Psychometric Properties of the Rowland University Dementia Assessment Scale – Peruvian Version Among Indigenous Amazonian Communities

Psychometric Properties of the Rowland University Dementia Assessment Scale – Peruvian Version Among Indigenous Amazonian Communities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Psychometric Properties of the Rowland University Dementia Assessment Scale – Peruvian Version Among Indigenous Amazonian Communities Alicia Boluarte Carbajal, Arantxa Sanchez Boluarte, Marleny Nolasco, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8852544/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Cognitive screening tools are rarely validated for Indigenous populations of Latin America, a barrier to early dementia detection. We culturally adapted and evaluated the psychometric performance of the Peruvian version of the Rowland Universal Dementia Assessment Scale (RUDAS-PE) for Shawi Indigenous Amazonian communities. We enrolled 472 adults aged ≥ 50 years who completed the RUDAS-PE. Cognitive interviews and community feedback revealed cultural mismatches, particularly in visuoconstruction tasks, requiring linguistic and contextual item modifications. The original 6-item structure showed poor fit, while a 5-item version demonstrated strong unidimensionality and improved model fit (confirmatory factor analysis, comparative fit index = 0.97; Root Mean Square Error of Approximation = 0.06). Item response analyses confirmed high measurement precision at low-moderate educational levels and identified severe floor effects in visuoconstruction. Culturally-valid adaptations of brief cognitive tools are essential to avoid diagnostic bias in Indigenous populations. Our findings provide a scalable framework for adaptation of cognitive screening for Indigenous communities. Health sciences/Health care Health sciences/Medical research Biological sciences/Psychology Social science/Psychology Cognitive impairment dementia Shawi RUDAS-PE psychometry Indigenous people Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Dementia is one of the leading causes of disability in older adults and the seventh leading cause of death worldwide [ 1 ]. Currently, an estimated 55 million people worldwide live with dementia, making it an international public health priority [ 2 ]. In Latin America, the prevalence of dementia is 8.4%, which is comparable to or higher than estimates of 7–8% in high-income countries [ 3 ], while in Indigenous communities it appears to be relatively low, with estimates of 0.9–4.9%, particularly in Amazonian populations in Brazil [ 4 ] and Bolivia [ 5 ]. However, the scarcity of epidemiological studies and the lack of instruments to detect dementia make it particularly difficult to study. In recent years, the study of cognitive impairment in Indigenous populations has become particularly relevant due to the need to understand how sociocultural, environmental, and biological factors affect the brain aging process [ 6 ]. This perspective has driven the development and adaptation of psychological assessment tools that enable the early identification of cognitive decline and, therefore, improve the timely detection of dementia [ 7 , 8 ]. Native communities generally have lifestyles, linguistic patterns, and traditional knowledge systems that can differ significantly from the urban contexts in which most cognitive assessment instruments have been developed. Therefore, it is crucial to design and adapt tools that account for unique aspects of these populations, the context in which they live, and their daily experiences. Historically, Indigenous peoples have been systematically marginalized from their rights and decisions that directly affect them, leading to the loss of their lands, their culture, limited access to resources and services, and food insecurity [ 9 , 10 ]. The Shawi people live on a subsistence economy consisting of agriculture, fishing, and hunting [ 11 ]. They are also the Peruvian ethnic group with the lowest percentage of state recognition of their lands and institutional affiliation [ 12 ]. This reality shows that Indigenous peoples face poorer economic, social, and health conditions as a result of structural inequalities [ 10 , 13 ]. Thus, the cognitive function assessment in Indigenous populations requires culturally sensitive approaches, without biases accounting for language, culture, literacy, and educational level [ 14 ]. Specifically, the Rowland Universal Dementia Assessment Scale (RUDAS) has been increasingly studied as a culturally valid alternative to traditional cognitive screening tools [ 15 ] used in various multicultural contexts as a screening tool [ 16 – 19 ]. It has been shown to have comparable or superior performance to the Mini Mental State Exam (MMSE) in the detection of mild cognitive impairment and dementia, particularly in people with low educational attainment or illiteracy [ 20 , 21 ]. The RUDAS has been shown to have good concurrent validity [ 19 – 23 ], but few studies report evidence of construct validity. Those that do report construct validity have been conducted with small samples, limiting its analysis [ 25 , 26 ]. It is crucial that cognitive screening tests reflect robust psychometric properties of validity and reliability, particularly when adapted to culturally and linguistically diverse populations. Our study represents the first attempt to analyze the psychometric performance of RUDAS in older adults from Shawi Amazonian communities. We sought to examine the validity and reliability of the RUDAS in this population. Following cross-cultural adaptation, our study used modern psychometric frameworks examining structural models through Classical Test Theory and Item Response Theory to explore the latent structure of cognitive domains and refine measurement accuracy of the Peruvian version of RUDAS [ 27 ] METHODS Design We conducted a psychometric study that aimed to ensure comprehensive and rigorous assessment of instrument reliability and validity [ 28 ]. We conducted analyses using Classical Test Theory (CTT) and Item Response Theory (IRT). Qualitative and quantitative methods were used aligning with international standards for test adaptation [ 29 ]. Participants Participants were recruited between October 2024 and February 2025 in rural communities recognized as Shawi by the Ministry of Culture, in the district of Balsapuerto, in the Loreto region of Peru. The Shawi Indigenous people live mainly in the districts of Cahuapanas and Balsapuerto (Loreto region) in the Amazon, and are considered one of the most vulnerable Amazonian groups, with 26,841 inhabitants [ 9 , 10 ]. It is estimated that 8% of Shawi people in Loreto, Peru, are over 50 years old, based on 2017 National Census data [ 30 ]. This results in approximately 2,126 Shawi people over the age of 50. This study included adults over 50 years of age who met at least one of the following criteria: 1) native language of Shawi, 2) residence in the district of Balsapuerto, either in the town of Balsapuerto or residing in Shawi communities and speaking Shawi and/or Spanish. Participants were recruited using door-to-door visits to the home and community meetings, strategies that were necessary given the scattered geography and adverse weather conditions in the area. Recruitment was also conducted during the monthly government programs, such as Pensión 65 and Juntos, when Peruvian citizens (including Shawi) over age 65 receive a monthly incentive from the government. Procedures We first adapted the RUDAS-PE was adapted using qualitative methods. First, opportunities for dialogue were created with residents and community leaders of the Shawi community, allowing for a deep immersion into Shawi culture. These informal group meetings were part of an apparent validity process [ 31 ], in which interviews were conducted with two community leaders and two Shawi residents, as well as local government leaders. We administered the RUDAS-PE during each interview and collected any observations and feedback on the RUDAS-PE items. Using this process, we assessed acceptability and understanding of each instrument item. Subsequently, we conducted cognitive interviews to these participants to obtain evidence of validity based on the response process [ 32 ], to ensure the instrument was understandable and relevant to the target population. We then incorporated feedback from these cognitive interviews. Native experts from the Shawi culture (“Apus,” primary school teachers in the community) acted as bilingual (Spanish/Shawi) facilitators to incorporate the feedback we received to adapt the instrument. Finally, we conducted iterative pilot tests to evaluate the suitability of each item translated and back-translated by two native Shawi speakers. The objective of this process was to ensure the semantic, conceptual, and cultural adequacy of the instrument to ensure that the instructions and items were understandable and relevant to the Shawi participants. Following the pilot testing, data collection was conducted. The data collection team was comprised of two native Shawi speakers with higher technical training, a Shawi teacher, a medical student, and three researchers, who participated on an ad hoc basis. Permission to access 36 native communities was obtained through the “Apus,” who are the authorities representing each community. The evaluations were carried out through home visits and meetings and people voluntarily agreed to participate by signing the Informed Consent form. Nearly all of older adults in each selected community were evaluated. Instruments The RUDAS is a brief six-item test that assesses memory, praxis, language, judgment, visuoconstruction, and body orientation [ 15 ]. It was originally developed in a multiethnic Australian population, where diagnostic accuracy did not vary by years of education ( p = 0.20) or preferred language ( p = 0.33). The RUDAS showed a sensitivity of 89% (95% CI: 76%-96%) and a specificity of 98% (95% CI: 88%-97%) with a cutoff point of 22/23 out of 30. The scale has been translated and adapted into multiple languages, although not all versions have undergone formal validation and diagnostic accuracy studies [ 33 ]. In Peru, the RUDAS has been studied in Spanish-speaking urban and rural populations and demonstrated concurrent validity and its ability to discriminate between dementia and mild cognitive impairment [ 21 , 22 , 34 , 35 ]. However, some studies reported alpha coefficients below the expected optimal values [ 21 , 22 , 34 ]. This study used the Peruvian version of the RUDAS (RUDAS-PE), linguistically and culturally adapted for use in Peruvian populations and is considered the most appropriate version for populations with sociocultural diversity and low levels of education [ 34 ] Data Analysis Initially, an exploratory analysis of the data was performed to understand the behavior of each of the six dimensions that make up the RUDAS-PE. The response ratio for each score category was reported