Identifying individuals at risk of cognitive decline: Cross-sectional analysis of variability in neuropsychological test scores among community-dwelling older adults

Identifying individuals at risk of cognitive decline: Cross-sectional analysis of variability in neuropsychological test scores among community-dwelling older adults | 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 Research Article Identifying individuals at risk of cognitive decline: Cross-sectional analysis of variability in neuropsychological test scores among community-dwelling older adults Lucía Sáez-González, Luis Antonio Martínez-López, Cristina García-García, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8143343/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Apr, 2026 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Cognitive impairment is a major public health concern due to its impact on functional independence and its risk of progression to dementia. Early detection is critical, but the estimated prevalence varies substantially depending on the screening tool used and the role of modifiable metabolic risk factors, in accelerating cognitive aging. Objective This study aimed to evaluate the concordance among the Montreal Cognitive Assessment (MoCA) using two alternative cut-offs and three other validated cognitive screening tools, and to analyze factors associated with lower cognitive performance in community-dwelling older adults. Methods A cross-sectional study was conducted with N = 166 community-dwelling patients aged over 60 years, recruited from community pharmacies in Albacete, Spain. Cognitive status was assessed using the MoCA (cut-offs < 26 and < 21), the Short Portable Mental Status Questionnaire, the Memory Impairment Screen, and the Semantic Verbal Fluency Test (animals). Comorbidities were assessed using active medication prescriptions as proxy variables. Cohen’s Kappa coefficients were computed to assess concordance, and a binary logistic regression was performed to identify independent predictors of cognitive impairment, defined as a MoCA score < 21. Results The estimated prevalence of cognitive impairment varied from 65.1% using the highest MoCA cut-off (< 26) to 18.1 using the more conservative MoCA < 21 threshold. Concordance analysis revealed low agreement between MoCA < 26 and the other instruments (Kappa < 0.008). However, using the MoCA < 21 cut-off, the observed agreement improved substantially to over 84% (all Kappa values statistically significant at p < 0.001). The adjusted binary logistic regression model demonstrated that older age (OR = 1.168, p < 0.001) and the diagnosis of hypercholesterolemia (OR = 3.558, p = 0.018) significantly increased the odds of cognitive impairment, whereas higher cognitive reserve was a protective factor (OR = 0.807, p < 0.001) Conclusions The estimated prevalence of suspected cognitive impairment is highly dependent on the screening instrument and threshold selected. The findings support the adoption of a more conservative MoCA cut-off < 21 to improve concordance with other brief instruments and reduce false positives in this population. Additionally, the independent association between hypercholesterolemia and lower cognitive performance highlights the importance of integrated preventive strategies in primary care, combining sensitive cognitive screening with cardiovascular risk management. Cognitive impairment older adults community-dwellers neuropsychological tests screening prevalence hypercholesterolemia community pharmacy Figures Figure 1 Figure 2 Background Cognitive impairment is a major public health concern due to its impact on functional independence, risk and progression to dementia, affecting quality of life and increasing healthcare needs and costs[1]. Early detection of cognitive decline is essential to implement preventive interventions and optimize care planning[2]. However, the selection of cognitive screening tools remains a matter of debate, as different instruments target partially overlapping cognitive domains and apply variable thresholds for impairment [3,4]. After decades of using the Mini-Mental State Examination as the golden standard, the Montreal Cognitive Assessment (MoCA) has been widely adopted as a screening tool with high sensitivity for mild cognitive impairment, encompassing a broad range of domains including memory, executive function (not assessed in Mini-Mental State Examination), attention, language, and visuospatial abilities [3,5]. In contrast, brief instruments such as the Short Portable Mental Status Questionnaire (SPMSQ), the Memory Impairment Screen (MIS), and semantic verbal fluency tasks (SVF) are more targeted in scope, often emphasizing orientation, episodic memory, or lexical access [2]. The cut-off point used to define impairment also substantially influences prevalence estimates, with studies in Spanish populations recommending lower thresholds (e.g. MoCA < 21, <23) to improve specificity and reduce false positives [4,6–9] Importantly, the diagnostic consistency among these tools has been insufficiently characterized in community-dwelling older adults. Understanding the degree of agreement and the potential determinants of test performance is critical to guide screening strategies in primary care and research settings. In addition to the selection of cognitive screening tools, growing evidence highlights the relevance of addressing modifiable risk factors throughout the life course. The Lancet Commission on Dementia Prevention, Intervention, and Care has identified 14 potentially modifiable risk factors that could prevent or delay nearly half of dementia cases worldwide [10]. These span from ensuring access to quality education and cognitively stimulating activities in early and midlife, to managing hearing and vision loss, and effectively treat depression. Lifestyle-related interventions, such as encouraging regular physical activity, reducing toxic habits as smoking and alcohol consumption and promoting social participation, are also essential in reducing vulnerability to cognitive decline. Crucially, a cluster of metabolic and cardiovascular risk factors—including hypertension, dyslipidemia, diabetes and obesity—has been consistently associated with accelerated cognitive aging and higher dementia risk [11–14]. Among these, detecting and treating elevated LDL-cholesterol from midlife, maintaining systolic blood pressure ≤ 130 mmHg, and preventing obesity play a pivotal role in preserving cognitive health. Addressing these metabolic conditions is particularly relevant in community-dwelling older adults, where multimorbidity and polypharmacy are prevalent, and early detection may offer opportunities for integrated interventions combining cardiovascular risk management with cognitive screening strategies [10–12]. Study Objectives The objective of this study was to evaluate the concordance between the MoCA (using two alternative cut-offs) and three other validated tools when screening for cognitive impairment, and to analyze the factors associated with lower cognitive performance, including demographic variables, cognitive reserve, functional independence and comorbidities, amongst others [9,15,16]. Special attention was paid to the role of metabolic conditions, such as hypercholesterolemia, diabetes and hypertension, which have been implicated in cognitive aging [11,12,14]. Materials and methods Study design and participants A cross-sectional study was conducted including community-dwelling patients older than 60 years of age, recruited from community pharmacies in the province of Albacete, Spain. Inclusion criteria were: (a) age older than 60; (b) non-institutionalized; (c) absence of significant functional or sensorial impairment, or any major difficulty to complete the interview; (d) Spanish-native speaker. Exclusion criteria were: (a) presence of sensorial or functional impairment or any major difficulty to complete the interview; (b) history of stroke, neurological, or psychiatric conditions that could interfere on cognitive performance. The minimum required sample size was estimated based on an expected prevalence of cognitive impairment of 11.6%, as reported in the DERIVA study [17]. Using a 95% confidence level, the calculated minimum sample size was 158 participants. A total of 231 participants were initially recruited for the study; however, only 166 met all inclusion criteria and completed the full assessment protocol. Human Ethics and Consent to Participate: The research protocol was approved by the Ethics Committee of the Albacete Integrated Care Management System (approval reference: 2023 − 155) and was conducted in accordance with the Declaration of Helsinki. All participants voluntarily enrolled the study and signed written informed consent. Measures Participants were invited to attend an individual interview with a trained pharmacist for a duration of one hour and 30 minutes with the purpose of collecting all the relevant information. Sociodemographic variables The following variables were obtained: age, sex and rural or urban residence by postal code. Cognitive assessment Cognitive status was evaluated using four validated neuropsychological tests: - Spanish version of the Montreal Cognitive Assessment (MoCA) Global cognitive screening tool assessing all cognitive domains (0–30). Two cut-off points were used to define suspected cognitive impairment: <26, as proposed in the original publication, and < 21, following recommendations by Lozano-Gallego et al. [18] and other studies to improve specificity in older populations[8,9]. - Short Portable Mental Status Questionnaire (SPMSQ) of Pfeiffer A brief screening instrument with 10 items assessing orientation, memory and calculation. Scores ≥ 3 errors were considered suggestive of possible cognitive impairment[19,20]. - Memory Impairment Screen (MIS) Test of episodic memory consisting in a 4-item delayed free and cued recall (0–8). Scores ≤ 4 were considered suggestive memory impairment [21,22]. - Semantic Verbal Fluency Test (SVF) – Animals Participants were asked to name as many animals as possible in 60 seconds. A score below 10 was considered indicative of impairment in Spanish older adults[23] . A cognitive reserve questionnaire (Cuestionario de Reserva Cognitiva, Rami et al ., 2011) was also applied, including items related to educational level, occupational complexity and cognitive leisure activities (global score range: 0–25)[24–26]. Two binary variables were created for MoCA classification (< 26 and < 21), and cross-tabulations were performed to compare prevalence and agreement across the instruments. Cohen’s Kappa coefficients were computed to assess concordance between MoCA and the other tests under each cut-off criterion. A bivariate correlation analysis was also conducted to evaluate the relationships among the total scores obtained on the