Sage Journals: Discover world-class research

Abstract

Introduction:

Public stigma of Alzheimer’s disease (AD) can delay help-seeking and be a barrier to research participation. This study aimed to understand what, if any, patterns exist among aspects of AD stigma. Knowing this may advance opportunities to reduce AD stigma.

Methods:

Adult respondents (N = 317) read a vignette about a man with mild stage AD dementia. Afterward, respondents answered the modified Family Stigma in Alzheimer’s Disease Scale (FS-ADS), which measures: Structural Discrimination, Negative Severity Attributions, Negative Aesthetic Attributions, Antipathy, Supportiveness, Pity, and Social Distance. In this correlational study, latent class analyses were used to derive response profiles. Regression models were used to assess correspondence of personal characteristics with profile membership.

Results:

Three profiles emerged from latent class analysis of four FS-ADS domains of: Structural Discrimination, Negative Severity Attributions, Supportiveness, and Social Distance. Two profiles characterized about 66.8% of respondents and were similar except for one distinguishing quality: beliefs that a person with AD would receive support from others. Additionally, membership in the “expecting higher support” profile was associated with identifying as White and having lower educational attainment, while membership in the “expecting lower support” profile was associated with relatively higher educational attainment.

Conclusions:

Beliefs about a lack of support, worries about discrimination, and expectations of social distance may depict a topic cluster to leverage in public messaging campaigns intended to reduce AD stigma. In doing so, our findings suggest it will be essential to consider the age and education level of the intended audience.

Keywords

Alzheimer’s disease Alzheimer’s stigma public profiles latent class analysis prognosis dementia

Introduction

Alzheimer’s disease (AD) is a debilitating neurological condition, causing individuals to have worsening impairment until they can no longer function in daily life. In recent decades, advances in testing have made it possible for clinicians to diagnose AD in earlier stages of disease. Yet, the diagnosis is still defined by cognitive and functional symptoms, which the public tends to overattribute. This over-attribution of severity is a hallmark of the social process that often accompanies an AD diagnosis, which is referred to as AD stigma.

Finding effective ways to reduce AD stigma is crucial. The number of people with AD is expected to nearly triple in coming decades. AD stigma can lead people with the disease to feel patronized, stereotyped, isolated, and discriminated against.^1,2 Thus, AD stigma can exert an influence that affects a wide range of important outcomes, including discouraging a person from seeking a diagnosis and inhibiting members of the public from adequately educating themselves about the disease.^3,4

AD stigma is multifactorial, consisting of prejudicial beliefs, negative attributions, and discriminatory behaviors that affect people closely associated with the disease. The prevalence of beliefs and attributions that characterize AD stigma vary in the general public.^5,6 The most common beliefs that have been reported include that a person with mild Alzheimer’s disease would not remember most recent events (73.8%), would be discriminated against by employers (55.3%), and would be excluded from medical decision-making (55.3%). Other attributes that were found to be less common included expecting a person with mild Alzheimer’s disease to be unkempt (11.7%), have neglected self-care (6.8%), and have a bad odor (5.8%).

Understanding the varied aspects of AD stigma that do or do not cluster together would aid in developing effective interventions to limit AD stigma. For example, people who make stronger attributions about the severity of a person’s symptoms may also have stronger expectations that the person would be socially excluded by others as more severe symptoms can worry or even repulse people in an effort to avoid disease. Moreover, this group of people that makes stronger attributions about the severity of a person’s symptoms and expects the person to be socially excluded may also be less likely to be willing to offer support to that person.

Latent Class Analysis (LCA) is an empirical approach that can classify data to discover subgroups.⁷ Prior research has shown the value in using LCA to understand relationships between AD dementia and types of symptoms.⁸ The results from LCA have been used to predict who will develop AD.⁹ In addition, prior research outside AD has used this approach to investigate stigma. For example, a study of the Cape Town area used LCA to discover four profiles associated with stigma of HIV among youths,¹⁰ with results showing the majority of stigmatizing reactions related to a lack of knowledge about HIV. In turn, these results were used to inform a youth education campaign.

Public messaging campaigns, such as the national campaign proposed by the Alzheimer’s Association,¹¹ may help change the public’s views and, thereby, reduce AD stigma.^12,13 To be effective, these campaigns would need to deliver focused messages, ideally aimed at the most prevalent or consequential attributions made by the public toward individuals with AD. For example, hyperbolic judgments about a person’s memory problems have been shown to be one of the most common judgments of persons with AD.⁶ Moreover, studies have consistently shown that the public associates AD with institutional discrimination,^6,14 for example, expecting a person with AD to face discrimination by employers and doctors. Knowing whether these attributes represent independent features of AD stigma or whether they tend to co-occur in population subgroups would be useful information for guiding the development of focused on message campaigns to address AD stigma.