in order to identify possible floor effects (accumulation of responses in the lowest categories) or ceiling effects (accumulation in the highest categories), which could limit an item's ability to discriminate between participants [ 36 ]. Given the ordinal nature of the dimension scores, a polychoric correlation matrix was estimated for the classical test theory (CTT) analyses. This method is the most appropriate for estimating the latent association between ordered categorical variables and will serve as a basis for evaluating item interrelationship and common factorial structure viability [ 37 – 40 ]. To evaluate the construct validity of the scale, a Confirmatory Factor Analysis (CFA) model was fitted. Based on the underlying theory of RUDAS-PE, a unidimensional structure was hypothesized where the six dimensions are explained by a single latent factor of “general cognitive ability.” We used the Weighted Least Squares Mean and Variance method to estimate the model, considered the most appropriate estimator for categorical or ordinal data [ 37 , 41 ]. The model fit was evaluated using multiple indices: the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI), where values > 0.90 indicate an acceptable fit and > 0.95 an excellent fit; and the Root Mean Square Error of Approximation (RMSEA), where values < 0.08 are acceptable and < 0.06 are desirable [ 42 , 43 ]. If the initial model did not show an adequate fit, the modification indices were examined to identify sources of misfit and guide a possible re-specification of the model, such as the removal of problematic items. Subsequently, Item Response Theory (IRT) was applied to gain a deeper understanding of the functioning of the items and the scale as a whole. Since most dimensions of the RUDAS-PE are scored by accumulating points for correct answers (partial credit), the Generalized Partial Credit Model (GPCM) was selected. This model is ideal for polytomous items where each response category represents an incremental level of success or ability [ 44 , 45 ]. Model Fit Evaluation and Item in IRT The overall fit of the GPCM model was evaluated using the M2 statistic and its associated indices (CFI, TLI, RMSEA, SRMR) [ 46 ]. At the individual level, the fit of each item to the model was examined using the S-X² statistic. This allowed us to identify whether any items behaved abnormally or inconsistently with the proposed model [ 47 ]. Once the TRI model was adjusted and validated, the characteristic curves were generated and analyzed. The Item Characteristic Curves (ICC) graphically showed the probability of obtaining each score on an item across the entire ability spectrum [ 48 ]. The Item Information Curves (IIC) and the Test Information Curve were fundamental to the analysis. These curves indicate the ranges of the ability in which each item and the test as a whole provide the greatest measurement accuracy. The Typical Error Curve was also generated, which is inversely proportional to the information and visualizes the degree of expected error in the estimation of ability for each level of theta. Finally, the reliability of the test was examined from the perspective of IRT, in which reliability is conceptualized as a function of ability [ 49 ]. The marginal reliability of the test, which depends on the Test Information Curve and the distribution of ability in the sample, was calculated and graphed. This approach allows us to understand how reliability varies across the ability continuum, providing a more accurate and appropriate view of measurement consistency [ 50 ]. Ethical considerations This study was approved by the local community leaders (“Apus”) of each community and the ethics committees (SIDISI 215474) of the Universidad Peruana Cayetano Heredia and the Universidad Cesar Vallejo in Lima, Peru. The study was exempt from IRB review by the University of North Carolina at Chapel Hill IRB in North Carolina, USA. The study was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Written informed consent was provided by each participant according to standard procedures. In the case the person was unable read and/or write, they provided their thumbprint as a proxy for signature. Participants benefited from the provision of their individual test results, as well as relevant counseling and referral to a specialist in their area when clinically indicated. Results Cultural Adaptations Through meetings with representatives of a sample of selected communities, we explored their worldview, values, beliefs, and community practices, which served as input for the next stage of cultural adaptation. Eight cognitive interviews were conducted with people over the age of 50 in various social roles in their community (such as healers, “Apus,” elders, and bilingual teachers), which helped identify how the Shawis conceptualize concepts such as attention, memory, and neurological health. Key aspects were identified, such as the perception of time, traditional forms of knowledge transmission, and the conceptualization of memory for remembering recent events. These findings allowed us to incorporate concepts related to their traditional activities of hunting, fishing, and agriculture into the instrument, ensuring that the items and each domain reflected the cognitive profile of the Shawi people. We adjusted the wording and scoring of the test items using the prior suggestions. Translation and back-translation into the original language ensured semantic equivalence and eliminated cultural biases, incorporating relevant elements of the Shawi community's sociocultural context. During the pilot study, comprehension problems, instruction and item ambiguities were resolved, and test administration time was adjusted. Significant sociocultural and linguistic differences were found. Specifically, the Shawi use a orientation system based on the position of the moon, concrete visuospatial activities, and familiarity with certain tasks based on the local ecological context. Adaptations of the RUDAS-PE prioritized the preservation of fundamental cognitive constructs, while modifying task instructions, visual stimuli, and culturally specific references that were unknown or inaccessible to community members. Descriptive Analysis Table 1 Sociodemographic characteristics of participants (N = 472). Characteristics n (%), median (IQR), mean (SD) Sociodemographics Age (years) 59 (54–67) Female sex 243 (54.7) Years of education 0 (0–2.5) Civil status Single 40 (9) Married / Cohabitating 319 (72) Widowed 84 (19) Salaried job 18 (4.1) Electricity 278 (65.6) Internet 32 (11.5) N people in same household 4 (3–6) Shawi ethnicity 399 (90.1) Monolingual Shawi 289 (61.2) Bilingual Shawi - Spanish 110 (24.9) Daily traditional alcohol intake 413 (93.4) * median, IQR- interquartile range; SD- standard deviation Figure 1 details the proportional distribution of responses for each of the six dimensions of the RUDAS-PE scale. As expected in a cognitive screening scale applied to a community sample, most items demonstrated an asymmetric distribution with a pronounced ceiling effect. Specifically, items 2 (Orientation), 5 (Judgment), 6 (Memory), and 7 (Language) showed that between 71% and 79% of those evaluated obtained the maximum score. This pattern is consistent with a population largely without cognitive impairment. However, item 4 (Visuospatial Construction) exhibited markedly anomalous behavior, showing a severe floor effect: 82% of the sample obtained the minimum score of 0. This low variability and its distribution, which is opposite to that of the rest of the scale, suggest that item 4 may not be functioning consistently with the other dimensions (Fig. 1 ) . To examine the interrelationship between the dimensions of the RUDAS-PE and to evaluate the internal consistency of the scale, a polychoric correlation matrix was calculated, the results of which are shown in Fig. 2 . The analysis revealed moderate and statistically significant positive correlations between most items, with coefficients ranging from 0.19 to 0.48, suggesting that they share a common latent construct. However, in line with the anomalies detected in the descriptive analysis, item 4 (Visuospatial Construction) shows a markedly divergent correlation pattern. This item shows correlations close to zero and insignificant with virtually all other dimensions of the scale. This lack of consistency, probably exacerbated by the severe floor effect previously observed, compromises its construct validity and suggests that its inclusion in a one-dimensional model would negatively affect the overall fit, justifying its potential exclusion in the factorial modeling stage. Confirmatory Factor Analysis To evaluate the structural validity and test the unidimensionality hypothesis of the RUDAS-PE scale, two Confirmatory Factor Analysis (CFA) models were fitted. The first model (M1) included the six original dimensions, while the second model (M2) was re-specified excluding item 4, whose anomalous behavior was identified in the descriptive and correlation analyses. Table 2 summarizes the fit indices for both models, demonstrating a substantial and decisive improvement in the re-specified model. Model M1 showed a poor fit to the data, with a significant Chi-square value (χ²(9) = 46.306, p < .001), a CFI of 0.884, and a TLI of 0.807, both below acceptable thresholds. In addition, the RMSEA was high (0.099), indicating a considerable discrepancy between the model and the observed data. In stark contrast, model M2 showed excellent fit: although the Chi-square remained significant (χ²(5) = 12.887, p = .024), the relative fit indices improved dramatically to a CFI of 0.969 and a TLI of 0.939. The RMSEA was also reduced to 0.061. This remarkable improvement in all fit indices confirms that the exclusion of item 4 resolves the main source of misfit and supports the plausibility of a unidimensional structure for the five-item scale. Table 2 Adjustment indices between the two AFC models from RUDAS-PE Model ꭓ 2 (gl) p (Chi2) CFI TLI RMSEA RMSEA CI SRMR M1 46.306 (9) < .001 0.884 0.807 0.099 [0.072, 0.128] 0.097 M2 12.887 (5) 0.024 0.969 0.939 0.061 [0.020, 0.103] 0.047 TRI Model: Generalized Partial Credit Model Following the same model specification logic used in CFA, the performance of the RUDAS-PE scale was evaluated under the IRT framework. A Generalized Partial Credit Model (GPCM) was fitted to examine the fit of the data to a unidimensional structure. Table 3 presents the fit indices for the two TRI models: the initial model (M1) with six items and the re-specified model (M2) with five items, excluding rudas_4. The results of the IRT analysis strongly corroborate the CFA findings. Although model M1 shows a marginally acceptable fit (CFI = 0.941, TLI = 0.902), it has an RMSEA (0.074) that suggests room for improvement and a significant Chi-square value (p < .001). In contrast, model M2 shows a dramatic improvement and excellent fit to the data, with a non-significant Chi-square (p = .303), CFI and TLI