different cognitive screening tests using Pearson’s correlation coefficient. Statistical significance was set at p < 0.005. Two-tailed tests were applied. Medication review and comorbidity assessment Medication data were collected through structured interview and review of medical prescriptions. For each participant, the following were recorded: i) active principles (ATC code); ii) daily dose; iii) number of concurrent medications; iv) polypharmacy status (defined as ≥ 5 active principles). This information also gave us the comorbidities of the patients (hypertension, diabetes mellitus, dyslipidemia). Medications were used as proxy variables for comorbidities, that is that diagnosis of a pathology was assumed if the participant was taking medications intended specifically to treat that disease. For example, dyslipidemia was assumed if the patient was taking any kind of lipid-lowering therapy. Functional independence Independence in basic and instrumental activities of daily living was assessed with: Barthel Index (0-100); and Lawton and Brody Index (0–8), higher scores indicating independence in both basic and instrumental activities. Statistical analysis In addition to descriptive statistics and concordance analyses, further post-hoc comparisons were performed to explore associations between cognitive impairment and participant characteristics. Categorical variables (sex, rural, chronic conditions, polypharmacy) were compared using chi-square tests and Odds Ratios (OR) with 95% confidence intervals (CI) were calculated when applicable. Pearson correlation coefficients were calculated to explore the relationship between age, cognitive reserve, number of medications, independence in basic and instrumental activities of daily living (Barthel Index and Lawton and Brody Scale) and cognitive test scores. To identify predictors of cognitive impairment, binary logistic regression analysis was conducted including sociodemographic, clinical, and functional variables as potential covariates. The dependent variable was cognitive impairment, defined as a MoCA score < 21. Variables with p < 0.10 in univariate analysis were considered for inclusion in the multivariate model. Results were reported as OR with 95% CI. Linear regression analyses were also performed using the different neuropsychological test’s total score as the dependent variables, to further explore associations with potential predictors (p < 0.10 in univariate analysis) and cognitive performance between tests. Separate regression models were estimated for each cognitive test. All statistical analyses were performed using IBM SPSS Statistics v29 (IBM Corp.), with statistical significance set at p < 0.05 (two-tailed). Results Sample characteristics A total of 166 patients participated in the study with a mean age of 72.57 ± 5.836, and 60.8% were women (101). The most frequent comorbidity was hypercholesterolemia (57.2%), followed by hypertension (46.4%). Nearly half of participants (48.8%) met criteria for polypharmacy (≥5 medications). Cognitive reserve averaged 12.49 ± 4.438 points, and most participants were functionally independent (mean Barthel Index 98.64 ± 3.627; Lawton and Brody 7.61 ± 0.850). Full descriptive characteristics of the sample are shown in Table 1. Table 1. Main characteristics of the sample. Number of subjects N 166 Age (Mean ± SD) 72.57 ± 5.836 Sex/gender (Female) N (%) 101 (60.8%) Rural N (%) 11 (6.6%) Comorbidities N (%) Hypertension Hypercholesterolemia Type 2 Diabetes Mellitus Hypertriglyceridemia 77 (46.4%) 95 (57.2%) 30 (18.1%) 3 (1.8%) Medication N (%) Antidepressants (N06A) Anxiolytics, Hypnotics (N05B, N05C) Dementia (Donepezile) 19 (11.4%) 27 (16.3%) 2 (1.2%) Cognitive Test (Mean ± SD) SPMSQ MIS SVF (Animals) MoCA 0.93 ± 1.115 7.31 ± 1.149 18.61 ± 5.906 23.48 ± 5.035 Cognitive Reserve 12.49 ± 4.438 Number of active principles Polypharmacy N (%) 5.22 ± 3.885 81 (48.8%) Independence Barthel Index Lawton and Brody 98.64 ± 3.627 7.61 ± 0.850 MIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment; SPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency . Cognitive impairment assessment: prevalence, concordance and correlation Regarding cognitive assessment, the average score in the Montreal Cognitive Assessment (MoCA) was 23.48 ± 5.035 (Table 1). When applying a cut-off of <26, 108 participants (65.1%) screened positive for cognitive impairment. This percentage dropped to 18.1% using the more conservative cut-off of <21 (Table 2). The prevalence of cognitive impairment varied substantially across screening tools: 6.6% for Short Portable Mental Status Questionnaire (SPMSQ), 4.8% for Memory Impairment Screen (MIS), and 4.2% for Semantic Verbal Fluency (SVF), compared to a 65.1% with MoCA (cut-off score of <26) (Table 2). Table 2. Prevalence of CI with different tests and cut-off points. Instrument Threshold N (%) positive MoCA <26 108 (65.1%) <21 30 (18.1%) MIS ≤4 8 (4.8%) SPMSQ ≥3 11 (6.6%) SVF <10 7 (4.2%) MIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment; SPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency . Concordance analysis (Table 3) revealed low agreement between MoCA cut-off score <26 and the other tests (observed agreement between 38.0% and 41.6%, all Kappa<0.08). These comparisons were not statistically significant except for MoCA<26 vs. SPMSQ (p=0.012). However, when using MoCA84% and statistically significant Kappa values (p<0.001 in all cases). Table 3. Concordance analysis between MoCA and alternative cognitive screening tests (MIS, SPMSQ, SVF) Comparison Positive cases (%) Negative cases (%) Observed agreement (%) Kappa (95%) p-value MoCA<26 vs. MIS vs. SPMSQ vs. SVF 108 (65.1%) 8 (4.8%) 11 (6.6%) 7 (4.2%) 58 (34.9%) 158 (95.2%) 155 (93.4%) 159 (95.8%) 64 (38.6%) 69 (41.6%) 63 (38.0%) 0.034 0.073 0.027 0.172 0.012* 0.242 MoCA<21 vs. MIS vs. SPMSQ vs. SVF 30 (18.1%) 8 (4.8%) 11 (6.6%) 7 (4.2%) 136 (81.9%) 158 (95.2%) 155 (93.4%) 159 (95.8%) 140 (84.3%) 143 (86.1%) 141 (84.9%) 0.259 0.379 0.275 <0.001* <0.001* <0.001* MIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment; SPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency . The correlation analysis demonstrated statistically significant associations among all cognitive tests evaluated (Appendix 1). The total MoCA score showed a moderate negative correlation with the SPMSQ (r = -0.419; p < 0.001), indicating that higher MoCA scores were associated with fewer errors on the SPMSQ. Furthermore, MoCA scores were positively correlated with the MIS (r = 0.328; p < 0.001) and the SVF (r = 0.404; p < 0.001), suggesting that better global cognitive performance was related to greater recall and verbal fluency. The SPMSQ was negatively correlated with the MIS (r = -0.380; p < 0.001) and the SVF (r = -0.373; p < 0.001). Finally, a positive correlation was also observed between MIS and SVF scores (r = 0.247; p = 0.001). Patient characteristics affecting test results: Associations between sociodemographic and clinical variables of patient characteristics and cognitive impairment, as assessed by each test, are presented in Table 4. For categorical variables, chi-square tests were performed using dichotomized cognitive impairment outcomes. For continuous variables, Pearson’s correlation coefficients were calculated with the total test scores. Older age was consistently associated with poorer cognitive performance across tests, with significant negative correlations with MoCA (r = -0.331, p<0.001), MIS (r = -0.246, p<0.001), and SVF (r = -0.351, p<0.001), and a positive correlation with SPMSQ errors (r = 0.245, p <0.001). Female sex was more frequent among participants classified as cognitively impaired by the MoCA<21 cut-off (70.0%), the SVF (71.4%) and the MIS (75.0%), although chi-square tests did not reach statistical significance for these comparisons. Regarding comorbidities, the presence of hypercholesterolemia was significantly associated with MoCA impairment with both cut-offs (cut-off <26: chi 2 = 9.149, p = 0.002 and cut-off <21: chi 2 = 5.652, p = 0.017), and also with impairment identified by the SVF (chi 2 = 5.462, p = 0.019). Hypertension was associated with SVF impairment (chi 2 = 4.545, p = 0.033), but no significant associations were observed for diabetes or depression. Cognitive reserve showed positive correlations with MoCA (r=0.282, p <0.001), MIS (r=0.174, p = 0.025), and SVF (r=0.350, p <0.001), and a negative correlation with SPMSQ errors (r=-0.248, p < 0.001). Regarding functional measures, higher independence in basic activities of daily living, measured by the Barthel Index, was associated with better performance in the MIS (r = 0.226, p = 0.003) and fewer errors in the SPMSQ (r =-0.212, p = 0.006). No significant associations were found between the Lawton and Brody scale and cognitive tests scores. No significant associations were observed between polypharmacy and dichotomized cognitive impairment in any of the tools, although the number of chronic medications showed a weak negative correlation with MIS performance (r =-0.204, p = 0.008). Table 4. Associations between patient characteristics and cognitive impairment according to different screening tools. Variables N=166 MoCA MIS PC = 8 SVF PC = 7 SPMSQ PC = 11 Cut-off <26 PC = 108 Cut-off<21 PC = 30 Female N (%) Chi 2 61 (56.5%) 2.469 21 (70.0%) 1.289 6 (75.0%) 0.707 5 (71.4%) 0.344 3 (27.3%) 0.698 Age Pearson’s r -0.331*** -0.246*** -0.351*** 0.245*** Rural N (%) Chi 2 8 (7.4%) 0.305 2 (6.7%) 0.000 0 (0.0%) 0.596 1 (14.3%) 0.693 0 (0.0%) 0.836 Hypertension N (%) Chi 2 55 (50.9%) 2.562 17 (56.7%) 1.556 5 (62.5%) 0.878 6 (85.7%) 4.545* 7 (63.6%) 1.410 Hypercholesterolemia N (%) Chi 2 71 (65.7%) 9.149** 23 (76.7%) 5.652* 4 (50.0%) 0.179 7 (100%) 5.462* 9 (81.1%) 2.910 Diabetes N (%) Chi 2 20 (18.5%) 0.042 7 (23.3%) 0.685 3 (37.5%) 2.143 2 (28.6%) 0.544 2 (18.2%) 0.000 Depression N (%) Chi 2 13 (12.0%) 0.107 1 (3.3%) 2.378 1 (12.5%) 0.009 0 (0.0%) 0.945 0 (0.0%) 1.523 Polypharmacy N (%) Chi 2 Number of AP Pearson’s r 55 (50.9%) 0.562 16 (53.3%) 0.302 5 (62.5%) 0.632 -0.204** 4 (57.1%) 0.204 -0.012 7 (63.6%) 1.039 0.086 -0.035 Cognitive reserve Pearson’s r 0.282*** 0.174* 0.350*** -0.248*** Barthel Index Pearson’s r Lawton & Brody Pearson’s r 0.085 -0.90 0.226** 0.081 0.114 -0.082 -0.212** 0.009 MIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment; PC: positive cases; SPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency . *: p<0.05; **: p<0.01; ***: p<0.001. Regression models A binary logistic regression analysis was conducted to examine the association between cognitive impairment (MoCA<21) and potential predictors, including age, cognitive reserve, and diagnosis of hypercholesterolemia (p<0.1 in univariate analysis). The logistic regression model was statistically significant, which supports the influence of the predictors on the likelihood of exhibiting cognitive