hispanic† 33% 29% 53%1,2,4 33% 34%Ethnicity

White 60% 74% 69% 65% 65%Black 37% 26% 23% 32% 32%Pacific Islander 1% 0% 4% 1% 1%Asian 1% 0% 2% 1% 1%Native American 0% 0% 0% 1% 1%

Spanish as Primary Language† 23% 21% 42%1,2,4 21% 24%Age

12–15 16% 21% 14% 25% 20%16–20 12% 13% 7% 16% 13%21–25 18% 14% 23% 18% 18%26–30 13% 16% 18% 13% 14%31–35 12% 11% 15% 10% 11%36–40 14% 7% 8% 7% 10%41–44 15% 18% 15% 11% 14%

GenderFemale 71% 78% 71% 73% 73%Male 29% 22% 29% 27% 27%

Notes: †Significant chi-square test for variable as a whole. ∗Significant chi-square across four classes at that level of the vari-able using Bonferroni. Superscripts 1–4 indicate significantly greater than the class referenced using Bonferroni correction.

496 Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm

Goldsworthy, Mayhorn, and Meade Warnings in Manufacturing

Having this level of data available, data that indi-cate the presence of differing types of target audiencemembers, where the “types” are richer than typical de-mographic categories, may be of value across a rangeof manufacturing hazards. Regardless of whether thehazard is a part of the manufacturing process or a prod-uct of it, understanding how audience members formgroups on a complex array of beliefs, behaviors, andpersonal characteristics is a step toward a more richunderstanding of our target audience. Not all hazardsare embedded in such complex matrices, but most haz-ards involve some level of personal decision making andself-evaluation of the likelihood of harm. As such theyare frequently more complex, although not always ap-parently so, than simply knowing what to do and whatnot to do. Understanding how employees and con-sumers cluster on engagement in harmful behaviors,beliefs about those behaviors, and personal character-istics should, therefore, in many cases allow us to bettercreate interventions to reduce risk and harm. There-fore, further use of LCA, or similar person-clusteringanalytical techniques, should be explored in manufac-turing hazard-mitigation efforts to better understandhow these tools can improve workplace and consumersafety.

5. CONCLUSION

Hazard mitigation is a vital part of workplace and con-sumer safety. A three-level hazard-mitigation frame-work provides guidance as to when and how hazardscan be removed and/or addressed. An important aspectof such a framework is consumer and worker educa-tional and warning messaging. Audience analysis, un-derstanding who we are talking to and why, is a centralcomponent of effective message design. In this article,we have illustrated the potential of LCA for warningsdevelopment. In the case of sharing prescription med-ication, a potentially hazardous consumer behavior,LCA was valuable for identifying types of medicationsharers and may be useful in an array of educational,warning, and mitigation efforts in manufacturing envi-ronments. A wide range of factors can be used in LCA,yielding a rich description that reduces the granularityof the data. In addition, grouping data by people ratherthan by items (as in factor analysis) may be more inline with the purposes of behavior change efforts. Incontrast, data must be dichotomous (although othersimilar types of analysis can handle other types of data),analysis cannot be done in the most common soft-

ware programs, and, commensurate with factor analy-sis, the interpretation of the results requires researcherjudgment.

We believe that LCA offers a valuable approach toaudience analysis for manufacturing products and pro-cesses, particularly for message targeting and mes-sage tailoring, is a worthwhile addition to our ana-lytical toolkit, and deserves further attention. In fact,LCA and similar statistical tools appear to align wellwith broad behavioral change models, such as the Na-tional Institute of Mental Health (NIMH) consensushealth behavioral change model, Health Belief Model,and reasoned action theories, all of which incorpo-rate a large number of individual audience variablesand which often highlight the impact of demographicfactors on risk perception and health-related deci-sion making (c.f., Ajzen & Fishbein, 2005; Becker,1990; Fishbein et al., 2001; Fisher & Fisher, 2002;Goldsworthy & Fortenberry, 2009; Sethares & Elliott,2004). Such theoretical frameworks, tied with statisticaltools such as LCA, may strengthen our audience anal-ysis tools and allow us to build more effective and ef-ficient professional development and worker-trainingprograms. Coupled together, they would enable us, ina theoretically and methodologically rich manner, toidentify homogenous groups within the broader pop-ulation of respondents and across many variables and,as a result, to tailor messages to these better-specifiedgroups.