indices close to unity (0.997 and 0.993, respectively), and a very low RMSEA (0.022). This convergence of evidence between the AFC and TRI frameworks reinforces the conclusion that the five-item model is psychometrically superior and more robust. Table 3 Adjustment indices between the two GPCM-TRI models from RUDAS-PE Model ꭓ 2 (gl) p (Chi2) CFI TLI RMSEA RMSEA CI SRMR M1 29.689 0.000 0.941 0.902 0.074 [0.045, 0.104] 0.077 M2 6.037 0.303 0.997 0.993 0.022 [0.000, 0.074] 0.067 Figure 3 shows the Test Information Curve (TIC) and its corresponding Standard Error of Measurement (SEM) curve. The ITC, which is the sum of the information from the individual items, reaches its peak at a skill level of approximately theta = -2.0. This confirms that the five-item RUDAS-PE scale, as a whole, offers the highest measurement accuracy for identifying individuals on the low-moderate cognitive ability spectrum. Conversely, the SEM curve shows that measurement error is minimal in this same range and increases progressively as ability moves away from this optimal point, especially at higher ability levels (theta > 1), where the test loses its discriminatory power. Figure 4 (Wright Map) provides an integrated visual representation that aligns the distribution of participants' ability (histogram on the left) with the distribution of item threshold difficulty (dots on the right) on the same logit scale. The map visually confirms the conclusion derived from the Test Information Curve. There is a partial mismatch between the difficulty of the test and the ability of the sample. Most of the difficulty thresholds of the items are located in the low to moderate ability range (theta from − 5.0 to -1.0), while most participants are grouped at a higher ability level. This indicates that the test, in its current form, is more difficult than necessary for most test-takers and is better targeted to accurately differentiate between individuals at the lower end of the cognitive spectrum. Finally, Fig. 5 shows the reliability function of the test, which varies depending on the skill level (theta). In line with the Test Information Curve, reliability peaks (at approximately 0.83) at a skill level of theta ≈ -2.0, the same point where the test provides the most information. Reliability decreases as participants' ability moves away from this optimal point, being considerably lower in the high ability ranges. These findings highlight one of the conceptual advantages of IRT over CTT: reliability is not a static property of the test, but a function of the interaction between test difficulty and individual ability. For this sample, the marginal (average) reliability of the test was 0.48, a modest value that reflects the mismatch between the difficulty of the test and the skill distribution of the population assessed. DISCUSSION Analyzing the validity and reliability of the RUDAS-PE represents a significant advance in the assessment of individuals from Indigenous communities, allowing for more accurate and culturally valid evaluations of cognitive function. This effort has important implications for its clinical use in the early detection of cognitive impairment and early dementia. By integrating qualitative and quantitative approaches, we sought to ensure that the instrument accurately captured the cognitive construct relevant to this cultural context. Although RUDAS was designed as a screening tool for culturally, linguistically, and educationally diverse populations [ 15 ], other studies have shown that adaptations are still needed to accurately assess dementia in diverse populations [ 51 , 52 ]. The RUDAS-PE has previously been adapted to suit the context of Peruvian populations [ 21 ]. However, the cross-cultural adaptation process showed that it could not be used to identify cognitive deficits without linguistic, semantic, and cultural adaptation; its direct use in a culturally different population could lead to misclassification rather than true cognitive impairment. Despite the adverse socioeconomic conditions of this community, the natives of the Shawi ethnic group still preserve the vitality and regional diversity of their language. This provided key cultural considerations for the study, given the impact of language on the understanding of their social, cultural, and health reality. In addition, we found that Shawi communities had ecological knowledge with unique linguistic structures and daily practices that differed substantially from those assumed in standardized instruments. These contextual differences highlight that culturally-based adaptation is essential not only to improve measurement accuracy but also to promote health equity in dementia screening in underserved populations [ 53 ]. The cross-cultural adaptation process strengthened the content and apparent validity of the tool and ensured that its administration is appropriate, understandable, and culturally responsive. Cognitive interviews were used to modify and add culturally relevant items, especially in the memory and judgment function. Other studies conducted in diverse populations agree with the use of cognitive interviews to detect possible comprehension difficulties focused on the user [ 54 ], unlike content validity, which was not used in the present study, as it is oriented toward the review of items by expert opinion [ 55 ]. These cultural adaptation procedures in screening tests are supported by the scientific literature to improve the reliability of the information collected [ 56 , 57 ]. In our descriptive analysis of the items, a ceiling effect is observed in most items, with 71% and 79% showing a maximum score. This response pattern reflects a ceiling effect similar to the findings reported in other screening tests [ 58 ], which are not usually reported, demonstrating a statistical and methodological weakness. Item 4, corresponding to the visuoconstruction function, showed a severe floor effect and no correlation with other items. This anomalous behavior is consistent with a study conducted in Nepal in which only 8 people responded to item Similarly, one study reported a ceiling effect in 75% of the sample and also showed that item 4 was associated with schooling [ 59 ]. It should be noted that, in the present study, 84% of older adults in the Shawi communities (n = 394) had no formal education, while 10% (n = 46) had basic education. In several countries, it has been shown that RUDAS results are independent of educational level and linguistic properties. Previous research has reported that test performance did not vary according to years of education, highlighting RUDAS as an appropriate tool for populations with low educational levels [ 14 , 15 , 19 , 21 ]. However, in our sample, educational level did have a significant impact, adding a point of controversy to the literature. This low performance on item 4 probably reflects a cultural incongruity rather than true cognitive impairment due to contextual differences. This pattern was also observed in studies that adapted visuospatial tasks for Aboriginal and Torres Strait Islander populations [ 60 ]. These results indicate that the relationship between schooling and performance on the RUDAS may not be uniform across all populations and reinforce the need to continue evaluating the validity of the scale in different sociocultural settings. Most studies have confirmed the diagnostic accuracy of the RUDAS through sensitivity and specificity analyses with AUC values exceeding 0.90 [ 21 , 22 , 34 , 35 ], with few reports validity based on internal structure. Our findings demonstrate a unidimensional structure and a substantially superior fit after we eliminiated item 4, similar to a study conducted in Indonesia demonstrating the unidimensionality of RUDAS. These results highlight that after the elimination of item 4, the AFC and TRI support the effectiveness of using a 5-factor scale. Likewise, Rasch analysis revealed that the five-item M2 offers greater measurement accuracy at low to moderate cognitive ability levels (θ = − 2) ( Fig. 3 ). This suggests that the abbreviated version of the RUDAS-PE is adequate for detecting deficits in people with low cognitive functioning, but loses discriminatory power as response ability increases, showing a discrepancy between the ability of the people assessed and the difficulty of the test ( Fig. 4 ). A similar situation was found in the Rasch analysis performed with the Montreal Cognitive Assessment (MoCA), where easy items showed low levels of discrimination, reducing measurement accuracy in high-functioning groups. Another study performed a Rasch-based analysis of the MMSE and found that the person's ability was greater than the difficulty of the item [ 61 ]. This study represents one of the first applications of Rasch modeling to RUDAS, providing a novel contribution to the psychometric evaluation of this widely used cognitive screening tool. Our findings highlight the need to revise or supplement the item pool to improve coverage across the cognitive spectrum. For reliability ( Fig. 5 ), our results reveal particular patterns. Reliability based on the Rasch model peaked (.83; theta ≈ -2.0), indicating that the instrument provides greater reliability at low levels of cognitive ability. This magnitude is consistent with the internal consistency coefficients documented in various international validation studies conducted in Ethiopia α ≥ .73, Nepal α ≥ .70, Brazil α ≥ .69, and in patients with traumatic brain injury, alpha coefficients between α ≥ .69 and α ≥ .74 have been reported [ 17 – 19 , 62 ]. In Peru, a study found an alpha of α ≥ .65 in older adults with low educational levels [ 22 ]. However, the marginal reliability (ρ = 0.48) shows little accuracy for people with high cognitive functioning, suggesting that internal consistency may vary depending on the sociocultural context and cognitive profile of the sample. By cross-culturally modifying the items, potential cultural bias was reduced and the ecological validity of the instrument was improved. This study ensures that cognitive screening is both linguistically and culturally appropriate for a historically underserved population. Accurate detection of cognitive impairment in these communities is crucial for reducing health disparities. Furthermore, item-level analysis using the Rasch model demonstrates a rigorous approach to tool validation beyond classical methods, setting a precedent for future adaptations of cognitive assessments in diverse cultural contexts [ 27 ]. The importance of combining cultural adaptation with modern psychometric techniques to improve diagnostic equity is evident, as it ensures that screening instruments do not perpetuate bias or misdiagnosis in Amazonian peoples. One of the major limitations of the study was the unfamiliarity of the research team with the Shawi language and relied on Shawi/Spanish interpreters, as most participants were Shawi monolingual speakers. Furthermore, geographical accessibility and climate conditions toughened