impairment. The model’s constant was B =-11.436 (SE=3.298, p < 0.001). Age was positively associated with cognitive impairment (B = 0.155, SE = 0.042, Wald= 13.42, p<0.001), corresponding to an OR of 1.168. This indicates that each additional year of age increased the odds of cognitive impairment by 16.8%. Cognitive reserve was inversely associated with cognitive impairment (B =-0.214, SE = 0.061, Wald = 12.51, p<0.001), with an OR of 0.807, suggesting that higher cognitive reserve reduced the odds by approximately 19.3% per unit increase. Importantly, the diagnosis of hypercholesterolemia remained a significant independent predictor after adjusting for age and cognitive reserve (B = 1.269, SE = 0.538, Wald = 5.56, p=0.018), corresponding to an OR of 3.558. This indicates that participants with hypercholesterolemia had more than three times the odds of cognitive impairment compared to those without this diagnosis. A forest plot was generated to illustrate the effect sizes of each predictor (Figure 1). The odds ratios (OR) and 95% confidence intervals (CI) were as follows: age (OR=1.168, 95% CI: 1.075–1.271), cognitive reserve (OR=0.807, 95% CI: 0.714–0.911), and hypercholesterolemia (OR=3.558, 95% CI: 1.241–10.202). These results indicate that higher age and the presence of hypercholesterolemia may significantly increased the odds of cognitive impairment, whereas higher cognitive reserve was a protective factor. Separate linear regression models were estimated to examine the predictors of performance in each cognitive test (Table 5). Standardized regression coefficients for predictors across cognitive tests are showed in Figure 2. Montreal Cognitive Assessment (MoCA) For the MoCA total score, older age was significantly associated with lower scores (B=-0.260, p < 0.001), while higher cognitive reserve was associated with higher scores (B=+0.265, p = 0.001). Hypercholesterolemia was also significantly associated with a decrease of approximately 1.75 points in MoCA scores (B =-1.554, p=0.032). The model explained 17% of the variance (adjusted R²=0.177). Short Portable Mental Status Questionnaire (SPMSQ) In the SPMSQ model, older age (B=+0.038, p=0.009), and lower cognitive reserve (B=-0.047, p=0.016) were significantly associated with higher error scores, indicating worse cognitive performance. Hypercholesterolemia (B=+0.130, p= 0.435) and lower functional independence as measured by the Barthel Index (B=+0.039, p=0.098), showed a nonsignificant association. The model accounted for 10.5% of the variance (adjusted R² = 0.105). Memory Impairment Screen (MIS) For the MIS total score, age (B=-0.038, p= 0.012) and the total number of chronic active pharmaceutical ingredients (B=-0.047, p= 0.036) were significant predictors, while Barthel Index (B=0.044, p= 0.078) cognitive reserve (B=+0.029, p= 0.148) showed nonsignificant associations. The model explained 10.8% of the variance (adjusted R² =0.108). Semantic Verbal Fluency (SVF) In the SVF model, higher age (B=-0.311, p<0.001) and lower cognitive reserve (B= 0.403, p<0.001) were associated with lower verbal fluency scores, The presence of hypercholesterolemia (B=-1.272, p=0.132) and hypertension (B=-0.295, p= 0.732) showed a nonsignificant association. The overall model accounted for 23.3% of the variance (adjusted R² = 0.213). Table 5. Linear regression models for each cognitive test performance Test Predictor B (SE) p-value Adj R² MoCA Age -0.260 <0.001 0.177 Cognitive Reserve +0.265 0.001 Hypercholesterolemia -1.554 0.032 SPMSQ Age +0.038 0.009 0.105 Cognitive Reserve -0.047 0.016 Barthel Index -0.039 0.098 Hypercholesterolemia +0.130 0.435 MIS Age -0.038 0.012 0.108 No. of Active Drugs -0.047 0.036 Barthel Index +0.044 0.078 Cognitive Reserve +0.029 0.148 SVF Age -0.311 <0.001 0.213 Cognitive Reserve +0.403 <0.001 Hypercholesterolemia -1.272 0.132 Hypertension -0.295 0.732 Adj R 2 : adjusted R 2; B: regression constant; MIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment; SPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency ; SE: standard error. Discussion This study demonstrates that the estimated prevalence of suspected cognitive impairment in community-dwelling older adults varies substantially depending on the screening instrument and the cut-off applied. Using the conventional Montreal Cognitive Assessment (MoCA) threshold of < 26, nearly two-thirds of participants screened positive for cognitive impairment. In contrast, the prevalence dropped to 18% with the more stringent cut-off of < 21, and fell below 7% when assessed with Short Portable Mental Status Questionnaire (SPMSQ), Memory Impairment Screen (MIS) or Semantic Verbal Fluency (SVF). These findings highlight the critical influence of methodological choices on prevalence estimates, and align with previous reports indicating that the MoCA’s sensitivity is achieved partly at the cost of reduced specificity in older populations, with MoCA < 26 likely overidentifying impairment in this population[3,8,9,18]. The concordance analysis revealed that MoCA cut-off < 26 showed poor agreement with other instruments (Kappa < 0.008), suggesting that this threshold may overidentify impairment relative to briefer tools. However, when applying MoCA 84% agreement, all Kappa values significant). This pattern supports the argument that a cut-off below 21 is more appropriate in Spanish community-dwelling older adults, as it improves the balance between sensitivity and specificity and reduces misclassification [9,18,27]. The correlation matrix underscored that while instruments share some variance (moderate correlation), each assesses partially distinct cognitive domains. The MoCA, with is broader scope, captures deficits in executive functioning and visuospatial processing that the SPMSQ and MIS do not systematically evaluate. This is consistent with evidence indicating that verbal fluency and executive dysfunction often precede memory complaints in prodromal dementia[28–31]. Notably, semantic fluency correlated positively with MoCA total scores, reinforcing the contribution of lexical access and executive retrieval processes to global cognitive performance. Beyond tests-results discordance, our analyses identified consistent factors associated with cognitive performance. Age emerged as a consistent negative predictor across all tests, confirming the pervasive effect of aging on multiple cognitive domains. Higher cognitive reserve also showed a protective association, particularly in the MoCA and SVF, in line with previous consolidated evidence that education and cognitively stimulating activities buffer against decline [25]. Importantly, the presence of hypercholesterolemia (using lipid-lowering medication use as a proxy variable for the disease) was independently associated with lower MoCA scores even after adjustment for age and cognitive reserve. This association suggest a potential vascular-metabolic contribution to global cognitive decline in this cohort, in line with longitudinal studies linking dyslipidemia with cognitive deterioration and increasing dementia risk [14]. Interestingly, the impact of hypercholesterolemia was most evident in the MoCA, but not consistently significant across the other instruments. This could reflect the greater sensitivity of the MoCA to detect executive function and attention deficits, commonly related to cardiovascular pathology [32]. This result reinforces the need to integrate cardiovascular risk management into strategies for cognitive screening and prevention. From a clinical perspective, our findings indicate that relying exclusively on a single screening test, particularly one with a highly sensitive threshold such as MoCA < 26, may lead to substantial overestimation of cognitive impairment prevalence. At the same time, very brief instruments such as the SPMSQ or MIS may fail to detect early executive dysfunction or mild cognitive decline. In clinical and research settings, where the aim is to balance sensitivity and specificity, applying a MoCA cut-off below 21 appears to offer a more conservative and valid approach. Nevertheless, it cannot be denied that cognitive decline may begin up to 20 years before symptoms become noticeable, so applying a stricter cut-off could help identify patients at risk and monitor their progression [33]. These results have implications for the design of screening protocols in primary care and for the interpretations of epidemiological data on cognitive impairment prevalence. Furthermore, they highlight the need for comprehensive cognitive profiling that considers multiple domains and adjusts for individual risk factors such as metabolic comorbidities and cognitive reserve. Strengths and limitations The study benefits from a well-characterized community sample and the simultaneous administration of multiple validated tools. Nevertheless, limitations should be acknowledged: the cross-sectional design precludes casual inference; the sample size, while adequate for prevalence estimates, may limit the power of multivariate models; and the absence of a reference standard diagnosis (e.g., clinical neuropsychological assessment) prevents to formally establish sensitivity and specificity. Future research should adopt longitudinal designs, incorporating biomarkers and neuroimaging that could help clarify the predictive value of different screening thresholds, and explore the cross-cultural sociodemographic and metabolic factors affecting the development of cognitive decline. Conclusion In summary, this research highlights the discordance among widely used cognitive screening instruments and emphasizes that the choice of tool and its cut-off critically determine the estimated prevalence of cognitive impairment. The findings support the adoption of a more conservative Montreal Cognitive Assessment cut-off (< 21) to improve agreement with other brief instruments and reduce false positives in Spanish older adults. Additionally, the observed association between hypercholesterolemia and lower cognitive performance reinforces the relevance of vascular-metabolic health in cognitive aging and highlights the importance of integrated preventive screening strategies in primary care and community settings, combining sensitive cognitive tools with cardiovascular risk management. Abbreviations B Regression coefficient CI Cognitive Impairment MIS Memory Impairment Screen MoCA Montreal Cognitive Assessment SD Standard Deviation SE Standard Error SPMSQ Short Portable Mental Status Questionnaire SVF Semantic Verbal Fluency Declarations Acknowledgments We are deeply grateful to Farmacia Ana Rubio and Farmacia Cid 51 for their invaluable cooperation in the recruitment and assessment of participants. We also extend our thanks to the Colegio Oficial de Farmacéuticos de Albacete for its collaboration and the participants themselves, whose willingness to take part made this research