The purpose of this study was to understand what, if any, patterns exist in how adults react to persons with AD. We hypothesized that stronger attributions about the severity of symptoms would coincide with lower support and greater social distance. We also aimed to examine what demographic characteristics would predict membership to this profile. Understanding what reactions to AD co-occur and which do not may help inform development of public health messaging campaigns and interventions to reduce stigma of AD. This knowledge may also help anticipate how advances in diagnosis and treatment of AD dementia may shift public stigma of the disease.

Methods

Study design

The present study is a cross-sectional analysis of secondary data collected in a randomly invited sample of adults from the general U.S. public.¹²

Data source

The original study data were obtained from an experiment that examined whether the cause and prognosis of mild stage dementia contributed to AD stigma in adults in the general population. The study asked respondents to read a vignette and then complete a survey. Respondents were recruited September 5th through 13th 2013 by an online panel provider.

The survey was distributed to a random sample of adults in the U.S. who were able to provide informed consent and read English. The survey completion rate was 58%. Respondents were asked to provide standard demographic information. The collection of race and ethnicity information was informed by the Census Alternative Questionnaire Experiment.¹⁵ Respondents were asked to self-identify by race or ethnicity or by multiple races.

The original study used a 3 × 3 factorial design whereby consenting adults (N = 1025) were assigned to one of nine conditions using unrestricted simple randomization.¹⁶ In the present study, we analyzed data from 317 of respondents randomized to 3 of the 9 conditions in which they were told that AD was the cause of the mild stage dementia. The three conditions differed based on whether they were told the person’s condition would (1) worsen, (2) improve, or (3) remain unchanged. A fuller description of the design and randomization is available elsewhere.¹²

Vignette design

The original study used vignettes to examine how diagnostic labels and prognosis contributed to attitudes, emotions, and expectations expressed by the public. The study was described to participants as being about “health beliefs” and did not mention AD during recruitment or consent.

The vignette described a man with cognitive and functional impairments consistent with mild stage AD dementia. The symptoms described reflected observable impairments in six domains of the Clinical Dementia Rating scale¹⁷: memory, orientation, judgment and problem solving, community affairs, home and hobbies, and personal care.

To personalize the vignette, the character was given a name, Mr. Andrews, and referred to as “he.” Pilot versions of the survey included male and female versions of the vignette, but restrictions in sample size required reducing the number of vignettes. Interest in being able to compare findings from the original study with a particular line of research favored retaining the male version of the vignette. No other demographic characteristics of the vignette character were given.

After reading the vignette, respondents were given a comprehension test to confirm that they accurately understood the salient details. They had two opportunities to answer correctly. Those who failed on the second attempt were excluded (n = 30).

Study measures

The modified Family Stigma in Alzheimer’s Disease Scale (FS-ADS) was adapted from the original scale¹⁸ and has 41 items that load onto 7 empirically-derived domains.¹² Items asked the extent to which the participant believed that the person described in the vignette: (a) should worry about encountering discrimination by insurance companies or employers and being excluded from voting or medical decision making (Structural Discrimination); (b) would be expected to have certain symptoms like speaking repetitively or suffering incontinence (Negative Severity Attributions); (c) should be expected to have poor hygiene, neglected self-care, and appear in other ways that provoke negative judgments (Negative Aesthetic Attributions); (d) evoked feelings of disgust or repulsion (Antipathy); (e) would evoke feelings of concern, compassion, or willingness to help from others (Support); (f) would evoke feelings of sympathy, sadness, or pity from others (Pity); and (g) would be ignored or have social relationships limited by others (Social Distance). We framed items on domains that pertained to negative or unpleasant attributes to be about the actions of “others” in order to minimize social desirability bias.^19,20 Responses were recorded on a five-point Likert scale arranged on the screen horizontally from left to right, and analyzed by domain using established methods,¹² with higher scores indicating stronger endorsement.