the data collection. Lastly, given the RUDAS was originally developed in another sociocultural context, it did not objectively reflect the reality of older adults of the Shawi ethnic group. Overall, this study demonstrates that the 5-item RUDAS-PE test, culturally adapted to the Shawi context, meets psychometric properties for screening and detecting cognitive impairment in native communities, but its results should be interpreted with caution as reliability reveals inconsistency and possible random error. This finding expands the psychometric evidence for RUDAS in contexts of low educational attainment and Indigenous languages. RUDAS has the potential to be adapted depending on the cultural context of the population under study. Our study demonstrates the importance of adapting the RUDAS based on feedback from the population and to study its psychometric properties in each particular population. In addition, the scientific literature on the measurement of cognitive impairment and dementia in indigenous populations reveals the urgent need to develop instruments that respond to the characteristics of Indigenous populations [ 47 ]. Declarations CONSENT STATEMENT All participants provided informed consent prior to enrollment in the study. Author Contribution AB conceptualized the study validation, assisted with proposal development, checked the data analysis, and wrote the manuscript draft. AS conceptualized the study, developed the proposal, elaborated a table, and edited the manuscript. MN MD revised the proposal, checked the data analysis, and revised the manuscript. The biostatistician performed the data analysis. All authors contributed to the article and approved the final version that will be published; selected the journal to which the article has been submitted; and promised to take responsibility for every aspect of the work. Acknowledgement We are grateful to the study team, neurologists and data collectors for their invaluable contributions to the adaptation of study instruments and to data collection. We sincerely appreciate the study participants for their willingness to share their time and experiences. We also thank the local authorities and community leaders whose support made this study possible. Dr. Arantxa Sanchez Boluarte was supported by the Fogarty International Center of the National Institutes of Health under grants #D43TW009345 awarded to the Northern Pacific Global Health Fellows Program and #D43TW009137 awarded to the Interdisciplinary Cerebrovascular Diseases Training Program in South America. Dr. Monica Diaz was supported by the National Institute of Mental Health #K23MH131466. Dr. Alicia Boluarte was supported by the Universidad Cesar Vallejo. Data Availability Unidentifiable data included in this manuscript may be accessed upon reasonable request to the corresponding author. References World Health Organization. Dementia. March 2025. Accessed December 19, 2025. https://www.who.int/news-room/fact-sheets/detail/dementia Chowdhary N, Barbui C, Anstey KJ, et al. 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Appl Psychol Meas. 2000;24(1):50–64. doi: 10.1177/01466216000241003 Baker FB, Kim SH. The Basics of Item Response Theory Using R. Springer International Publishing; 2017. doi: 10.1007/978-3-319-54205-8 Kim S, Feldt LS. The estimation of the IRT reliability coefficient and its lower and upper bounds, with comparisons to CTT reliability statistics. Asia Pac Educ Rev. 2010;11(2):179–188. doi: 10.1007/s12564-009-9062-8 Steven P. Reise, Dennis A. Revicki. Handbook of Item Response Theory Modeling: Applications to Typical Performance Assessment. First.; 2014. Accessed December 16, 2025. https://www.routledge.com/Handbook-of-Item-Response-Theory-Modeling-Applications-to-Typical-Performance-Assessment/Reise-Revicki/p/book/9781138787858 Sayegh P, Knight BG. Cross-cultural differences in dementia: the Sociocultural Health Belief Model. Int Psychogeriatr. 2013;25(4):517–530. doi: 10.1017/S104161021200213X Bezerra CC, Toledo N das N, da Silva DF, et al. Culturally adapted cognitive assessment tool for Indigenous communities in Brazil: Content, construct, and criterion validity. Alzheimers Dement Amst Neth. 2024;16(2):e12591. doi: 10.1002/dad2.12591 Czerwinski-Alley NC, Chithiramohan T, Subramaniam H, Beishon L, Mukaetova-Ladinska EB. The Effect of Translation and Cultural Adaptations on Diagnostic Accuracy and Test Performance in Dementia Cognitive Screening Tools: A Systematic Review. J Alzheimers Dis Rep. 2024;8(1):659–675. doi: 10.3233/ADR-230198 Hodiamont F, Hock H, Ellis-Smith C, et al. Culture in the spotlight—cultural adaptation and content validity of the integrated palliative care outcome scale for dementia: A cognitive interview study. Palliat Med . 2021;35(5):962–971. doi: 10.1177/02692163211004403 Escobar-Pérez J, Cuervo-Martínez Á. VALIDEZ DE CONTENIDO Y JUICIO DE EXPERTOS: UNA APROXIMACIÓN A SU UTILIZACIÓN. Avances en Medición . 2008;(6):27–36. Chambergo-Michilot D, Custodio N, Montesinos R, et al. Brief Cognitive Screening Tools for Dementia in Low-Educated Population from South America: A Systematic Review. Dement Geriatr Cogn Disord . Published online September 29, 2025. doi: 10.1159/000548735 Coelho-Guimarães N, Garcia-Casal JA, Díaz-Mosquera S, Álvarez-Ariza M, Martínez-Abad F, Mateos-Álvarez R. Validación del RUDAS como instrumento de cribado de población con demencia en atención primaria. Aten Primaria . 2021;53(5). doi: 10.1016/j.aprim.2021.102024 Aiello EN, Rimoldi S, Bolognini N, Appollonio I, Arcara G. Psychometrics and diagnostics of Italian cognitive screening tests: a systematic review. Neurol Sci. 2022;43(2):821–845. doi: 10.1007/s10072-021-05683-4 Sepúlveda-Ibarra C, Chaparro FH, Marcotti A, Soto G, Slachevsky A. Normalization of Rowland Universal Dementia Assessment Scale (RUDAS) in Chilean older people. Dement Neuropsychol. 2023;17:e20230033. doi: 10.1590/1980-5764-dn-2023-0033 Rowland JT, Basic D, Storey JE, Conforti DA. The Rowland Universal Dementia Assessment Scale (RUDAS) and the Folstein MMSE in a multicultural cohort of elderly persons. Int Psychogeriatr . 2006;18(1):111–120. doi: 10.1017/S1041610205003133 Melo DM de, Barbosa AJG, Castro NR de, Neri AL. Mini-Mental State Examination in Brazil: An Item Response Theory Analysis. Paid Ribeirão Preto . 2020;30:e3014. doi: https://doi.org/10.1590/1982-4327e3014 Cheng Y, Zhang Y, Zhang Y, WU YH, Zhang S. Reliability and validity of the Rowland Universal Dementia Assessment Scale for patients with traumatic brain injury. Appl Neuropsychol Adult. 2022;29(5):1160–1166. doi: 10.1080/23279095.2020.1856850 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8852544","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":592953385,"identity":"da65007f-96c2-44db-8a92-46c757a3969a","order_by":0,"name":"Alicia Boluarte Carbajal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACCQbGA2AGewOYYmwgQgvDATDFc4BkLRIJRGrhn9184DBPzb06fsk3hp95GGxkNxzgPfgAryV3jiUc5jlWLCE5O8dYmochzXjDAb5kA7zW3MgxOMzDliBhcDvHjHEGw+HEDQd4zCTw6ZAHa/mXIGF/8wxIy3+QFvMf+LQYgLTwtgFtkeAxY/jAcABsC153GQL9cnBuX4LkjDNpxRIfDJKNZx7mS8brMLnbzQcfvPmWwM/ffnjjh4QKO9m+470HP+C1Bs2dQMzMQ4IGKCBDyygYBaNgFAxrAADSXUsAc3pCbAAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad Cesar Vallejo","correspondingAuthor":true,"prefix":"","firstName":"Alicia","middleName":"Boluarte","lastName":"Carbajal","suffix":""},{"id":592953387,"identity":"2443471b-7b04-43dd-bfe6-e55539d4589f","order_by":1,"name":"Arantxa Sanchez Boluarte","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Arantxa","middleName":"Sanchez","lastName":"Boluarte","suffix":""},{"id":592953395,"identity":"91cdd376-2ee1-4797-a418-4d53514af009","order_by":2,"name":"Marleny Nolasco","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Marleny","middleName":"","lastName":"Nolasco","suffix":""},{"id":592953397,"identity":"016e4a0e-c0ee-4cb1-ad1d-830bbdc0f865","order_by":3,"name":"Monica Diaz","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Monica","middleName":"","lastName":"Diaz","suffix":""}],"badges":[],"createdAt":"2026-02-11 13:55:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8852544/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8852544/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103226966,"identity":"4bc3304a-85fd-41fe-895b-95fb5c314848","added_by":"auto","created_at":"2026-02-23 11:21:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49658,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of RUDAS-PE response ratios\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852544/v1/28d715b36c4110e019bd7750.jpg"},{"id":103226967,"identity":"9813a7da-3691-4f3b-859e-913b69625eec","added_by":"auto","created_at":"2026-02-23 11:21:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49884,"visible":true,"origin":"","legend":"\u003cp\u003ePolychoric correlations between RUDAS items among Shawi Indigenous participants (N=472)\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852544/v1/99c291a8d0cb6af5055cb288.jpg"},{"id":103226969,"identity":"16ec9eb8-f822-47a4-9b94-9c6d9ad9f1d7","added_by":"auto","created_at":"2026-02-23 11:21:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42718,"visible":true,"origin":"","legend":"\u003cp\u003eTest Information and Standard Errors\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852544/v1/02d6ccba31b87f2d47173788.jpg"},{"id":103226965,"identity":"70624717-49a7-41de-bf31-746749ab5806","added_by":"auto","created_at":"2026-02-23 11:21:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29607,"visible":true,"origin":"","legend":"\u003cp\u003eWrights Map for the distribution of items\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852544/v1/e37b242b6c47e427f9911100.jpg"},{"id":103505812,"identity":"947aa392-699d-42ab-99ec-6d986e022ca2","added_by":"auto","created_at":"2026-02-26 13:33:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28774,"visible":true,"origin":"","legend":"\u003cp\u003eReliability of adapted RUDAS-PE\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852544/v1/f0dbf868966ab522f52b30b5.jpg"},{"id":103509875,"identity":"486dad92-b903-4920-b4c5-e13008b4835c","added_by":"auto","created_at":"2026-02-26 14:01:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":877403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8852544/v1/addb2d3a-5742-40fc-89b3-27682845e9ac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePsychometric Properties of the Rowland University Dementia Assessment Scale – Peruvian Version Among Indigenous Amazonian Communities\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDementia is one of the leading causes of disability in older adults and the seventh leading cause of death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Currently, an estimated 55\u0026nbsp;million people worldwide live with dementia, making it an international public health priority\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Latin America, the prevalence of dementia is 8.4%, which is comparable to or higher than estimates of 7\u0026ndash;8% in high-income countries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], while in Indigenous communities it appears to be relatively low, with estimates of 0.9\u0026ndash;4.9%, particularly in Amazonian populations in Brazil [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and Bolivia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the scarcity of epidemiological studies and the lack of instruments to detect dementia make it particularly difficult to study.