posible. Funding This work was supported by the Excelentísima Diputación de Albacete with the award of a research grant from the Juan Carlos Izpisúa Belmonte program . Ethics Approval and Consent to Participate The research protocol was approved by the Ethics Committee of the Albacete Integrated Care Management System (approval reference: 2023-155) and was conducted in accordance with the Declaration of Helsinki. All participants voluntarily enrolled the study and signed written informed consent. Consent for publication Not applicable. Competing Interests None declared. Authors’ contribution All authors made a substantial contribution to the manuscript and agree with the final published version. LSG and LAML were responsible for the study conceptualization and design. The methodology was developed by LSG, including the statistical analysis strategy. LSG and CGG conducted the investigation and data acquisition, being responsible for participant recruitment and test administration. Data curation and formal statistical analysis (concordance and regression models) were primarily the responsibility of LSG. LCV, GBA and JACdL provided project supervision, resource management, and funding acquisition. LSG wrote the original draft preparation, while LCV and LAML performed the critical review and editing of the manuscript for intellectual content and clarity. All authors have read and agreed to the published version of the manuscript. Availability of Data and Materials Data are available upon reasonable request. References Petersen RC. Mild Cognitive Impairment. Continuum (N Y) 2016;22:404–18. https://doi.org/10.1212/CON.0000000000000313. Alzola P, Carnero C, Bermejo-Pareja F, Sánchez-Benavides G, Peña-Casanova J, Puertas-Martín V, et al. Neuropsychological Assessment for Early Detection and Diagnosis of Dementia: Current Knowledge and New Insights. J Clin Med 2024;13:3442. https://doi.org/10.3390/jcm13123442. Valles-Salgado M, Matias-Guiu JA, Delgado-Álvarez A, Delgado-Alonso C, Gil-Moreno MJ, Valiente-Gordillo E, et al. Comparison of the Diagnostic Accuracy of Five Cognitive Screening Tests for Diagnosing Mild Cognitive Impairment in Patients Consulting for Memory Loss. J Clin Med 2024;13. https://doi.org/10.3390/jcm13164695. Chun CT, Seward K, Patterson A, Melton A, MacDonald-Wicks L. Evaluation of Available Cognitive Tools Used to Measure Mild Cognitive Decline: A Scoping Review. Nutrients 2021;13:3974. https://doi.org/10.3390/nu13113974. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005;53:695–9. https://doi.org/10.1111/j.1532-5415.2005.53221.x. Bulgarelli L, Gyr E, Villanueva J, Mejía K, Mejía C, Paredes R, et al. Normative data of the Spanish version of the Montreal Cognitive Assessment (MoCA) in older individuals from Peru. ACTA Paulista de Enfermagem 2025;19. https://doi.org/10.1590/1980-5764-dn-2024-0261. Rossetti HC, Lacritz LH, Cullum CM, Weiner MF. Normative data for the Montreal Cognitive Assessment (MoCA) in a population-based sample. Neurology 2011;77:1272–5. https://doi.org/10.1212/WNL.0b013e318230208a. Carson N, Leach L, Murphy KJ. A re‐examination of Montreal Cognitive Assessment (MoCA) cutoff scores. Int J Geriatr Psychiatry 2018;33:379–88. https://doi.org/10.1002/gps.4756. Delgado C, Araneda A, Behrens MI. Validación del instrumento Montreal Cognitive Assessment en español en adultos mayores de 60 años. Neurología 2019;34:376–85. https://doi.org/10.1016/j.nrl.2017.01.013. Livingston G, Huntley J, Liu KY, Costafreda SG, Selbæk G, Alladi S, et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. The Lancet 2024;404:572–628. https://doi.org/10.1016/S0140-6736(24)01296-0. Solomon A, Kivipelto M, Wolozin B, Zhou J, Whitmer RA. Midlife Serum Cholesterol and Increased Risk of Alzheimer’s and Vascular Dementia Three Decades Later. Dement Geriatr Cogn Disord 2009;28:75–80. https://doi.org/10.1159/000231980. Whitmer RA, Sidney S, Selby J, Johnston SC, Yaffe K. Midlife cardiovascular risk factors and risk of dementia in late life. Neurology 2005;64:277–81. https://doi.org/10.1212/01.WNL.0000149519.47454.F2. Deckers K, Cadar D, van Boxtel MPJ, Verhey FRJ, Steptoe A, Köhler S. Modifiable Risk Factors Explain Socioeconomic Inequalities in Dementia Risk: Evidence from a Population-Based Prospective Cohort Study. Journal of Alzheimer’s Disease 2019;71:549–57. https://doi.org/10.3233/JAD-190541. Anstey KJ, Ee N, Eramudugolla R, Jagger C, Peters R. A Systematic Review of Meta-Analyses that Evaluate Risk Factors for Dementia to Evaluate the Quantity, Quality, and Global Representativeness of Evidence. J Alzheimer’s Dis 2019;70:S165–86. https://doi.org/10.3233/JAD-190181. García C, Moreno L, Alacreu M, Muñoz FJ, Martínez LA. Addressing Psychosocial Factors in Cognitive Impairment Screening from a Holistic Perspective: The DeCo-Booklet Methodology Design and Pilot Study. Int J Environ Res Public Health 2022;19:12911. https://doi.org/10.3390/ijerph191912911. Ojeda N, del Pino R, Ibarretxe-Bilbao N, Schretlen DJ, Peña D. Test de evaluación cognitiva de Montreal: normalización y estandarización de la prueba en población española. Rev Neurol 2016:488–96. PMID: 27874165 Bermejo-Pareja F, Benito-León J, Vega-Q S, Díaz-Guzmán J, Rivera-Navarro J, Molina JA, et al. [The NEDICES cohort of the elderly. Methodology and main neurological findings]. Rev Neurol n.d.;46:416–23. PMID: 18389461 Lozano Gallego M, Hernández Ferrándiz M, Turró Garriga O, Pericot Nierga I, López-Pausa S, Vilalta Franch J. Validación del Montreal Cognitive Assessment (MoCA): test de cribado para el deterioro cognitivo leve. Datos preliminares. Alzheimer Real Invest Demenc 2009;43:4–11. Pfeiffer E. A Short Portable Mental Status Questionnaire for the Assessment of Organic Brain Deficit in Elderly Patients†. J Am Geriatr Soc 1975;23:433–41. https://doi.org/10.1111/j.1532-5415.1975.tb00927.x. Gornemann I, Zunzunegui MV, Martı́nez C, del Carmen Onı́s M. Screening for impaired cognitive function among the elderly in Spain: reducing the number of items in the Short Portable Mental Status Questionnaire. Psychiatry Res 1999;89:133–45. https://doi.org/10.1016/S0165-1781(99)00089-X. Modrego PJ, Gazulla J. The Predictive Value of the Memory Impairment Screen in Patients With Subjective Memory Complaints. Prim Care Companion CNS Disord 2013. https://doi.org/10.4088/PCC.12m01435. Buschke H, Kuslansky G, Katz M, Stewart WF, Sliwinski MJ, Eckholdt HM, et al. Screening for dementia with the Memory Impairment Screen. Neurology 1999;52:231–231. https://doi.org/10.1212/WNL.52.2.231. Carnero Pardo C, Lendínez González A. Utilidad del test de fluencia verbal semántica en el diagnóstico de demencia. Rev Neurol 1999;29:709. https://doi.org/10.33588/rn.2908.99233. Rami L, Valls-Pedret C, Bartrés-Faz D, Caprile C, Solé-Padullés C, Castellvi M, et al. Cognitive reserve questionnaire. Scores obtained in a healthy elderly population and in one with Alzheimer’s disease. Rev Neurol 2011;52:195–201. PMID: 21312165 Stern Y. Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurol 2012;11:1006–12. https://doi.org/10.1016/S1474-4422(12)70191-6. Martino P, Caycho Rodríguez T, Valencia PD, Politis D, Gallegos M, De Bortoli MÁ, et al. Cuestionario de reserva cognitiva: análisis psicométrico desde la teoría de respuesta al ítem. Rev Neurol 2022;75:173. https://doi.org/10.33588/rn.7507.2022113. Elkana O, Tal N, Oren N, Soffer S, Ash EL. Is the Cutoff of the MoCA too High? Longitudinal Data From Highly Educated Older Adults. J Geriatr Psychiatry Neurol 2020;33:155–60. https://doi.org/10.1177/0891988719874121. Tierney MC, Yao C, Kiss A, McDowell I. Neuropsychological tests accurately predict incident Alzheimer disease after 5 and 10 years. Neurology 2005;64:1853–9. https://doi.org/10.1212/01.WNL.0000163773.21794.0B. Liampas I, Folia V, Zoupa E, Siokas V, Yannakoulia M, Sakka P, et al. Qualitative Verbal Fluency Components as Prognostic Factors for Developing Alzheimer’s Dementia and Mild Cognitive Impairment: Results from the Population-Based HELIAD Cohort. Medicina (Kaunas) 2022;58. https://doi.org/10.3390/medicina58121814. Clark LR, Schiehser DM, Weissberger GH, Salmon DP, Delis DC, Bondi MW. Specific measures of executive function predict cognitive decline in older adults. J Int Neuropsychol Soc 2012;18:118–27. https://doi.org/10.1017/S1355617711001524. Reinvang I, Grambaite R, Espeseth T. Executive Dysfunction in MCI: Subtype or Early Symptom. Int J Alzheimers Dis 2012;2012:936272. https://doi.org/10.1155/2012/936272. Hayes CA, Young CB, Abdelnour C, Reeves A, Odden MC, Nirschl J, et al. The impact of arteriolosclerosis on cognitive impairment in decedents without severe dementia from the National Alzheimer’s Coordinating Center. Alzheimer’s & Dementia 2025;21. https://doi.org/10.1002/alz.70059. Caselli RJ, Langlais BT, Dueck AC, Chen Y, Su Y, Locke DEC, et al. Neuropsychological decline up to 20 years before incident mild cognitive impairment. Alzheimers Dement 2020;16:512–23. https://doi.org/10.1016/j.jalz.2019.09.085. Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx Cite Share Download PDF Status: Published Journal Publication published 09 Apr, 2026 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 18 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers invited by journal 23 Jan, 2026 Editor invited by journal 05 Jan, 2026 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 18 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8143343","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580440012,"identity":"6cce1ba2-cebe-48c6-8533-0775e1895f99","order_by":0,"name":"Lucía Sáez-González","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACPiA+AGEyA+kCIrSwIbSwJTAwGBCpBQp4DIjUIpH88MAPhjp5fv4z3yR+GGxj4Oc/QEhLmsHBHobDhjMbzm6T7DG4zSA5I4GQlhyGAzwMBxg3HOzdJs0A1GJwg6DDchgO/mGos99/mOcZWIv9eYIOy2E4zMPAnLiBjYcNYgsDIYfxPDM4LGNwOHnGGTZjS6BfeCRuENDCz578+OObijrb/v7DD2/8qLgtx99PwGEQgBQdPMSoHwWjYBSMglFAAAAAdPM8XKVq3noAAAAASUVORK5CYII=","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":true,"prefix":"","firstName":"Lucía","middleName":"","lastName":"Sáez-González","suffix":""},{"id":580440013,"identity":"997267e1-6fb0-4aba-862a-5d623150136e","order_by":1,"name":"Luis Antonio Martínez-López","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"Antonio","lastName":"Martínez-López","suffix":""},{"id":580440014,"identity":"9082de91-52ab-4184-b44b-e2f98d757127","order_by":2,"name":"Cristina García-García","email":"","orcid":"","institution":"Tiriez Community Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"García-García","suffix":""},{"id":580440015,"identity":"f8358c26-a12c-431b-a037-0148a969d098","order_by":3,"name":"Gema Blázquez-Abellán","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Gema","middleName":"","lastName":"Blázquez-Abellán","suffix":""},{"id":580440016,"identity":"e077d6ed-e9b7-423f-a415-7717ce44cce4","order_by":4,"name":"Jose Antonio Carbajal-de