A shortened Alzheimer’s Disease Knowledge Scale (ADKS)²¹ was used to evaluate general knowledge of Alzheimer’s disease. The abbreviated instrument omitted eight items on the original assessment because they could have been answered using information in the vignette.¹³ Respondents were also asked to rate the degree that they felt the person’s condition (i.e. Alzheimer’s disease) was a mental illness from “not at all” (1) to “a very great extent” (5). A fuller description of the study’s methods is available elsewhere.¹²

Statistical analysis

Descriptive statistics were used to summarize the sample characteristics. We performed a latent class analysis (LCA) using Mplus, Version 4.1.²² Outcomes were entered as categorical variables into one of two models; one comprised of FS-ADS domains that have been demonstrated to independently associate with respondent age, and a second demonstrated to be associated with respondent gender. The first model included FS-ADS domains of Structural Discrimination, Negative Severity Attributions, Supportiveness, and Social Distance. The second model included FS-ADS domains of Negative Aesthetic Attributions, Supportiveness, and Pity.

Fitting two models, rather than one, gave us the potential to identify a larger number of profiles than would be possible with all the variables.²³ We used age and gender as criteria for variable selection given their theoretical relevance to AD stigma,²⁴ their empirical relevance to AD diagnosis risk,²⁵ and AD caregiving responsibilities.²⁶

Model solutions were evaluated on the basis of the bootstrap likelihood ratio test (BLRT) and entropy.²⁷ On the basis of a recent simulation study, the BIC performed better than other information criteria and likelihood ratio tests in identifying the appropriate number of latent classes.²⁷ Instead of assuming the difference distribution follows a known distribution, the BLRT empirically estimates the difference distribution and provides a p value to compare the increase in model fit between models with varied numbers of classes. Entropy is an index for assessing the precision of assigning latent class membership whereby higher probability values indicate greater classification precision. To avoid model convergence on local, rather than global, solutions, multiple start values were estimated for model parameters.²⁸ The analysis had over 95% power to find the true k class model.

To build a multivariate model to predict class membership, we used forward step-wise selection in multivariable ordered logistic regression to construct statistical models that adjusted for interrelationships among respondent characteristics (Alpha-to-Keep ≤ 0.20). Candidate covariates were demographic characteristics, general knowledge about AD, and strength of belief that this disease was a mental illness. We then entered the retained covariates together into a generalized linear model (GLM) with log link to test which if any characteristics were independently associated with each latent class. We report the probability of class membership.

Caregiver status was excluded from analysis as small group size prohibited comparisons (n ≤ 19) and inclusion as a covariate did not substantively alter the main results.²⁹ Respondents’ race was included as a binary comparison of White respondents compared to all other respondents due to small group sizes. All independent variables were screened for multicollinearity (correlation coefficient r > 0.7). Analyses adjusted for multiple comparisons (p > 5.0). All independent variables were screened for interactions with study prognostic category. All statistical tests were two-sided. p values ≤0.05 were considered statistically significant. All non-LCA statistical analyses were performed using Stata 14 (College Station, TX).

Results

Respondent characteristics

In a sample of 317 adults from the public, respondents’ median age was 49 years (IQR 29), about half (49%) were female, most (80%) self-identified as White (non-Latino), and about a third (34.7%) had a college degree or beyond (Table 1). Most (78.5%) resided in urban as opposed to rural areas. About half believed strongly AD was a mental illness.

Table 1.

Characteristics of random sample of general public (N = 317).

Respondent characteristic	Sample (N = 317)
Age, median (IQR)	49 (29)
65+ years old, % (n)	19.1 (61)
Females, % (n)	49.0 (156)
Race/ethnicity, % (n)
White, non-Latino	80.4 (255)
African American, non-Latino	7.3 (23)
Other^a	12.3 (39)
Education, % (n)
High school/GED or less	23.7 (75)
Some college or 2-year degree	41.6 (132)
4-year college degree or beyond	34.7 (100)
Caregiver (past or present), % (n)^b	6.0 (19)
Urban/metro setting,^c % (n)	78.5 (249)
Mental illness rating,^d median (IRQ)	3 (3)
Alzheimer’s Disease Knowledge Scale (ADKS),^e median (IQR)	15 (5)

Column percentages may not total 100 due to rounding.

Category includes those who identified as Asian, Native American, multiple races, Hispanic or Latino only, other or did not respond (n = 4).

Reported past or current primary caregiver of a person with Alzheimer’s disease.

Resides in urban rather than rural area based on Rural Urban Commuting Area (RUCA) classifications. Urban areas included RUCA classes 1–3 and rural included classes 4–10.

Respondents were also asked to rate the degree the condition described in the vignette was a mental illness from “not at all” (1) to “a very great extent” (5).

Abbreviated version. Maximum possible score = 22.