\u003c/p\u003e \u003cp\u003eIn recent years, the study of cognitive impairment in Indigenous populations has become particularly relevant due to the need to understand how sociocultural, environmental, and biological factors affect the brain aging process [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This perspective has driven the development and adaptation of psychological assessment tools that enable the early identification of cognitive decline and, therefore, improve the timely detection of dementia [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Native communities generally have lifestyles, linguistic patterns, and traditional knowledge systems that can differ significantly from the urban contexts in which most cognitive assessment instruments have been developed. Therefore, it is crucial to design and adapt tools that account for unique aspects of these populations, the context in which they live, and their daily experiences.\u003c/p\u003e \u003cp\u003eHistorically, Indigenous peoples have been systematically marginalized from their rights and decisions that directly affect them, leading to the loss of their lands, their culture, limited access to resources and services, and food insecurity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The Shawi people live on a subsistence economy consisting of agriculture, fishing, and hunting [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. They are also the Peruvian ethnic group with the lowest percentage of state recognition of their lands and institutional affiliation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This reality shows that Indigenous peoples face poorer economic, social, and health conditions as a result of structural inequalities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Thus, the cognitive function assessment in Indigenous populations requires culturally sensitive approaches, without biases accounting for language, culture, literacy, and educational level [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpecifically, the Rowland Universal Dementia Assessment Scale (RUDAS) has been increasingly studied as a culturally valid alternative to traditional cognitive screening tools [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] used in various multicultural contexts as a screening tool [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It has been shown to have comparable or superior performance to the Mini Mental State Exam (MMSE) in the detection of mild cognitive impairment and dementia, particularly in people with low educational attainment or illiteracy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The RUDAS has been shown to have good concurrent validity [\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], but few studies report evidence of construct validity. Those that do report construct validity have been conducted with small samples, limiting its analysis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It is crucial that cognitive screening tests reflect robust psychometric properties of validity and reliability, particularly when adapted to culturally and linguistically diverse populations.\u003c/p\u003e \u003cp\u003eOur study represents the first attempt to analyze the psychometric performance of RUDAS in older adults from Shawi Amazonian communities. We sought to examine the validity and reliability of the RUDAS in this population. Following cross-cultural adaptation, our study used modern psychometric frameworks examining structural models through Classical Test Theory and Item Response Theory to explore the latent structure of cognitive domains and refine measurement accuracy of the Peruvian version of RUDAS [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eWe conducted a psychometric study that aimed to ensure comprehensive and rigorous assessment of instrument reliability and validity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We conducted analyses using Classical Test Theory (CTT) and Item Response Theory (IRT). Qualitative and quantitative methods were used aligning with international standards for test adaptation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003e Participants were recruited between October 2024 and February 2025 in rural communities recognized as Shawi by the Ministry of Culture, in the district of Balsapuerto, in the Loreto region of Peru. The Shawi Indigenous people live mainly in the districts of Cahuapanas and Balsapuerto (Loreto region) in the Amazon, and are considered one of the most vulnerable Amazonian groups, with 26,841 inhabitants [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is estimated that 8% of Shawi people in Loreto, Peru, are over 50 years old, based on 2017 National Census data [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This results in approximately 2,126 Shawi people over the age of 50.\u003c/p\u003e \u003cp\u003eThis study included adults over 50 years of age who met at least one of the following criteria: 1) native language of Shawi, 2) residence in the district of Balsapuerto, either in the town of Balsapuerto or residing in Shawi communities and speaking Shawi and/or Spanish. Participants were recruited using door-to-door visits to the home and community meetings, strategies that were necessary given the scattered geography and adverse weather conditions in the area. Recruitment was also conducted during the monthly government programs, such as Pensi\u0026oacute;n 65 and Juntos, when Peruvian citizens (including Shawi) over age 65 receive a monthly incentive from the government.\u003c/p\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eWe first adapted the RUDAS-PE was adapted using qualitative methods. First, opportunities for dialogue were created with residents and community leaders of the Shawi community, allowing for a deep immersion into Shawi culture. These informal group meetings were part of an apparent validity process [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], in which interviews were conducted with two community leaders and two Shawi residents, as well as local government leaders. We administered the RUDAS-PE during each interview and collected any observations and feedback on the RUDAS-PE items. Using this process, we assessed acceptability and understanding of each instrument item. Subsequently, we conducted cognitive interviews to these participants to obtain evidence of validity based on the response process [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], to ensure the instrument was understandable and relevant to the target population.\u003c/p\u003e \u003cp\u003eWe then incorporated feedback from these cognitive interviews. Native experts from the Shawi culture (\u0026ldquo;Apus,\u0026rdquo; primary school teachers in the community) acted as bilingual (Spanish/Shawi) facilitators to incorporate the feedback we received to adapt the instrument. Finally, we conducted iterative pilot tests to evaluate the suitability of each item translated and back-translated by two native Shawi speakers. The objective of this process was to ensure the semantic, conceptual, and cultural adequacy of the instrument to ensure that the instructions and items were understandable and relevant to the Shawi participants.\u003c/p\u003e \u003cp\u003eFollowing the pilot testing, data collection was conducted. The data collection team was comprised of two native Shawi speakers with higher technical training, a Shawi teacher, a medical student, and three researchers, who participated on an ad hoc basis.\u003c/p\u003e \u003cp\u003ePermission to access 36 native communities was obtained through the \u0026ldquo;Apus,\u0026rdquo; who are the authorities representing each community. The evaluations were carried out through home visits and meetings and people voluntarily agreed to participate by signing the Informed Consent form. Nearly all of older adults in each selected community were evaluated.\u003c/p\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cp\u003eThe RUDAS is a brief six-item test that assesses memory, praxis, language, judgment, visuoconstruction, and body orientation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It was originally developed in a multiethnic Australian population, where diagnostic accuracy did not vary by years of education (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20) or preferred language (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33). The RUDAS showed a sensitivity of 89% (95% CI: 76%-96%) and a specificity of 98% (95% CI: 88%-97%) with a cutoff point of 22/23 out of 30. The scale has been translated and adapted into multiple languages, although not all versions have undergone formal validation and diagnostic accuracy studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Peru, the RUDAS has been studied in Spanish-speaking urban and rural populations and demonstrated concurrent validity and its ability to discriminate between dementia and mild cognitive impairment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, some studies reported alpha coefficients below the expected optimal values [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study used the Peruvian version of the RUDAS (RUDAS-PE), linguistically and culturally adapted for use in Peruvian populations and is considered the most appropriate version for populations with sociocultural diversity and low levels of education [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eInitially, an exploratory analysis of the data was performed to understand the behavior of each of the six dimensions that make up the RUDAS-PE. The response ratio for each score category was reported in order to identify possible floor effects (accumulation of responses in the lowest categories) or ceiling effects (accumulation in the highest categories), which could limit an item's ability to discriminate between participants [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Given the ordinal nature of the dimension scores, a polychoric correlation matrix was estimated for the classical test theory (CTT) analyses. This method is the most appropriate for estimating the latent association between ordered categorical variables and will serve as a basis for evaluating item interrelationship and common factorial structure viability [\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo evaluate the construct validity of the scale, a Confirmatory Factor Analysis (CFA) model was fitted. Based on the underlying theory of RUDAS-PE, a unidimensional structure was hypothesized where the six dimensions are explained by a single latent factor of \u0026ldquo;general cognitive ability.