Lara","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Antonio Carbajal-de","lastName":"Lara","suffix":""},{"id":580440017,"identity":"f287724e-a854-406c-9d96-56f1765a67b6","order_by":5,"name":"Rosa María Martínez García","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Rosa","middleName":"María Martínez","lastName":"García","suffix":""},{"id":580440018,"identity":"22c37ebe-eeed-42b2-b619-29c57b5b9a8a","order_by":6,"name":"Lucía Castro-Vázquez","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Lucía","middleName":"","lastName":"Castro-Vázquez","suffix":""}],"badges":[],"createdAt":"2025-11-18 09:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8143343/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8143343/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-026-27246-y","type":"published","date":"2026-04-09T15:58:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101363163,"identity":"3a451725-f4df-48b4-9820-0f335da15e83","added_by":"auto","created_at":"2026-01-29 00:34:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38329,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot adjusted odds ratios for cognitive impairment\u003c/strong\u003e. Odds ratios are adjusted for age, cognitive reserve, and hypercholesterolemia. Bars represent 95% confidence intervals\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8143343/v1/4bdaf53610a4827322f13558.png"},{"id":101363164,"identity":"1514b3bf-6619-4878-969f-1ec90fecfbb8","added_by":"auto","created_at":"2026-01-29 00:34:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStandardized regression coefficients for predictors across cognitive tests. \u003c/strong\u003e\u003cem\u003eMIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment; SPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency\u003c/em\u003e. Positive values indicate an association with higher test scores (better cognitive performance), while negative values indicate an association with lower scores. For the SPMSQ, higher scores reflect more errors and worse performance. Bars represent the estimated B coefficients. Non-significant predictors are included for comparison. \u003cem\u003eCoefficients significant at p \u0026lt; 0.05 are marked with an asterisk.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8143343/v1/fdc9e4997aed08b96389d46f.png"},{"id":106809091,"identity":"6763e2b0-bbec-47b0-b313-883b7b87cd35","added_by":"auto","created_at":"2026-04-13 16:06:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1549416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8143343/v1/735cf988-d520-470f-a55d-4213108eba4a.pdf"},{"id":101363165,"identity":"6771ca08-51aa-4cf6-8d0c-3d803878901d","added_by":"auto","created_at":"2026-01-29 00:34:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17251,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8143343/v1/a7d6c0a666509234c2164b9e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying individuals at risk of cognitive decline: Cross-sectional analysis of variability in neuropsychological test scores among community-dwelling older adults","fulltext":[{"header":"Background","content":"\u003cp\u003eCognitive impairment is a major public health concern due to its impact on functional independence, risk and progression to dementia, affecting quality of life and increasing healthcare needs and costs[1]. Early detection of cognitive decline is essential to implement preventive interventions and optimize care planning[2]. However, the selection of cognitive screening tools remains a matter of debate, as different instruments target partially overlapping cognitive domains and apply variable thresholds for impairment [3,4].\u003c/p\u003e \u003cp\u003eAfter decades of using the Mini-Mental State Examination as the golden standard, the Montreal Cognitive Assessment (MoCA) has been widely adopted as a screening tool with high sensitivity for mild cognitive impairment, encompassing a broad range of domains including memory, executive function (not assessed in Mini-Mental State Examination), attention, language, and visuospatial abilities [3,5]. In contrast, brief instruments such as the Short Portable Mental Status Questionnaire (SPMSQ), the Memory Impairment Screen (MIS), and semantic verbal fluency tasks (SVF) are more targeted in scope, often emphasizing orientation, episodic memory, or lexical access [2]. The cut-off point used to define impairment also substantially influences prevalence estimates, with studies in Spanish populations recommending lower thresholds (e.g. MoCA\u0026thinsp;\u0026lt;\u0026thinsp;21, \u0026lt;23) to improve specificity and reduce false positives [4,6\u0026ndash;9]\u003c/p\u003e \u003cp\u003eImportantly, the diagnostic consistency among these tools has been insufficiently characterized in community-dwelling older adults. Understanding the degree of agreement and the potential determinants of test performance is critical to guide screening strategies in primary care and research settings.\u003c/p\u003e \u003cp\u003eIn addition to the selection of cognitive screening tools, growing evidence highlights the relevance of addressing modifiable risk factors throughout the life course. The Lancet Commission on Dementia Prevention, Intervention, and Care has identified 14 potentially modifiable risk factors that could prevent or delay nearly half of dementia cases worldwide [10]. These span from ensuring access to quality education and cognitively stimulating activities in early and midlife, to managing hearing and vision loss, and effectively treat depression. Lifestyle-related interventions, such as encouraging regular physical activity, reducing toxic habits as smoking and alcohol consumption and promoting social participation, are also essential in reducing vulnerability to cognitive decline. Crucially, a cluster of metabolic and cardiovascular risk factors\u0026mdash;including hypertension, dyslipidemia, diabetes and obesity\u0026mdash;has been consistently associated with accelerated cognitive aging and higher dementia risk [11\u0026ndash;14]. Among these, detecting and treating elevated LDL-cholesterol from midlife, maintaining systolic blood pressure\u0026thinsp;\u0026le;\u0026thinsp;130 mmHg, and preventing obesity play a pivotal role in preserving cognitive health. Addressing these metabolic conditions is particularly relevant in community-dwelling older adults, where multimorbidity and polypharmacy are prevalent, and early detection may offer opportunities for integrated interventions combining cardiovascular risk management with cognitive screening strategies [10\u0026ndash;12].\u003c/p\u003e \u003cp\u003eStudy Objectives\u003c/p\u003e \u003cp\u003eThe objective of this study was to evaluate the concordance between the MoCA (using two alternative cut-offs) and three other validated tools when screening for cognitive impairment, and to analyze the factors associated with lower cognitive performance, including demographic variables, cognitive reserve, functional independence and comorbidities, amongst others [9,15,16]. Special attention was paid to the role of metabolic conditions, such as hypercholesterolemia, diabetes and hypertension, which have been implicated in cognitive aging [11,12,14].\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy design and participants\u003c/p\u003e \u003cp\u003eA cross-sectional study was conducted including community-dwelling patients older than 60 years of age, recruited from community pharmacies in the province of Albacete, Spain.\u003c/p\u003e \u003cp\u003eInclusion criteria were: (a) age older than 60; (b) non-institutionalized; (c) absence of significant functional or sensorial impairment, or any major difficulty to complete the interview; (d) Spanish-native speaker.\u003c/p\u003e \u003cp\u003eExclusion criteria were: (a) presence of sensorial or functional impairment or any major difficulty to complete the interview; (b) history of stroke, neurological, or psychiatric conditions that could interfere on cognitive performance.\u003c/p\u003e \u003cp\u003eThe minimum required sample size was estimated based on an expected prevalence of cognitive impairment of 11.6%, as reported in the DERIVA study [17]. Using a 95% confidence level, the calculated minimum sample size was 158 participants. A total of 231 participants were initially recruited for the study; however, only 166 met all inclusion criteria and completed the full assessment protocol.\u003c/p\u003e \u003cp\u003eHuman Ethics and Consent to Participate:\u003c/p\u003e \u003cp\u003e The research protocol was approved by the Ethics Committee of the Albacete Integrated Care Management System (approval reference: 2023\u0026thinsp;\u0026minus;\u0026thinsp;155) and was conducted in accordance with the Declaration of Helsinki. All participants voluntarily enrolled the study and signed written informed consent.\u003c/p\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003cp\u003e Participants were invited to attend an individual interview with a trained pharmacist for a duration of one hour and 30 minutes with the purpose of collecting all the relevant information.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic variables\u003c/h2\u003e \u003cp\u003eThe following variables were obtained: age, sex and rural or urban residence by postal code.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCognitive assessment\u003c/h3\u003e\n\u003cp\u003eCognitive status was evaluated using four validated neuropsychological tests:\u003c/p\u003e\n\u003ch3\u003e- Spanish version of the Montreal Cognitive Assessment (MoCA)\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGlobal cognitive screening tool assessing all cognitive domains (0\u0026ndash;30). Two cut-off points were used to define suspected cognitive impairment: \u0026lt;26, as proposed in the original publication, and \u0026lt;\u0026thinsp;21, following recommendations by Lozano-Gallego et al. [18] and other studies to improve specificity in older populations[8,9].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e- Short Portable Mental Status Questionnaire (SPMSQ) of Pfeiffer\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA brief screening instrument with 10 items assessing orientation, memory and calculation. Scores\u0026thinsp;\u0026ge;\u0026thinsp;3 errors were considered suggestive of possible cognitive impairment[19,20].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e- Memory Impairment Screen (MIS)\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTest of episodic memory consisting in a 4-item delayed free and cued recall (0\u0026ndash;8). Scores\u0026thinsp;\u0026le;\u0026thinsp;4 were considered suggestive memory impairment [21,22].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e- Semantic Verbal Fluency Test (SVF) \u0026ndash; Animals\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eParticipants were asked to name as many animals as possible in 60 seconds. A score below 10 was considered indicative of impairment in Spanish older adults[23] .