Overall, we recommend increased awareness of thesystematic processes available for mitigation of hazardsacross the three levels of hazard control. We believe thatadditional attention to audience analysis during suchactivities, particularly during message design, can leadto improvements in mitigation efforts in manufactur-ing, and we specifically suggest increased use of LCA asan analytic tool for such message design.

ACKNOWLEDGMENTS

The research discussed herein was supported by grants1R43DD00001-01 and 1R44DD00001-01 to the firstauthor from the National Center on Birth Defects andDevelopmental Disabilities (NCBDDD), a part of theCenters for Disease Control and Prevention (CDC).

References

Ajzen, I., & Fishbein, M. (2005). The influence of attitudeson behavior. In D. Albarracın, B. T. Johnson, & M. P.

Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm 497

Warnings in Manufacturing Goldsworthy, Mayhorn, and Meade

Zanna (Eds.), The handbook of attitudes (pp. 173–221).Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

Almasi, E. A., Stafford, R. S., Kravitz, R. L., & Mansfield,P. R. (2006). What are the public health effects of direct-to-consumer drug advertising? PLoS Medicine, 3(3),e145.

Anglin, M. K., & White, J. C. (1999). Poverty, health care,and problems of prescription medication: A case study.Substance Use & Misuse, 34(14), 2073–2093.

Barker, M. R., Bailey, J. S., & Lee, N. (2004). The impactof verbal prompts on child safety-belt use in shoppingcarts. Journal of Applied Behavior Analysis, 37(4), 527–530.

Becker, M. H. (1990). Theoretical models of adher-ence and strategies for improving adherence. In S. A.Shumaker, E. B. Schron, J. K. Ockene, C. T. Parker, & J.L. Probstfield (Eds.), The handbook of health behaviorchange (pp. 5–43). New York: Springer Publishing Co.

Bedell, S. E., Jabbour, S., Goldberg, R., Glaser, H., Gobble,S., Young-Xu, Y., et al. (2000). Discrepancies in the use ofmedications. Archives of Internal Medicine, 160, 2129–2134.

Bell, R. A., Kravitz, R. L., & Wilkes, M. S. (1999). Direct-to-consumer prescription drug advertising and thePublic. JGIM: Journal of General Internal Medicine,14, 651–657.

Boyd, C. J., McCabe, S. E., Cranford, J. A., & Young, A.(2007). Adolescents’ motivations to abuse prescriptionmedications. Pediatrics, 118, 2472–2480.

Daniel, K. L., Honein, M. A., & Moore, C. A. (2003).Sharing prescription medication among teenage girls:Potential danger to unplanned/undiagnosed pregnan-cies. Pediatrics, 111(5 Part 2), 1167–1170.

DeJoy, D. M. (1999). Attitudes and beliefs. In M. S.Wogalter, D. M. DeJoy, & K. R. Laughery (Eds.), Warn-ings and risk communication (pp. 189–219). Philadel-phia, PA: Taylor & Francis.

Deschepper, R., Grigoryan, L., Lundborg, C. S., Hofstede,G., Cohen, J., Van Der Kelen, G., et al. (2008). Are cul-tural dimensions relevant for explaining cross-nationaldifferences in antibiotic use in Europe? BMC HealthServices Research, 8(123).

Edworthy, J., & Adams, A. (1996). Warning design: Aresearch prospective. London: Taylor & Francis.

Fishbein, M., Triandis, H. C., Kanfer, F. H., Becker, M.,Middlestadt, S. E., & Eichler, A. (2001). Factors in-fluencing behavior and behavior change. In A. Baum,T. A. Revenson, & J. E. Singer (Eds.), Handbook ofhealth psychology (pp. 3–17). Mahwah, NJ: LawrenceErlbaum Associates.

Fisher, J. D., & Fisher, W. A. (2002). The information-motivation-behavioral skills model. In R. J.DiClemente, R. A. Crosby, & M. C. Kegler (Eds.),

Emerging theories in health promotion practice andresearch (pp. 40–70). San Francisco, CA: Jossey-Bass.

Fox, S. (2004). Prescription drugs online. Washington,DC: Pew Internet and American Life Project.