\u0026rdquo; We used the Weighted Least Squares Mean and Variance method to estimate the model, considered the most appropriate estimator for categorical or ordinal data [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The model fit was evaluated using multiple indices: the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI), where values\u0026thinsp;\u0026gt;\u0026thinsp;0.90 indicate an acceptable fit and \u0026gt;\u0026thinsp;0.95 an excellent fit; and the Root Mean Square Error of Approximation (RMSEA), where values\u0026thinsp;\u0026lt;\u0026thinsp;0.08 are acceptable and \u0026lt;\u0026thinsp;0.06 are desirable [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. If the initial model did not show an adequate fit, the modification indices were examined to identify sources of misfit and guide a possible re-specification of the model, such as the removal of problematic items.\u003c/p\u003e \u003cp\u003eSubsequently, Item Response Theory (IRT) was applied to gain a deeper understanding of the functioning of the items and the scale as a whole. Since most dimensions of the RUDAS-PE are scored by accumulating points for correct answers (partial credit), the Generalized Partial Credit Model (GPCM) was selected. This model is ideal for polytomous items where each response category represents an incremental level of success or ability [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModel Fit Evaluation and Item in IRT\u003c/h2\u003e \u003cp\u003eThe overall fit of the GPCM model was evaluated using the M2 statistic and its associated indices (CFI, TLI, RMSEA, SRMR) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. At the individual level, the fit of each item to the model was examined using the S-X\u0026sup2; statistic. This allowed us to identify whether any items behaved abnormally or inconsistently with the proposed model [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOnce the TRI model was adjusted and validated, the characteristic curves were generated and analyzed. The Item Characteristic Curves (ICC) graphically showed the probability of obtaining each score on an item across the entire ability spectrum [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The Item Information Curves (IIC) and the Test Information Curve were fundamental to the analysis. These curves indicate the ranges of the ability in which each item and the test as a whole provide the greatest measurement accuracy. The Typical Error Curve was also generated, which is inversely proportional to the information and visualizes the degree of expected error in the estimation of ability for each level of theta.\u003c/p\u003e \u003cp\u003eFinally, the reliability of the test was examined from the perspective of IRT, in which reliability is conceptualized as a function of ability [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The marginal reliability of the test, which depends on the Test Information Curve and the distribution of ability in the sample, was calculated and graphed. This approach allows us to understand how reliability varies across the ability continuum, providing a more accurate and appropriate view of measurement consistency [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e This study was approved by the local community leaders (\u0026ldquo;Apus\u0026rdquo;) of each community and the ethics committees (SIDISI 215474) of the Universidad Peruana Cayetano Heredia and the Universidad Cesar Vallejo in Lima, Peru. The study was exempt from IRB review by the University of North Carolina at Chapel Hill IRB in North Carolina, USA. The study was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Written informed consent was provided by each participant according to standard procedures. In the case the person was unable read and/or write, they provided their thumbprint as a proxy for signature. Participants benefited from the provision of their individual test results, as well as relevant counseling and referral to a specialist in their area when clinically indicated.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCultural Adaptations\u003c/h2\u003e \u003cp\u003eThrough meetings with representatives of a sample of selected communities, we explored their worldview, values, beliefs, and community practices, which served as input for the next stage of cultural adaptation.\u003c/p\u003e \u003cp\u003eEight cognitive interviews were conducted with people over the age of 50 in various social roles in their community (such as healers, \u0026ldquo;Apus,\u0026rdquo; elders, and bilingual teachers), which helped identify how the Shawis conceptualize concepts such as attention, memory, and neurological health. Key aspects were identified, such as the perception of time, traditional forms of knowledge transmission, and the conceptualization of memory for remembering recent events. These findings allowed us to incorporate concepts related to their traditional activities of hunting, fishing, and agriculture into the instrument, ensuring that the items and each domain reflected the cognitive profile of the Shawi people.\u003c/p\u003e \u003cp\u003eWe adjusted the wording and scoring of the test items using the prior suggestions. Translation and back-translation into the original language ensured semantic equivalence and eliminated cultural biases, incorporating relevant elements of the Shawi community's sociocultural context.\u003c/p\u003e \u003cp\u003eDuring the pilot study, comprehension problems, instruction and item ambiguities were resolved, and test administration time was adjusted. Significant sociocultural and linguistic differences were found. Specifically, the Shawi use a orientation system based on the position of the moon, concrete visuospatial activities, and familiarity with certain tasks based on the local ecological context. Adaptations of the RUDAS-PE prioritized the preservation of fundamental cognitive constructs, while modifying task instructions, visual stimuli, and culturally specific references that were unknown or inaccessible to community members.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of participants (N\u0026thinsp;=\u0026thinsp;472).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en (%), median (IQR),\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSociodemographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (54\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYears of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCivil status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMarried / Cohabitating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSalaried job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eElectricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e278 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInternet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eN people in same household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eShawi ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e399 (90.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMonolingual Shawi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e289 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBilingual Shawi - Spanish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDaily traditional alcohol intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e413 (93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* median, IQR- interquartile range; SD- standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e details the proportional distribution of responses for each of the six dimensions of the RUDAS-PE scale. As expected in a cognitive screening scale applied to a community sample, most items demonstrated an asymmetric distribution with a pronounced ceiling effect. Specifically, items 2 (Orientation), 5 (Judgment), 6 (Memory), and 7 (Language) showed that between 71% and 79% of those evaluated obtained the maximum score. This pattern is consistent with a population largely without cognitive impairment. However, item 4 (Visuospatial Construction) exhibited markedly anomalous behavior, showing a severe floor effect: 82% of the sample obtained the minimum score of 0.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis low variability and its distribution, which is opposite to that of the rest of the scale, suggest that item 4 may not be functioning consistently with the other dimensions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTo examine the interrelationship between the dimensions of the RUDAS-PE and to evaluate the internal consistency of the scale, a polychoric correlation matrix was calculated, the results of which are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The analysis revealed moderate and statistically significant positive correlations between most items, with coefficients ranging from 0.19 to 0.48, suggesting that they share a common latent construct.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eHowever, in line with the anomalies detected in the descriptive analysis, item 4 (Visuospatial Construction) shows a markedly divergent correlation pattern. This item shows correlations close to zero and insignificant with virtually all other dimensions of the scale. This lack of consistency, probably exacerbated by the severe floor effect previously observed, compromises its construct validity and suggests that its inclusion in a one-dimensional model would negatively affect the overall fit, justifying its potential exclusion in the factorial modeling stage.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory Factor Analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the structural validity and test the unidimensionality hypothesis of the RUDAS-PE scale, two Confirmatory Factor Analysis (CFA) models were fitted. The first model (M1) included the six original dimensions, while the second model (M2) was re-specified excluding item 4, whose anomalous behavior was identified in the descriptive and correlation analyses.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the fit indices for both models, demonstrating a substantial and decisive improvement in the re-specified model. Model M1 showed a poor fit to the data, with a significant Chi-square value (χ\u0026sup2;(9)\u0026thinsp;=\u0026thinsp;46.306, p \u0026lt; .001), a CFI of 0.884, and a TLI of 0.807, both below acceptable thresholds. In addition, the RMSEA was high (0.099), indicating a considerable discrepancy between the model and the observed data. In stark contrast, model M2 showed excellent fit: although the Chi-square remained significant (χ\u0026sup2;(5)\u0026thinsp;=\u0026thinsp;12.887, p = .024), the relative fit indices improved dramatically to a CFI of 0.969 and a TLI of 0.939. The RMSEA was also reduced to 0.061. This remarkable improvement in all fit indices confirms that the exclusion of item 4 resolves the main source of misfit and supports the plausibility of a unidimensional structure for the five-item scale.