Profiles from structural discrimination, negative severity attributions, supportiveness, and social distance

Three similarly prevalent profiles emerged from analysis of Discrimination, Negative Severity Attributions, Supportiveness, and Social Distance (all p > 0.05; Table 2). Profile 1 showed a pattern whereby over half of those fitting this profile strongly endorsed expectations that a person with AD would experience social distance, structural discrimination, and negative symptom severity (all p < 0.05; Figure 1). Profile 2 showed about half of those matching this profile strongly endorsed expectations that others would express support toward a person with AD (p < 0.05). Profile 3 showed relatively low prevalence of endorsement for each domain in the analysis.

Table 2.

Latent class analysis of Alzheimer’s disease stigma adjusted for study prognosis and respondent characteristics (N = 317).

FS-ADS model and covariates	Profile 1, %	Profile 2, %	Profile 3, %	OR [1 v 3]	OR [2 v 3]	OR [1 v 2]
Model of FS-ADS domains of structural discrimination, negative severity attributions, supportiveness, and social distance
Sample	33.1	37.5	29.3	1.28	1.13	1.13
Prognosis^a
Worsen	31.1	27.7	33.3	1.13	0.77	0.68
Static	32.4	31.1	44.1	0.57*	0.61	0.94
Improve	31.4	41.2	22.6	1.57	2.40**	1.52
Domains^b
Symptom severity	54.3	11.8	36.6	2.06**	0.23***	0.11***
Supportiveness	20.9	50.4	32.3	0.56*	2.14**	3.84***
Social distance	76.2	0	10.8	26.56***	0.01***	0.01***
Discrimination	52.4	26.9	29.0	2.69**	0.90	0.33***
Model of FS-ADS domains of FS-ADS domains of negative aesthetic attributions, supportiveness, and pity
Sample	35.9	48.9	15.1	1.43**	3.23***	1.36*
Prognosis ^a
Worsen	22.8	38.1	35.4	0.55*	1.12	2.08**
Static	33.3	34.8	41.7	0.75	0.68	1.07
Improve	43.9	27.1	22.9	1.25	2.69***	0.48**
Domains^b
Aesthetics	14.9	32.3	58.3	0.26***	0.34**	2.72***
Pity	6.1	55.4	10.4	0.14***	10.72***	19.05***
Supportiveness	15.8	60.6	0	0.33***	0***	8.22***

FS-ADS: family stigma Alzheimer’s disease scale.

Respondents were told the vignette character’s condition would worsen over time, remain the same over time, or improve over time.

Percentages reflect strongest endorsement of stigma, ratings in fourth quartile.

<0.05; **<0.01; ***<0.001.

Figure 1.

Latent classes of Alzheimer’s disease stigma.

Profiles from negative Aesthetic attributions, supportiveness, and pity

Of the three profiles that emerged from analysis of Negative Aesthetic Attributions, Supportiveness, and Pity (Table 2), Profile 2 was most common (48.9%; Figure 2) with a majority respondents endorsing expectations that others would support a person with AD and that this person would be pitied by others. In contrast, Profile 3, which was the least common (15.1%), showed a pattern whereby over half of those fitting this profile strongly endorsed expectations a person with AD would have a poor physical appearance (Negative Aesthetic Attributions; p < 0.05). Profile 1 described about 36% of respondents and did not have majority of respondents endorsing any of the specific domains.

Figure 2.

Latent classes of Alzheimer’s disease stigma.

Personal predictors of profile membership

In comparisons of personal characteristics between profiles of AD stigma, general knowledge of AD discerned between profiles defined by Structural Discrimination, Negative Severity Attributions, Supportiveness, and Social Distance (p < 0.05; Table 3). No other statistically discernable differences were observed on assessed characteristics (all p > 0.05).

Table 3.

Differences in personal characteristics between latent classes of Alzheimer’s disease stigma (N = 317).

Respondent characteristic	Profile 1 (OR)	Profile 2 (OR)	Profile 3 (OR)	p value
Model of FS-ADS domains of Structural Discrimination, negative severity attributions, supportiveness, and social distance
Age, years	49.4	46.3	48.5	0.34
Females	50.5	51.3	45.2	0.64
White	88.6	73.1	80.6	0.13
High school/GED or less	21.9	29.4	18.3	0.15
Urban/metro setting^a	80.0	79.0	76.3	0.82
Mental illness rating^b	2.8	2.8	2.8	0.89
Alzheimer’s Disease Knowledge Scale (ADKS)^c	14.2	15.1	16.1	0.004
Model of FS-ADS domains of FS-ADS domains of negative aesthetic attributions, supportiveness, and pity
Age, years	50.3	45.9	49.1	0.09
Females	47.4	54.8	35.4	0.05
White	81.6	80.0	79.2	0.92
High school/GED or less	28.9	18.1	29.2	0.07
Urban/metro setting^a	79.8	77.4	79.2	0.90
Mental illness rating^b	2.9	2.8	2.8	0.86
Alzheimer’s Disease Knowledge Scale (ADKS)^c	14.8	15.4	14.7	0.44

FS-ADS: family stigma Alzheimer’s disease scale; OR: odds ratio.