\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eA cognitive reserve questionnaire (Cuestionario de Reserva Cognitiva, Rami \u003cem\u003eet al\u003c/em\u003e., 2011) was also applied, including items related to educational level, occupational complexity and cognitive leisure activities (global score range: 0\u0026ndash;25)[24\u0026ndash;26].\u003c/p\u003e \u003cp\u003eTwo binary variables were created for MoCA classification (\u0026lt;\u0026thinsp;26 and \u0026lt;\u0026thinsp;21), and cross-tabulations were performed to compare prevalence and agreement across the instruments. Cohen\u0026rsquo;s Kappa coefficients were computed to assess concordance between MoCA and the other tests under each cut-off criterion.\u003c/p\u003e \u003cp\u003eA bivariate correlation analysis was also conducted to evaluate the relationships among the total scores obtained on the different cognitive screening tests using Pearson\u0026rsquo;s correlation coefficient. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.005. Two-tailed tests were applied.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMedication review and comorbidity assessment\u003c/h3\u003e\n\u003cp\u003eMedication data were collected through structured interview and review of medical prescriptions. For each participant, the following were recorded: i) active principles (ATC code); ii) daily dose; iii) number of concurrent medications; iv) polypharmacy status (defined as \u0026ge;\u0026thinsp;5 active principles). This information also gave us the comorbidities of the patients (hypertension, diabetes mellitus, dyslipidemia). Medications were used as proxy variables for comorbidities, that is that diagnosis of a pathology was assumed if the participant was taking medications intended specifically to treat that disease. For example, dyslipidemia was assumed if the patient was taking any kind of lipid-lowering therapy.\u003c/p\u003e\n\u003ch3\u003eFunctional independence\u003c/h3\u003e\n\u003cp\u003eIndependence in basic and instrumental activities of daily living was assessed with: Barthel Index (0-100); and Lawton and Brody Index (0\u0026ndash;8), higher scores indicating independence in both basic and instrumental activities.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn addition to descriptive statistics and concordance analyses, further \u003cem\u003epost-hoc\u003c/em\u003e comparisons were performed to explore associations between cognitive impairment and participant characteristics.\u003c/p\u003e \u003cp\u003eCategorical variables (sex, rural, chronic conditions, polypharmacy) were compared using chi-square tests and Odds Ratios (OR) with 95% confidence intervals (CI) were calculated when applicable.\u003c/p\u003e \u003cp\u003ePearson correlation coefficients were calculated to explore the relationship between age, cognitive reserve, number of medications, independence in basic and instrumental activities of daily living (Barthel Index and Lawton and Brody Scale) and cognitive test scores.\u003c/p\u003e \u003cp\u003eTo identify predictors of cognitive impairment, binary logistic regression analysis was conducted including sociodemographic, clinical, and functional variables as potential covariates. The dependent variable was cognitive impairment, defined as a MoCA score\u0026thinsp;\u0026lt;\u0026thinsp;21. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analysis were considered for inclusion in the multivariate model. Results were reported as OR with 95% CI.\u003c/p\u003e \u003cp\u003eLinear regression analyses were also performed using the different neuropsychological test\u0026rsquo;s total score as the dependent variables, to further explore associations with potential predictors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analysis) and cognitive performance between tests. Separate regression models were estimated for each cognitive test.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics v29 (IBM Corp.), with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch3\u003eSample characteristics\u003c/h3\u003e\n\u003cp\u003eA total of 166 patients participated in the study with a mean age of 72.57\u0026nbsp;\u0026plusmn;\u0026nbsp;5.836, and 60.8% were women (101). The most frequent comorbidity was hypercholesterolemia (57.2%), followed by hypertension (46.4%). Nearly half of participants (48.8%) met criteria for polypharmacy (\u0026ge;5 medications). Cognitive reserve averaged 12.49 \u0026plusmn; 4.438 points, and most participants were functionally independent (mean Barthel Index 98.64 \u0026plusmn; 3.627; Lawton and Brody 7.61\u0026nbsp;\u0026plusmn;\u0026nbsp;0.850). Full descriptive characteristics of the sample are shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Main characteristics of the sample.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of subjects\u003c/strong\u003e N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e (Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e72.57\u0026nbsp;\u0026plusmn;\u0026nbsp;5.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex/gender (Female)\u003c/strong\u003e N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e101 (60.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural\u0026nbsp;\u003c/strong\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e11 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u0026nbsp;\u003c/strong\u003eN (%)\u003c/p\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eHypercholesterolemia\u003c/p\u003e\n \u003cp\u003eType 2 Diabetes Mellitus\u003c/p\u003e\n \u003cp\u003eHypertriglyceridemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77 (46.4%)\u003c/p\u003e\n \u003cp\u003e95 (57.2%)\u003c/p\u003e\n \u003cp\u003e30 (18.1%)\u003c/p\u003e\n \u003cp\u003e3 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedication\u003c/strong\u003e N (%)\u003c/p\u003e\n \u003cp\u003eAntidepressants (N06A)\u003c/p\u003e\n \u003cp\u003eAnxiolytics, Hypnotics (N05B, N05C)\u003c/p\u003e\n \u003cp\u003eDementia (Donepezile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19 (11.4%)\u003c/p\u003e\n \u003cp\u003e27 (16.3%)\u003c/p\u003e\n \u003cp\u003e2 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive Test\u003c/strong\u003e (Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003cp\u003eSPMSQ\u003c/p\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003cp\u003eSVF (Animals)\u003c/p\u003e\n \u003cp\u003eMoCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.93\u0026nbsp;\u0026plusmn;\u0026nbsp;1.115\u003c/p\u003e\n \u003cp\u003e7.31\u0026nbsp;\u0026plusmn;\u0026nbsp;1.149\u003c/p\u003e\n \u003cp\u003e18.61\u0026nbsp;\u0026plusmn;\u0026nbsp;5.906\u003c/p\u003e\n \u003cp\u003e23.48\u0026nbsp;\u0026plusmn;\u0026nbsp;5.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive Reserve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e12.49\u0026nbsp;\u0026plusmn;\u0026nbsp;4.438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of active principles\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePolypharmacy\u003c/strong\u003e N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e5.22\u0026nbsp;\u0026plusmn;\u0026nbsp;3.885\u003c/p\u003e\n \u003cp\u003e81 (48.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBarthel Index\u003c/p\u003e\n \u003cp\u003eLawton and Brody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e98.64\u0026nbsp;\u0026plusmn;\u0026nbsp;3.627\u003c/p\u003e\n \u003cp\u003e7.61\u0026nbsp;\u0026plusmn;\u0026nbsp;0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment; SPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency\u003c/em\u003e.\u003c/p\u003e\n\u003ch3\u003eCognitive impairment assessment: prevalence, concordance and correlation\u003c/h3\u003e\n\u003cp\u003eRegarding cognitive assessment, the average score in the Montreal Cognitive Assessment (MoCA) was 23.48\u0026nbsp;\u0026plusmn;\u0026nbsp;5.035 (Table 1). When applying a cut-off of \u0026lt;26, 108 participants (65.1%) screened positive for cognitive impairment. This percentage dropped to 18.1% using the more conservative cut-off of \u0026lt;21 (Table 2).\u003c/p\u003e\n\u003cp\u003eThe prevalence of cognitive impairment varied substantially across screening tools: 6.6% for Short Portable Mental Status Questionnaire (SPMSQ), 4.8% for Memory Impairment Screen (MIS), and 4.2% for Semantic Verbal Fluency (SVF), compared to a 65.1% with MoCA (cut-off score of \u0026lt;26) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Prevalence of CI with different tests and cut-off points.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstrument\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThreshold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%) positive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026lt;26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e108 (65.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026lt;21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e30 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026le;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e8 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPMSQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e11 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSVF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026lt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e7 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment; SPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eConcordance analysis (Table 3) revealed low agreement between MoCA cut-off score \u0026lt;26 and the other tests (observed agreement between 38.0% and 41.6%, all Kappa\u0026lt;0.08). These comparisons were not statistically significant except for MoCA\u0026lt;26 vs. SPMSQ (p=0.012). However, when using MoCA\u0026lt;21 as a cut-off, the concordance improved, with observed agreement \u0026gt;84% and statistically significant Kappa values (p\u0026lt;0.001 in all cases).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Concordance analysis between MoCA and alternative cognitive screening tests (MIS, SPMSQ, SVF)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive cases\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative cases (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObserved agreement (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKappa (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoCA\u0026lt;26\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evs. MIS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evs. SPMSQ\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evs. SVF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e108 (65.1%)\u003c/p\u003e\n \u003cp\u003e8 (4.8%)\u003c/p\u003e\n \u003cp\u003e11 (6.6%)\u003c/p\u003e\n \u003cp\u003e7 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e58 (34.9%)\u003c/p\u003e\n \u003cp\u003e158 (95.2%)\u003c/p\u003e\n \u003cp\u003e155 (93.4%)\u003c/p\u003e\n \u003cp\u003e159 (95.