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eAdjustment indices between the two AFC models from RUDAS-PE\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e (gl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep (Chi2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSEA CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.306 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.072, 0.128]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.887 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.020, 0.103]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTRI Model: Generalized Partial Credit Model\u003c/h2\u003e \u003cp\u003eFollowing the same model specification logic used in CFA, the performance of the RUDAS-PE scale was evaluated under the IRT framework. A Generalized Partial Credit Model (GPCM) was fitted to examine the fit of the data to a unidimensional structure. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the fit indices for the two TRI models: the initial model (M1) with six items and the re-specified model (M2) with five items, excluding rudas_4. The results of the IRT analysis strongly corroborate the CFA findings. Although model M1 shows a marginally acceptable fit (CFI\u0026thinsp;=\u0026thinsp;0.941, TLI\u0026thinsp;=\u0026thinsp;0.902), it has an RMSEA (0.074) that suggests room for improvement and a significant Chi-square value (p \u0026lt; .001). In contrast, model M2 shows a dramatic improvement and excellent fit to the data, with a non-significant Chi-square (p = .303), CFI and TLI indices close to unity (0.997 and 0.993, respectively), and a very low RMSEA (0.022). This convergence of evidence between the AFC and TRI frameworks reinforces the conclusion that the five-item model is psychometrically superior and more robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eAdjustment indices between the two GPCM-TRI models from RUDAS-PE\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e (gl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep (Chi2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSEA CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.045, 0.104]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.000, 0.074]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the Test Information Curve (TIC) and its corresponding Standard Error of Measurement (SEM) curve. The ITC, which is the sum of the information from the individual items, reaches its peak at a skill level of approximately theta = -2.0. This confirms that the five-item RUDAS-PE scale, as a whole, offers the highest measurement accuracy for identifying individuals on the low-moderate cognitive ability spectrum. Conversely, the SEM curve shows that measurement error is minimal in this same range and increases progressively as ability moves away from this optimal point, especially at higher ability levels (theta\u0026thinsp;\u0026gt;\u0026thinsp;1), where the test loses its discriminatory power.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (Wright Map) provides an integrated visual representation that aligns the distribution of participants' ability (histogram on the left) with the distribution of item threshold difficulty (dots on the right) on the same logit scale. The map visually confirms the conclusion derived from the Test Information Curve. There is a partial mismatch between the difficulty of the test and the ability of the sample. Most of the difficulty thresholds of the items are located in the low to moderate ability range (theta from \u0026minus;\u0026thinsp;5.0 to -1.0), while most participants are grouped at a higher ability level. This indicates that the test, in its current form, is more difficult than necessary for most test-takers and is better targeted to accurately differentiate between individuals at the lower end of the cognitive spectrum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the reliability function of the test, which varies depending on the skill level (theta). In line with the Test Information Curve, reliability peaks (at approximately 0.83) at a skill level of theta \u0026asymp; -2.0, the same point where the test provides the most information. Reliability decreases as participants' ability moves away from this optimal point, being considerably lower in the high ability ranges. These findings highlight one of the conceptual advantages of IRT over CTT: reliability is not a static property of the test, but a function of the interaction between test difficulty and individual ability. For this sample, the marginal (average) reliability of the test was 0.48, a modest value that reflects the mismatch between the difficulty of the test and the skill distribution of the population assessed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAnalyzing the validity and reliability of the RUDAS-PE represents a significant advance in the assessment of individuals from Indigenous communities, allowing for more accurate and culturally valid evaluations of cognitive function. This effort has important implications for its clinical use in the early detection of cognitive impairment and early dementia.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBy integrating qualitative and quantitative approaches, we sought to ensure that the instrument accurately captured the cognitive construct relevant to this cultural context. Although RUDAS was designed as a screening tool for culturally, linguistically, and educationally diverse populations\u003c/span\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eother studies have shown that adaptations are still needed to accurately assess dementia in diverse populations\u003c/span\u003e [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe RUDAS-PE has previously been adapted to suit the context of Peruvian populations\u003c/span\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHowever, the cross-cultural adaptation process showed that it could not be used to identify cognitive deficits without linguistic, semantic, and cultural adaptation; its direct use in a culturally different population could lead to misclassification rather than true cognitive impairment.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e Despite the adverse socioeconomic conditions of this community, the natives of the Shawi ethnic group still preserve the vitality and regional diversity of their language. This provided key cultural considerations for the study, given the impact of language on the understanding of their social, cultural, and health reality. In addition, we found that Shawi communities had ecological knowledge with unique linguistic structures and daily practices that differed substantially from those assumed in standardized instruments. These contextual differences highlight that culturally-based adaptation is essential not only to improve measurement accuracy but also to promote health equity in dementia screening in underserved populations\u003c/span\u003e [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe cross-cultural adaptation process strengthened the content and apparent validity of the tool and ensured that its administration is appropriate, understandable, and culturally responsive. Cognitive interviews were used to modify and add culturally relevant items, especially in the memory and judgment function. Other studies conducted in diverse populations agree with the use of cognitive interviews to detect possible comprehension difficulties focused on the user\u003c/span\u003e [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eunlike content validity, which was not used in the present study, as it is oriented toward the review of items by expert opinion\u003c/span\u003e [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThese cultural adaptation procedures in screening tests are supported by the scientific literature to improve the reliability of the information collected\u003c/span\u003e [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn our descriptive analysis of the items, a ceiling effect is observed in most items, with 71% and 79% showing a maximum score. This response pattern reflects a ceiling effect similar to the findings reported in other screening tests\u003c/span\u003e [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ewhich are not usually reported, demonstrating a statistical and methodological weakness.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eItem 4, corresponding to the visuoconstruction function, showed a severe floor effect and no correlation with other items. This anomalous behavior is consistent with a study conducted in Nepal in which only 8 people responded to item Similarly, one study reported a ceiling effect in 75% of the sample and also showed that item 4 was associated with schooling\u003c/span\u003e [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIt should be noted that, in the present study, 84% of older adults in the Shawi communities (n\u0026thinsp;=\u0026thinsp;394) had no formal education, while 10% (n\u0026thinsp;=\u0026thinsp;46) had basic education. In several countries, it has been shown that RUDAS results are independent of educational level and linguistic properties. Previous research has reported that test performance did not vary according to years of education, highlighting RUDAS as an appropriate tool for populations with low educational levels\u003c/span\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHowever, in our sample, educational level did have a significant impact, adding a point of controversy to the literature. This low performance on item 4 probably reflects a cultural incongruity rather than true cognitive impairment due to contextual differences. This pattern was also observed in studies that adapted visuospatial tasks for Aboriginal and Torres Strait Islander populations\u003c/span\u003e [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThese results indicate that the relationship between schooling and performance on the RUDAS may not be uniform across all populations and reinforce the need to continue evaluating the validity of the scale in different sociocultural settings.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMost studies have confirmed the diagnostic accuracy of the RUDAS through sensitivity and specificity analyses with AUC values exceeding 0.90\u003c/span\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ewith few reports validity based on internal structure. Our findings demonstrate a unidimensional structure and a substantially superior fit after we eliminiated item 4, similar to a study conducted in Indonesia demonstrating the unidimensionality of RUDAS.