Results from ordered logistic regression analyses.

Resides in urban rather than rural area based on Rural Urban Commuting Area (RUCA) classifications. Urban areas included RUCA classes 1–3 and rural included classes 4–10.

Respondents were also asked to rate the degree the condition described in the vignette was a mental illness from “not at all” (1) to “a very great extent” (5).

Abbreviated version. Maximum possible score = 22.

In comparisons of personal characteristics between profiles of AD stigma, no discernable differences were observed at the p = 0.05 level on profiles defined by Negative Aesthetic Attributions, Supportiveness, and Pity. However, some statistical trends were observed for age, gender and education. Older individuals were more likely to belong to Profile 1 as compared to Profile 2 (p = 0.09) and individuals who identified as female were more likely to belong to Profile 2 rather than Profile 1 (p = 0.05). Individuals with a high school education or less were more likely to belong to Profile 3 than Profile 2 (p = 0.07).

Within class correlates of latent classes of Alzheimer’s Disease Stigma

In analysis of Profile 1 in which respondents were likely to expect Social Distance, make Negative Severity Attributions, worry about Discrimination, and expected others to show low Supportiveness, lower AD knowledge predicted Profile membership (p < 0.05, Table 4). In analysis of Profile 2 in which respondents were unlikely to expect Social Distance, make Negative Severity Attributions, or worry about Discrimination, but expected others to express Supportiveness, lower education and identifying as White predicted membership (both p < 0.05). In analysis of Profile 3 in which respondents were unlikely to make Negative Severity Attributions or expect Social Distance as well as unlikely to express Supportiveness and worry about Discrimination, higher knowledge about AD predicted membership (p < 0.05).

Table 4.

Within class correlates of latent classes of Alzheimer’s disease stigma (N = 317).

FS-ADS model and covariates	Profile 1 (OR)	Profile 2 (OR)	Profile 3 (OR)
Model of FS-ADS domains of structural discrimination, negative severity attributions, supportiveness, and social distance
Age (years)	1.01	0.99	1.00
White	0.66	1.41**	1.04
High school versus higher	—	0.75*	1.39
Alzheimer’s Disease knowledge scale (ADKS)^a	0.96*	—	1.04**
Model of FS-ADS domains of FS-ADS domains of negative Aesthetic attributions, supportiveness, and pity
Age, years	1.01*	0.99**	—
Female	—	1.16	0.55*
High school versus higher	0.81	1.40*	—

FS-ADS: family stigma Alzheimer’s disease scale; OR: odds ratio.

Results from GLM with log link.

Abbreviated version. Maximum possible score = 22.

<0.05; **<0.01

In the second set of analyses, Profile 1 in which respondents endorsed no remarkable pattern among domains of Negative Aesthetic Attributions, Supportiveness, and Pity, older age predicted profile membership (p < 0.05, Table 4). In analysis of Profile 2 in which respondents expressed high Supportiveness and endorsed expectations of Pity, younger age and higher education predicted profile membership (both p < 0.05). In analysis of Profile 3 in which respondents were likely to endorse Negative Aesthetic Attributions, self-describing as male predicted profile membership (p < 0.05).

Discussion

We conducted analyses of a secondary data source that consisted of a sample of 317 adults from the U.S. general population. The purpose of the study was to explore what, if any, patterns exist in profiles of AD stigma in the adult public. AD stigma is multifaceted, including prejudicial thoughts, beliefs, and behaviors that are informed by stereotypes associated with the disease. Understanding whether features of AD stigma tend to cluster together in profile patterns in subgroups of the population could be informative to developing public health messaging campaigns and interventions to reduce AD stigma.

Based on results from prior studies,^30,31 we hypothesized that stronger judgments about the severity of symptoms would coincide with lower expectations of others’ willingness to support a person with AD and greater expectations that a person with AD would be socially distanced by others. We found some support for this hypothesis; a third of adults in our sample placed persons with AD as unsupported, discriminated against, and socially distanced.