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64 (38.6%)\u003c/p\u003e\n \u003cp\u003e69 (41.6%)\u003c/p\u003e\n \u003cp\u003e63 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoCA\u0026lt;21\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evs. MIS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evs. SPMSQ\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evs. SVF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e30 (18.1%)\u003c/p\u003e\n \u003cp\u003e8 (4.8%)\u003c/p\u003e\n \u003cp\u003e11 (6.6%)\u003c/p\u003e\n \u003cp\u003e7 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e136 (81.9%)\u003c/p\u003e\n \u003cp\u003e158 (95.2%)\u003c/p\u003e\n \u003cp\u003e155 (93.4%)\u003c/p\u003e\n \u003cp\u003e159 (95.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e140 (84.3%)\u003c/p\u003e\n \u003cp\u003e143 (86.1%)\u003c/p\u003e\n \u003cp\u003e141 (84.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment; SPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe correlation analysis demonstrated statistically significant associations among all cognitive tests evaluated (Appendix 1). The total MoCA score showed a moderate negative correlation with the SPMSQ (r =\u0026nbsp;-0.419; p \u0026lt; 0.001), indicating that higher MoCA scores were associated with fewer errors on the SPMSQ. Furthermore, MoCA scores were positively correlated with the MIS (r = 0.328; p \u0026lt; 0.001) and the SVF (r = 0.404; p \u0026lt; 0.001), suggesting that better global cognitive performance was related to greater recall and verbal fluency. The SPMSQ was negatively correlated with the MIS (r =\u0026nbsp;-0.380; p \u0026lt; 0.001) and the SVF (r =\u0026nbsp;-0.373; p \u0026lt; 0.001). Finally, a positive correlation was also observed between MIS and SVF scores (r = 0.247; p = 0.001).\u003c/p\u003e\n\u003ch3\u003ePatient characteristics affecting test results:\u003c/h3\u003e\n\u003cp\u003eAssociations between sociodemographic and clinical variables of patient characteristics and cognitive impairment, as assessed by each test, are presented in Table 4. For categorical variables, chi-square tests were performed using dichotomized cognitive impairment outcomes. For continuous variables, Pearson\u0026rsquo;s correlation coefficients were calculated with the total test scores.\u003c/p\u003e\n\u003cp\u003eOlder age was consistently associated with poorer cognitive performance across tests, with significant negative correlations with MoCA (r =\u0026nbsp;-0.331, p\u0026lt;0.001), MIS (r =\u0026nbsp;-0.246, p\u0026lt;0.001), and SVF (r =\u0026nbsp;-0.351, p\u0026lt;0.001), and a positive correlation with SPMSQ errors (r = 0.245, p \u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eFemale sex was more frequent among participants classified as cognitively impaired by the MoCA\u0026lt;21 cut-off (70.0%), the SVF (71.4%) and the MIS (75.0%), although chi-square tests did not reach statistical significance for these comparisons.\u003c/p\u003e\n\u003cp\u003eRegarding comorbidities, the presence of hypercholesterolemia was significantly associated with MoCA impairment with both cut-offs (cut-off \u0026lt;26: chi\u003csup\u003e2\u003c/sup\u003e = 9.149, p = 0.002 and cut-off \u0026lt;21: chi\u003csup\u003e2\u003c/sup\u003e = 5.652, p = 0.017), and also with impairment identified by the SVF (chi\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 5.462, p = 0.019). Hypertension was associated with SVF impairment (chi\u003csup\u003e2\u003c/sup\u003e = 4.545, p = 0.033), but no significant associations were observed for diabetes or depression.\u003c/p\u003e\n\u003cp\u003eCognitive reserve showed positive correlations with MoCA (r=0.282, p \u0026lt;0.001), MIS (r=0.174, p = 0.025), and SVF (r=0.350, p \u0026lt;0.001), and a negative correlation with SPMSQ errors (r=-0.248, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eRegarding functional measures, higher independence in basic activities of daily living, measured by the Barthel Index, was associated with better performance in the MIS (r = 0.226, p = 0.003) and fewer errors in the SPMSQ (r =-0.212, p = 0.006). No significant associations were found between the Lawton and Brody scale and cognitive tests scores.\u003c/p\u003e\n\u003cp\u003eNo significant associations were observed between polypharmacy and dichotomized cognitive impairment in any of the tools, although the number of chronic medications showed a weak negative correlation with MIS performance (r =-0.204, p = 0.008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Associations between patient characteristics and cognitive impairment according to different screening tools.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN=166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePC = 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSVF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePC = 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPMSQ\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePC = 11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off \u0026lt;26\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePC = 108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off\u0026lt;21\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePC = 30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u0026nbsp;\u003c/strong\u003eN (%)\u003c/p\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e61 (56.5%)\u003c/p\u003e\n \u003cp\u003e2.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e21 (70.0%)\u003c/p\u003e\n \u003cp\u003e1.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6 (75.0%)\u003c/p\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5 (71.4%)\u003c/p\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3 (27.3%)\u003c/p\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003ePearson\u0026rsquo;s r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-0.331***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.246***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.351***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.245***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural\u0026nbsp;\u003c/strong\u003eN (%)\u003c/p\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8 (7.4%)\u003c/p\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2 (6.7%)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1 (14.3%)\u003c/p\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u0026nbsp;\u003c/strong\u003eN (%)\u003c/p\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e55 (50.9%)\u003c/p\u003e\n \u003cp\u003e2.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e17 (56.7%)\u003c/p\u003e\n \u003cp\u003e1.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5 (62.5%)\u003c/p\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6 (85.7%)\u003c/p\u003e\n \u003cp\u003e4.545*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7 (63.6%)\u003c/p\u003e\n \u003cp\u003e1.410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypercholesterolemia\u0026nbsp;\u003c/strong\u003eN (%)\u003c/p\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e71 (65.7%)\u003c/p\u003e\n \u003cp\u003e9.149**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e23 (76.7%)\u003c/p\u003e\n \u003cp\u003e5.652*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4 (50.0%)\u003c/p\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7 (100%)\u003c/p\u003e\n \u003cp\u003e5.462*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9 (81.1%)\u003c/p\u003e\n \u003cp\u003e2.910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u0026nbsp;\u003c/strong\u003eN (%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e20 (18.5%)\u003c/p\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7 (23.3%)\u003c/p\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3 (37.5%)\u003c/p\u003e\n \u003cp\u003e2.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2 (28.6%)\u003c/p\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2 (18.2%)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u0026nbsp;\u003c/strong\u003eN (%)\u003c/p\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e13 (12.0%)\u003c/p\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1 (3.3%)\u003c/p\u003e\n \u003cp\u003e2.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003cp\u003e1.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolypharmacy\u0026nbsp;\u003c/strong\u003eN (%)\u003c/p\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of AP\u0026nbsp;\u003c/strong\u003ePearson\u0026rsquo;s r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e55 (50.9%)\u003c/p\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e16 (53.3%)\u003c/p\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e5 (62.5%)\u003c/p\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003cp\u003e-0.204**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4 (57.1%)\u003c/p\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7 (63.6%)\u003c/p\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive reserve\u0026nbsp;\u003c/strong\u003ePearson\u0026rsquo;s r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.282***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.174*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.350***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.248***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarthel Index\u0026nbsp;\u003c/strong\u003ePearson\u0026rsquo;s r\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLawton \u0026amp; Brody\u0026nbsp;\u003c/strong\u003ePearson\u0026rsquo;s r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003cp\u003e-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.226**\u003c/p\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003cp\u003e-0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.212**\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment;\u0026nbsp;\u003c/em\u003ePC: positive cases; \u003cem\u003eSPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency\u003c/em\u003e. *: p\u0026lt;0.05; **: p\u0026lt;0.01; ***: p\u0026lt;0.001.\u003c/p\u003e\n\u003ch3\u003eRegression models\u003c/h3\u003e\n\u003cp\u003eA binary logistic regression analysis was conducted to examine the association between cognitive impairment (MoCA\u0026lt;21) and potential predictors, including age, cognitive reserve, and diagnosis of hypercholesterolemia (p\u0026lt;0.1 in univariate analysis). The logistic regression model was statistically significant, which supports the influence of the predictors on the likelihood of exhibiting cognitive impairment. The model\u0026rsquo;s constant was B =-11.436 (SE=3.298, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eAge was positively associated with cognitive impairment (B = 0.155, SE = 0.042, Wald= 13.42, p\u0026lt;0.001), corresponding to an OR of 1.168. This indicates that each additional year of age increased the odds of cognitive impairment by 16.8%.