Frosch, D. L., Krueger, P. M., Hornik, R. C., Cronholm,P. F., & Barg, F. K. (2007). Creating demand for pre-scription drugs: A content analysis of television direct-to-consumer advertising. Annals of Family Medicine,5(1), 6–13.

Furberg, H. (2005). The types of regular cigarette smok-ers: A latent class analysis. Nicotine and Tobacco Re-search, 7, 351–360.

Ghaleb, M. A., Barber, N., Franklin, B. D., Yeung, V. W. S.,Khaki, Z. F., & Wong, I. C. K. (2006). Systematic reviewof medication errors in pediatric patients. The Annalsof Pharmacotherapy, 40(10), 1766–1776.

Goldsworthy, R. C., & Fortenberry, J. D. (2009). Patternsand determinants of patient-delivered partner therapyuptake among healthcare consumers. Sexually Trans-mitted Diseases, 36(1), 25–32.

Goldsworthy, R. C., & Kaplan, B. (2006a). Exploratoryevaluation of several teratogen warning symbols. BirthDefects Research. Part A, Clinical and Molecular Tera-tology, 76(6), 453–460.

Goldsworthy, R. C., & Kaplan, B. (2006b). Warning sym-bol development: A case study on teratogen symbol de-sign and evaluation. In M. S. Wogalter (Ed.), Handbookof warnings (pp. 739–754). Mahwah, NJ: Lawrence Erl-baum Associates.

Green, B. F. (1951). A general solution for the latentclass model of latent structure analysis. Psychometrika,16(2), 151–166.

Green, P. E., Carmone, F. J., & Wachspress, D. P. (1976).Consumer segmentation via latent class analysis. Jour-nal of Consumer Research, 3(3), 170–174.

Harrell, W. A. (2003). Effect of two warning signs on adultsupervision and risky activities by children in groceryshopping carts. Psychological Reports, 92(3), 889–898.

Hind, D., Pilgrim, H., & Ward, S. (2007). The direct-to-consumer advertising genie. Lancet, 369, 1.

Huott, M. A., & Storrow, A. B. (1997). A survey ofadolescent’s knowledge regarding toxicity of over-the-counter medications. Academic Emergency Medicine,4(3), 214–218.

Hurwitz, W. (2005). The challenge of prescription drugmisuse: A review and commentary. Pain Medicine, 6(2),152–161.

Institute of Medicine. (2007). Crossing the quality chasm:Preventing medication errors. Washington, DC: Na-tional Academies Press.

Kaiser, H. F. (1958). The varimax criterion for analyticrotation in factor analysis. Psychometrika, 23(3), 187–200.

498 Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm

Goldsworthy, Mayhorn, and Meade Warnings in Manufacturing

Laughery, K. R. (2006). Safety communications: Warn-ings. Applied Ergonomics, 37(4), 467–478.

Lazarsfeld, P. F. (1955). Recent developments in latentstructure analysis. Sociometry, 18(4), 391–403.

Lesar, T. S., Briceland, L., & Stein, D. S. (1997). Factorsrelated to errors in medication prescribing. JAMA: TheJournal Of The American Medical Association, 277(4),312–317.

Lindell, M. K., & Perry, R. W. (2004). Risk communica-tion. Thousand Oaks, CA: Sage Publications.

Mager, D. R. (2007). Medication errors and the home carepatient. Home Healthcare Nurse, 25(3), 151–155.

Massy, W. F. (1965). Discriminant-analysis of audiencecharacteristics. Journal of Advertising Research, 5(1),39–48.

Mayhorn, C. B., & Goldsworthy, R. C. (2007). Refin-ing teratogen warning symbol design for diverse pop-ulations. Birth Defects Research. Part A, Clinical andMolecular Teratology, 79, 494–506.

Mayhorn, C. B., & Podany, K. I. (2006). Older adults andwarnings. In M. S. Wogalter (Ed.), The handbook ofwarnings (pp. 355–361). Mahwah, NJ: Lawrence Erl-baum Associates.

Mayhorn, C. B., Wogalter, M. S., & Bell, J. L. (2004). Arewe ready? Misunderstanding homeland security safetysymbols. Ergonomics in Design, 12(4), 6–14.

Mintzes, B., Barer, M. L., Kravitz, R. L., Bassett, K.,Lexchin, J., Kazanjian, A., et al. (2003). How does direct-to-consumer advertising (DTCA) affect prescribing? Asurvey in primary care environments with and with-out legal DTCA. CMAJ: Canadian Medical AssociationJournal, 169(5), 405–412.