The triad of beliefs about a lack of support, worries about discrimination, and expectations of social distance may depict a topic cluster that can be leveraged in public messaging campaigns intended to reduce AD stigma. Together, this cluster of beliefs and expectations describe what might be a stereotypic understanding of a person with AD. Public messaging campaigns that can reach the public to challenge this stereotypic view with representations of persons with AD who are well supported both by their families and society may aid in lowering AD stigma.

The triad of beliefs about supportiveness, discrimination, and social distance appeared to be the singular prominent profile of AD stigma in the adult public. This finding is consistent with the notion that there is a dominant stereotype of persons with AD, which is a widely held but fixed and oversimplified idea of a person with AD.³² However, the finding contrasts with the general expectation that higher educational attainment is associated with lower stigma.^33,34 Rather, in our study, we found higher education and this particular characterization of higher stigma cluster together, suggesting this may be the profile for AD stigma in social groups with higher educational attainment. This conclusion is supported in part by the observation that we have not found education to correlate with AD stigma in any of our other analyses in the current study sample or those conducted in other samples of the adult public.

Another profile that emerged from analysis of the FS-ADS domains of Negative Aesthetic Attributions, Support, and Pity showed a pattern whereby over half of those fitting this profile strongly endorsed expectations that a person with AD has a poor physical appearance. This is a highly notable finding as high Negative Aesthetic Attributions is a fairly distinct feature of AD stigma.⁶ Having a high school education or less predicted membership on this profile. These findings raise a question of whether they may reflect how social and structural determinants of health, in this case educational attainment and its healthcare and economic correlates, may affect AD stigma. Further studies to explore this avenue of inquiry may be useful for developing opportunities to reduce AD stigma in low-resourced communities.

While our findings point to a dominant profile of AD stigma, which may be helpful to guiding public messaging campaigns, we otherwise found a lack of other well-defined profiles, which is highly notable as it suggests that aspects of stigma may operate independently or, alternatively, have universal factors that lead them to heighten or lower together. For example, we found level of Support to be an instrumental factor that distinguished between profiles in analysis of FS-ADS domains associated with participant age. Thus, anti-stigma campaigns that focus on issues of support could inadvertently shift people between profiles without necessarily mitigating stigma.

Limitations

Our sample of 317 is small to adequately reflect the rich diversity of the large U.S. general population. It was also collected in 2013, and, while we are unaware of large shifts in AD stigma toward diagnosis since this time, the age of the study data is a limitation. Moreover, we did not calculate an estimate of sample size a priori, given this was an analysis of existing data. Further research with large, newly collected, random samples representative of the general population is needed to derive more precise estimates and to understand how AD stigma may differ across divergent racial, ethnic, and socioeconomic groups.³⁵ In addition, our vignette described a specific patient with symptoms of mild stage dementia. Results to date from similar studies—particularly those that have experimentally varied the gender of the vignette character—have not found appreciable differences in reactions among the public based on gender of the vignette character.³⁶ However, they have found evidence that suggests a person’s judgments about social roles and interpersonal relationships can affect how that person judges someone with Alzheimer’s disease.^37,38 This is an area that warrants further investigation, and vignette studies may be a particularly useful method for this work.^39,40 The findings from this study are a first step. Future studies are needed with larger, more sociocultural diverse samples in order to replicate the findings in the current study.

Conclusion

The findings are informative to understanding opportunities to address AD stigma. Our findings point to a singular dominant profile of AD stigma, which may be helpful to guiding public messaging campaigns. We also found less prominent signals that may be relevant to considering opportunities to reduce AD stigma. Other profiles we observed reinforce the interpretation that universal factors may be either heightening or lowering AD stigma, such as higher social support and access to social resources.

Footnotes

ORCID iDs

Shana D. Stites

Carolyn Kuz

Ethical considerations

The Institutional Review Board of the University of Pennsylvania approved all procedures involving human subjects for the “Health Beliefs Study” (No. 828348).

Consent to participate

All participants provided written informed consent prior to participating.

Author contributions

SDS wrote the initial draft of the article. SDS, CK, KH, JDR, and RJ contributed to interpreting the results and writing the article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Alzheimer’s Association (AARF-17-528934), the National Institute on Aging (K23AG065442), and cooperative agreements from the Centers for Disease Control and Prevention (CDC) Prevention Research Centers Program (U48 DP 005006, 005002, 005053, 005000, and 005013). The funding sources had no involvement in the study design, collection, analysis or interpretation of data, writing of the report, or the decision to submit the article for publication.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The de-identified data, analytic code, and materials on which the study conclusions are based are available for purposes of replication. Written request may be made to the corresponding author. The data have not been made publicly available as the research team has not completed planned analyses for publications. Reasoning for the sample size, any data exclusions, all manipulations, and all measures are included in this publication. The study design, hypotheses, and analytic plan were not preregistered.