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThese results highlight that after the elimination of item 4, the AFC and TRI support the effectiveness of using a 5-factor scale. Likewise, Rasch analysis revealed that the five-item M2 offers greater measurement accuracy at low to moderate cognitive ability levels (θ = \u0026minus;\u0026thinsp;2) (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e). This suggests that the abbreviated version of the RUDAS-PE is adequate for detecting deficits in people with low cognitive functioning, but loses discriminatory power as response ability increases, showing a discrepancy between the ability of the people assessed and the difficulty of the test (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e). A similar situation was found in the Rasch analysis performed with the Montreal Cognitive Assessment (MoCA), where easy items showed low levels of discrimination, reducing measurement accuracy in high-functioning groups. Another study performed a Rasch-based analysis of the MMSE and found that the person's ability was greater than the difficulty of the item\u003c/span\u003e [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThis study represents one of the first applications of Rasch modeling to RUDAS, providing a novel contribution to the psychometric evaluation of this widely used cognitive screening tool. Our findings highlight the need to revise or supplement the item pool to improve coverage across the cognitive spectrum.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFor reliability (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e), our results reveal particular patterns. Reliability based on the Rasch model peaked (.83; theta \u0026asymp; -2.0), indicating that the instrument provides greater reliability at low levels of cognitive ability. This magnitude is consistent with the internal consistency coefficients documented in various international validation studies conducted in Ethiopia α\u0026thinsp;\u0026ge;\u0026thinsp;.73, Nepal α\u0026thinsp;\u0026ge;\u0026thinsp;.70, Brazil α\u0026thinsp;\u0026ge;\u0026thinsp;.69, and in patients with traumatic brain injury, alpha coefficients between α\u0026thinsp;\u0026ge;\u0026thinsp;.69 and α\u0026thinsp;\u0026ge;\u0026thinsp;.74 have been reported\u003c/span\u003e [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn Peru, a study found an alpha of α\u0026thinsp;\u0026ge;\u0026thinsp;.65 in older adults with low educational levels\u003c/span\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHowever, the marginal reliability (ρ\u0026thinsp;=\u0026thinsp;0.48) shows little accuracy for people with high cognitive functioning, suggesting that internal consistency may vary depending on the sociocultural context and cognitive profile of the sample.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBy cross-culturally modifying the items, potential cultural bias was reduced and the ecological validity of the instrument was improved. This study ensures that cognitive screening is both linguistically and culturally appropriate for a historically underserved population. Accurate detection of cognitive impairment in these communities is crucial for reducing health disparities. Furthermore, item-level analysis using the Rasch model demonstrates a rigorous approach to tool validation beyond classical methods, setting a precedent for future adaptations of cognitive assessments in diverse cultural contexts\u003c/span\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe importance of combining cultural adaptation with modern psychometric techniques to improve diagnostic equity is evident, as it ensures that screening instruments do not perpetuate bias or misdiagnosis in Amazonian peoples.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e One of the major limitations of the study was the unfamiliarity of the research team with the Shawi language and relied on Shawi/Spanish interpreters, as most participants were Shawi monolingual speakers. Furthermore, geographical accessibility and climate conditions toughened the data collection. Lastly, given the RUDAS was originally developed in another sociocultural context, it did not objectively reflect the reality of older adults of the Shawi ethnic group.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOverall, this study demonstrates that the 5-item RUDAS-PE test, culturally adapted to the Shawi context, meets psychometric properties for screening and detecting cognitive impairment in native communities, but its results should be interpreted with caution as reliability reveals inconsistency and possible random error. This finding expands the psychometric evidence for RUDAS in contexts of low educational attainment and Indigenous languages.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRUDAS has the potential to be adapted depending on the cultural context of the population under study. Our study demonstrates the importance of adapting the RUDAS based on feedback from the population and to study its psychometric properties in each particular population. In addition, the scientific literature on the measurement of cognitive impairment and dementia in indigenous populations reveals the urgent need to develop instruments that respond to the characteristics of Indigenous populations\u003c/span\u003e [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCONSENT STATEMENT\u003c/h2\u003e \u003cp\u003e All participants provided informed consent prior to enrollment in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAB conceptualized the study validation, assisted with proposal development, checked the data analysis, and wrote the manuscript draft. AS conceptualized the study, developed the proposal, elaborated a table, and edited the manuscript. MN MD revised the proposal, checked the data analysis, and revised the manuscript. The biostatistician performed the data analysis. All authors contributed to the article and approved the final version that will be published; selected the journal to which the article has been submitted; and promised to take responsibility for every aspect of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to the study team, neurologists and data collectors for their invaluable contributions to the adaptation of study instruments and to data collection. We sincerely appreciate the study participants for their willingness to share their time and experiences. We also thank the local authorities and community leaders whose support made this study possible. Dr. Arantxa Sanchez Boluarte was supported by the Fogarty International Center of the National Institutes of Health under grants #D43TW009345 awarded to the Northern Pacific Global Health Fellows Program and #D43TW009137 awarded to the Interdisciplinary Cerebrovascular Diseases Training Program in South America. Dr. Monica Diaz was supported by the National Institute of Mental Health #K23MH131466. Dr. Alicia Boluarte was supported by the Universidad Cesar Vallejo.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eUnidentifiable data included in this manuscript may be accessed upon reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Dementia. March 2025. 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Appl Neuropsychol Adult. 2022;29(5):1160\u0026ndash;1166. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/23279095.2020.1856850\u003c/span\u003e\u003cspan address=\"10.1080/23279095.2020.1856850\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-dementia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Dementia](https://www.nature.com/npjdementia/)","snPcode":"44400","submissionUrl":"https://submission.springernature.com/new-submission/44400/3","title":"npj Dementia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cognitive impairment, dementia, Shawi, RUDAS-PE, psychometry, Indigenous people","lastPublishedDoi":"10.21203/rs.3.rs-8852544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8852544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCognitive screening tools are rarely validated for Indigenous populations of Latin America, a barrier to early dementia detection. We culturally adapted and evaluated the psychometric performance of the Peruvian version of the Rowland Universal Dementia Assessment Scale (RUDAS-PE) for Shawi Indigenous Amazonian communities. We enrolled 472 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years who completed the RUDAS-PE. Cognitive interviews and community feedback revealed cultural mismatches, particularly in visuoconstruction tasks, requiring linguistic and contextual item modifications. The original 6-item structure showed poor fit, while a 5-item version demonstrated strong unidimensionality and improved model fit (confirmatory factor analysis, comparative fit index\u0026thinsp;=\u0026thinsp;0.97; Root Mean Square Error of Approximation\u0026thinsp;=\u0026thinsp;0.06). Item response analyses confirmed high measurement precision at low-moderate educational levels and identified severe floor effects in visuoconstruction. Culturally-valid adaptations of brief cognitive tools are essential to avoid diagnostic bias in Indigenous populations. Our findings provide a scalable framework for adaptation of cognitive screening for Indigenous communities.\u003c/p\u003e","manuscriptTitle":"Psychometric Properties of the Rowland University Dementia Assessment Scale – Peruvian Version Among Indigenous Amazonian Communities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 11:21:15","doi":"10.21203/rs.3.rs-8852544/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-18T14:36:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-13T23:22:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114189801058242429322988133971982879890","date":"2026-03-11T14:35:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87609836548719350170223401814427542282","date":"2026-03-05T21:45:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T17:19:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322329860781304784888641994559618780030","date":"2026-02-17T15:49:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-16T21:15:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-13T19:12:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T10:17:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Dementia","date":"2026-02-11T13:38:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-dementia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Dementia](https://www.nature.com/npjdementia/)","snPcode":"44400","submissionUrl":"https://submission.springernature.com/new-submission/44400/3","title":"npj Dementia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9fa3a39a-d8e1-4464-ac1d-5fc1b2f41a8c","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63372356,"name":"Health sciences/Health care"},{"id":63372357,"name":"Health sciences/Medical research"},{"id":63372358,"name":"Biological sciences/Psychology"},{"id":63372359,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-15T00:38:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 11:21:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8852544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8852544","identity":"rs-8852544","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}