Muthen, B. O. (2001). Latent variable mixture model-ing. In G. A. Marcoulides & R. E. Schumacker (Eds.),New developments and techniques in structural equa-tion modeling (pp. 1–34). Mahwah, NJ: Lawrence Erl-baum Associates.

Muthen, L. K., & Muthen, B. O. (1998–2007). Mplus user’sguide (4th ed.). Los Angeles, CA: Muthen & Muthen.

Nylund, K. L., Asparouhov, R., & Muthen, B. O. (2007).Deciding on the number of classes in latent class analysisand growth mixture modeling: A Monte Carlo simula-tion study. Structural Equation Modeling, 14, 535–569.

Portes, A., & Truelove, C. (1987). Making sense of di-versity: Recent research on Hispanic minorities in theUnited States. Annual Review of Sociology, 13, 359–385.

Sanders, M. S., & McCormick, E. J. (1993). Human fac-tors in engineering and design (7th ed.). New York:McGraw-Hill.

Saradamma, R. D., Higginbotham, N., & Nichter, M.(2000). Social factors influencing the acquisition of an-tibiotics without prescription in Kerala State, South In-dia. Social Science & Medicine, 50, 891–903.

Sethares, K. A., & Elliott, K. (2004). The effect of tai-lored message intervention on heart failure readmissionrates, quality of life, and benefit and barrier beliefs inpersons with heart failure. Heart & Lung, 33(4), 249–260.

Simoni-Wastila, L., Ritter, G., & Strickler, G. (2004). Gen-der and other factors associated with the nonmedicaluse of abusable prescription drugs. Substance Use andMisuse 39(1), 1–23.

Smith-Jackson, T. L., Wogalter, M. S., & Quintela, Y.(2008). Cultural ergonomics and the pesticide risk di-vide. Proceedings of the 2nd International Conferenceon Applied Human Factors and Ergonomics, Las Vegas,Nevada.

Sommers, L. K. (1991). Inventing Latinismo: The creationof “Hispanic” panethnicity in the United States. TheJournal of American Folklore, 104(411), 32–53.

Turner, R. H., Nigg, J. M. , & Heller-Paz, D. (1986).Waiting for disaster. Los Angeles, CA: University ofCalifornia Press.

Vermunt, J. K., & Magidson, J. (2002). Latent class clus-ter analysis. In J. A. Hagenaars & A. L. McCutcheon(Eds.), Applied latent class analysis. Cambridge, UK:Cambridge University Press.

Westley, B. H., & Lynch, M. D. (1962). Multiple factor-analysis of dichotomous audience data. JournalismQuarterly, 39(3), 369–372.

White, B. P., Becker-Blease, K. A., & Grace-Bishop, K.(2006). Stimulant medication use, misuse, and abuse inan undergraduate and graduate student sample. Journalof American College Health, 54(5), 261–268.

Wogalter, M. S. (2006). Handbook of warnings. Mahwah,NJ: Lawrence Erlbaum Associates.

Wogalter, M. S., & Mayhorn, C. B. (2005). Providing cog-nitive support with technology-based warning systems.Ergonomics, 48, 522–533.

Wogalter, M. S., Vigilante, W. J., & Conzola, V. C. (1999).Usability principles applied to the development ofwarnings. Proceedings of the Human Factors and Er-gonomics Society 43rd Annual Meeting, Santa Monica,CA: Human Factors and Ergonomics Society.

Wolff, J. S., & Wogalter, M. S. (1993). Test and de-velopment of pharmaceutical pictorials. Interface ’93,(pp.187–192). Santa Monica, CA: Human Factors andErgonomics Society.

Wolff, J. S., & Wogalter, M. S. (1998). Comprehension ofpictorial symbols: Effects of context and test method.Human Factors, 40, 173–186.

Wright, J. W., Griffin, R., MacLennan, P. A., Rue,L. W., & McGwin, G. (2008). The incidence of shop-ping cart-related injuries in the United States, 2002-2006. Accident Analysis and Prevention, 40(3), 1253–1256.

Human Factors and Ergonomics in Manufacturing & Service Industries DOI: 10.1002/hfm 499