References

Corner

Bond

Being at risk of dementia: fears and anxieties of older adults. J Aging Stud 2004; 18: 143–155.

Werner

Giveon

SM.

Discriminatory behavior of family physicians toward a person with Alzheimer’s disease. Int Psychogeriatr 2008; 20: 824–839.

Alzheimer’s Association National Plan Milestone Workgroup, Fargo

Aisen

, et al. 2014 Report on the milestones for the US National Plan to address Alzheimer’s disease. Alzheimers Dement J Alzheimers Assoc 2014; 10: S430–S452. 2014.

Link

Cullen

Mirotznik

, et al. The consequences of stigma for persons with mental illness: Evidence from the social sciences. In: Fink

Tasman

(eds). Stigma and mental illness. American Psychiatric Association, 1992, pp.87–96.

U.S. Department of Health and Human Services. National plan to address Alzheimer’s disease: 2014 update. U.S. Department of Health and Human Services, https://aspe.hhs.gov/sites/default/files/migrated_legacy_files//181116/NatlPlan2014%20with%20Note.pdf(2014).

Stites

Rubright

Karlawish

What features of stigma do the public most commonly attribute to Alzheimer’s disease dementia? Results of a survey of the U.S. General public. Alzheimer Dement 2018; 14: 925–932.

Formann

, et al. Latent class analysis. In: Balakrishnan

Colton

Everitt

(eds) Wiley StatsRef: statistics reference online. John Wiley & Sons, Ltd, 2014, pp.1–7. DOI: 10.1002/9781118445112.stat00070.

Roberto

Portella

Marquié

, et al. Neuropsychiatric profiles and conversion to dementia in mild cognitive impairment, a latent class analysis. Sci Rep 2021; 11: 6448.

Alashwal

Diallo

TMO

Tindle

, et al. Latent class and transition analysis of Alzheimer’s disease data. Front Comput Sci 2020; 2: 551481. DOI: 10.3389/fcomp.2020.551481

10.

Brinkley-Rubinstein

Craven

A latent class analysis of stigmatizing attitudes and knowledge of HIV risk among youth in South Africa. PLoS One 2014; 9: e89915.

11.

Robinson

Tansil

Elder

, et al. Mass media health communication campaigns combined with health-related product distribution: a community guide systematic review. Am J Prev Med 2014; 47: 360–371.

12.

Johnson

Harkins

Cary

, et al. The relative contributions of disease label and disease prognosis to Alzheimer’s stigma: a vignette-based experiment. Soc Sci Med 2015; 143: 117–127.

13.

Stites

Johnson

Harkins

, et al. Identifiable characteristics and potentially malleable beliefs predict stigmatizing attributions toward persons with Alzheimer’s disease dementia: Results of a survey of the U.S. general public. Health Commun 2018; 33: 264–273.

14.

Nguyen

Understanding public-stigma and self-stigma in the context of dementia: A systematic review of the global literature. Sage Publ Co, 2020. Vol. 19, pp.148–181.

15.

Rastogi

Jones

. Overview of the 2010 Census Alternative Questionnaire Experiment: Race and Hispanic Origin Research. In: Hoque

Swanson

(eds) Opportunities and challenges for applied demography in the 21st Century. Springer Netherlands, 2012, pp.11–26.

16.

Schulz

Grimes

DA.

Unequal group sizes in randomised trials: guarding against guessing. Lancet Lond Engl 2002; 359: 966–970.

17.

Hughes

Berg

Danziger

, et al. A new clinical scale for the staging of dementia. Br J Psychiatry J Ment Sci 1982; 140: 566–572.

18.

Werner

Goldstein

Heinik

Development and validity of the family stigma in Alzheimer’s disease scale (FS-ADS). Alzheimer Dis Assoc Disord 2011; 25: 42–48.

19.

Bazinger

Kühberger

Is social projection based on simulation or theory? Why new methods are needed for differentiating. New Ideas Psychol 2012; 30: 328–335.

20.

Fisher

RJ.

Social desirability bias and the validity of indirect questioning. J Consum Res 1993; 20: 303.

21.

Carpenter

Balsis

Otilingam

, et al. The Alzheimer’s disease knowledge scale: development and psychometric properties. Gerontologist 2009; 49: 236–247.

22.

Muthén Muthén . Mplus at a Glance. Mplus, https://www.statmodel.com/glance.shtml (2024, accessed 1 July 2024).

23.

Dean

Raftery

AE.

Latent class analysis variable selection. Ann Inst Stat Math 2010; 62: 11–35.

24.

Stites

Milne

Karlawish

Advances in Alzheimer’s imaging are changing the experience of Alzheimer’s disease. Alzheimers Dement Diagn Assess Dis Monit 2018; 10: 285–300.

25.

Alzheimer’s Association. 2023 Alzheimer’s disease facts and figures. Alzheimers Dement 2023; 19: 1598–1695.

26.

Dorstyn

Ward

, et al. Alzheimers’ disease and caregiving: a meta-analytic review comparing the mental health of primary carers to controls. Aging Ment Health 2018; 22: 1395–1405.

27.

Nylund

Asparouhov

Muthén

BO.

Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model Multidiscip J 2007; 14: 535–569.

28.

McLachlan

Peel

Sons

, et al. Finite mixture models. Wiley, 2000. https://trove.nla.gov.au/version/19243046 (2000, accessed 19 July 2018).

29.

Hosmer

Lemeshow

Sturdivant

Applied logistic regression. John Wiley & Sons, 2013.

30.

Stites

Gill

Largent

, et al. The relative contributions of biomarkers, disease modifying treatment, and dementia severity to Alzheimer’s stigma: a vignette-based experiment. Soc Sci Med 2022; 292: 114620.

31.

Stites

Lee

Largent

, et al. Double-edged sword: a positive brain scan result heightens confidence in an Alzheimer’s diagnosis but also leads to higher stigma among older adults in a vignette-based experiment. J Gerontol B Psychol Sci Soc Sci 2024; 79: gbae109. DOI: 10.1093/geronb/gbae109

32.

Low

L-F

Purwaningrum

Negative stereotypes, fear and social distance: a systematic review of depictions of dementia in popular culture in the context of stigma. BMC Geriatr 2020; 20: 477.

33.

Kim

Richardson

Werner

, et al. Dementia stigma reduction (DESeRvE) through education and virtual contact in the general public: a multi-arm factorial randomised controlled trial. Dementia 2021; 20: 2152–2169.

34.

Błaszkiewicz

Szcześniak

Ciułkowicz

, et al. Biomedical knowledge of dementia is not enough to counteract its stigma – quantitative research among future medical and social care staff in Poland. Aging Ment Health 2024; 28: 1317–1325.

35.

Woo

Mehta

Examining the differences in the stigma of dementia and diabetes among Chinese Americans. Geriatr Gerontol Int 2017; 17: 760–764.

36.

Stites

Midgett

Largent

, et al. Deconstructing Alzheimer’s Stigma: gender identities correspond with patterns of internalization and judgment. Am J Geriatr Psychiatry Open Sci Educ Pract 2025; 5: 63–75.

37.

Low

Anstey

KJ.

Dementia literacy: recognition and beliefs on dementia of the Australian public. Alzheimers Dement J Alzheimers Assoc 2009; 5: 43–49.

38.

Wadley

Haley

WE.

Diagnostic attributions versus labeling: impact of Alzheimer’s disease and major depression diagnoses on emotions, beliefs, and helping intentions of family members. J Gerontol B Psychol Sci Soc Sci 2001; 56: 244–252.

39.

Malik

Norman

Puente

AE.

A vignette-based study examining attitudes toward mental and brain illness in the United States and Pakistan. Stigma Health 2025; 10(3): 456–466.

40.

Baumgartner

Corrigan

Wan

, et al. The difference in stigmatizing attributions toward older adults with or without Alzheimer’s disease. Stigma Health 2023; 8: 467–475.

Public profiles in Alzheimer’s disease stigma: Unsupported,discriminated,and alone

Abstract

Introduction:

Methods:

Results:

Conclusions:

Keywords

Introduction

Methods

Study design

Data source

Vignette design

Study measures

Statistical analysis

Results

Respondent characteristics

Profiles from structural discrimination, negative severity attributions, supportiveness, and social distance

Profiles from negative Aesthetic attributions, supportiveness, and pity

Personal predictors of profile membership

Within class correlates of latent classes of Alzheimer’s Disease Stigma

Discussion

Limitations

Conclusion

Footnotes

ORCID iDs

Ethical considerations

Consent to participate

Author contributions

Funding

Declaration of conflicting interests

Data availability statement

References