\u003c/p\u003e\n\u003cp\u003eCognitive reserve was inversely associated with cognitive impairment (B =-0.214, SE = 0.061, Wald = 12.51, p\u0026lt;0.001), with an OR of 0.807, suggesting that higher cognitive reserve reduced the odds by approximately 19.3% per unit increase.\u003c/p\u003e\n\u003cp\u003eImportantly, the diagnosis of hypercholesterolemia remained a significant independent predictor after adjusting for age and cognitive reserve (B = 1.269, SE = 0.538, Wald = 5.56, p=0.018), corresponding to an OR of 3.558. This indicates that participants with hypercholesterolemia had more than three times the odds of cognitive impairment compared to those without this diagnosis.\u003c/p\u003e\n\u003cp\u003eA forest plot was generated to illustrate the effect sizes of each predictor (Figure 1). The odds ratios (OR) and 95% confidence intervals (CI) were as follows: age (OR=1.168, 95% CI: 1.075\u0026ndash;1.271), cognitive reserve (OR=0.807, 95% CI: 0.714\u0026ndash;0.911), and hypercholesterolemia (OR=3.558, 95% CI: 1.241\u0026ndash;10.202). These results indicate that higher age and the presence of hypercholesterolemia may significantly increased the odds of cognitive impairment, whereas higher cognitive reserve was a protective factor.\u003c/p\u003e\n\u003cp\u003eSeparate linear regression models were estimated to examine the predictors of performance in each cognitive test (Table 5). Standardized regression coefficients for predictors across cognitive tests are showed in Figure 2.\u003c/p\u003e\n\u003ch4\u003eMontreal Cognitive Assessment (MoCA)\u003c/h4\u003e\n\u003cp\u003eFor the MoCA total score, older age was significantly associated with lower scores (B=-0.260, p \u0026lt; 0.001), while higher cognitive reserve was associated with higher scores (B=+0.265, p = 0.001). Hypercholesterolemia was also significantly associated with a decrease of approximately 1.75 points in MoCA scores (B =-1.554, p=0.032). The model explained 17% of the variance (adjusted R\u0026sup2;=0.177).\u003c/p\u003e\n\u003ch4\u003eShort Portable Mental Status Questionnaire (SPMSQ)\u003c/h4\u003e\n\u003cp\u003eIn the SPMSQ model, older age (B=+0.038, p=0.009), and lower cognitive reserve (B=-0.047, p=0.016) were significantly associated with higher error scores, indicating worse cognitive performance. Hypercholesterolemia (B=+0.130, p= 0.435) and lower functional independence as measured by the Barthel Index (B=+0.039, p=0.098), showed a nonsignificant association. The model accounted for 10.5% of the variance (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.105).\u003c/p\u003e\n\u003ch4\u003eMemory Impairment Screen (MIS)\u003c/h4\u003e\n\u003cp\u003eFor the MIS total score, age (B=-0.038, p= 0.012) and the total number of chronic active pharmaceutical ingredients (B=-0.047, p= 0.036) were significant predictors, while Barthel Index (B=0.044, p= 0.078) cognitive reserve (B=+0.029, p= 0.148) showed nonsignificant associations. The model explained 10.8% of the variance (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e=0.108).\u003c/p\u003e\n\u003ch4\u003eSemantic Verbal Fluency (SVF)\u003c/h4\u003e\n\u003cp\u003eIn the SVF model, higher age (B=-0.311, p\u0026lt;0.001) and lower cognitive reserve (B= 0.403, p\u0026lt;0.001) were associated with lower verbal fluency scores, The presence of hypercholesterolemia (B=-1.272, p=0.132) and hypertension (B=-0.295, p= 0.732) showed a nonsignificant association. The overall model accounted for 23.3% of the variance (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.213).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Linear regression models for each cognitive test performance\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eB (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAdj R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognitive Reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypercholesterolemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPMSQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognitive Reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBarthel Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypercholesterolemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo. of Active Drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBarthel Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognitive Reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSVF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognitive Reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypercholesterolemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eAdj R\u003csup\u003e2\u003c/sup\u003e: adjusted R\u003csup\u003e2;\u0026nbsp;\u003c/sup\u003eB: regression constant; MIS: Memory Impairment Screen; MoCA: Montreal Cognitive Assessment; SPMSQ: Short Portable Mental Status Questionnaire; SVF: Semantic Verbal Fluency\u003c/em\u003e\u003cem\u003e;\u003c/em\u003e\u003cem\u003e\u0026nbsp;SE: standard error.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that the estimated prevalence of suspected cognitive impairment in community-dwelling older adults varies substantially depending on the screening instrument and the cut-off applied. Using the conventional Montreal Cognitive Assessment (MoCA) threshold of \u0026lt;\u0026thinsp;26, nearly two-thirds of participants screened positive for cognitive impairment. In contrast, the prevalence dropped to 18% with the more stringent cut-off of \u0026lt;\u0026thinsp;21, and fell below 7% when assessed with Short Portable Mental Status Questionnaire (SPMSQ), Memory Impairment Screen (MIS) or Semantic Verbal Fluency (SVF). These findings highlight the critical influence of methodological choices on prevalence estimates, and align with previous reports indicating that the MoCA\u0026rsquo;s sensitivity is achieved partly at the cost of reduced specificity in older populations, with MoCA\u0026thinsp;\u0026lt;\u0026thinsp;26 likely overidentifying impairment in this population[3,8,9,18].\u003c/p\u003e \u003cp\u003eThe concordance analysis revealed that MoCA cut-off \u0026lt;\u0026thinsp;26 showed poor agreement with other instruments (Kappa\u0026thinsp;\u0026lt;\u0026thinsp;0.008), suggesting that this threshold may overidentify impairment relative to briefer tools. However, when applying MoCA\u0026thinsp;\u0026lt;\u0026thinsp;21, the observed agreement with the SPMSQ, MIS, and SVF increased substantially (\u0026gt;\u0026thinsp;84% agreement, all Kappa values significant). This pattern supports the argument that a cut-off below 21 is more appropriate in Spanish community-dwelling older adults, as it improves the balance between sensitivity and specificity and reduces misclassification [9,18,27].\u003c/p\u003e \u003cp\u003eThe correlation matrix underscored that while instruments share some variance (moderate correlation), each assesses partially distinct cognitive domains. The MoCA, with is broader scope, captures deficits in executive functioning and visuospatial processing that the SPMSQ and MIS do not systematically evaluate. This is consistent with evidence indicating that verbal fluency and executive dysfunction often precede memory complaints in prodromal dementia[28\u0026ndash;31]. Notably, semantic fluency correlated positively with MoCA total scores, reinforcing the contribution of lexical access and executive retrieval processes to global cognitive performance.\u003c/p\u003e \u003cp\u003eBeyond tests-results discordance, our analyses identified consistent factors associated with cognitive performance. Age emerged as a consistent negative predictor across all tests, confirming the pervasive effect of aging on multiple cognitive domains. Higher cognitive reserve also showed a protective association, particularly in the MoCA and SVF, in line with previous consolidated evidence that education and cognitively stimulating activities buffer against decline [25].\u003c/p\u003e \u003cp\u003eImportantly, the presence of hypercholesterolemia (using lipid-lowering medication use as a proxy variable for the disease) was independently associated with lower MoCA scores even after adjustment for age and cognitive reserve. This association suggest a potential vascular-metabolic contribution to global cognitive decline in this cohort, in line with longitudinal studies linking dyslipidemia with cognitive deterioration and increasing dementia risk [14]. Interestingly, the impact of hypercholesterolemia was most evident in the MoCA, but not consistently significant across the other instruments. This could reflect the greater sensitivity of the MoCA to detect executive function and attention deficits, commonly related to cardiovascular pathology [32]. This result reinforces the need to integrate cardiovascular risk management into strategies for cognitive screening and prevention.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, our findings indicate that relying exclusively on a single screening test, particularly one with a highly sensitive threshold such as MoCA\u0026thinsp;\u0026lt;\u0026thinsp;26, may lead to substantial overestimation of cognitive impairment prevalence. At the same time, very brief instruments such as the SPMSQ or MIS may fail to detect early executive dysfunction or mild cognitive decline. In clinical and research settings, where the aim is to balance sensitivity and specificity, applying a MoCA cut-off below 21 appears to offer a more conservative and valid approach. Nevertheless, it cannot be denied that cognitive decline may begin up to 20 years before symptoms become noticeable, so applying a stricter cut-off could help identify patients at risk and monitor their progression [33].\u003c/p\u003e \u003cp\u003eThese results have implications for the design of screening protocols in primary care and for the interpretations of epidemiological data on cognitive impairment prevalence. Furthermore, they highlight the need for comprehensive cognitive profiling that considers multiple domains and adjusts for individual risk factors such as metabolic comorbidities and cognitive reserve.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe study benefits from a well-characterized community sample and the simultaneous administration of multiple validated tools. Nevertheless, limitations should be acknowledged: the cross-sectional design precludes casual inference; the sample size, while adequate for prevalence estimates, may limit the power of multivariate models; and the absence of a reference standard diagnosis (e.g., clinical neuropsychological assessment) prevents to formally establish sensitivity and specificity. Future research should adopt longitudinal designs, incorporating biomarkers and neuroimaging that could help clarify the predictive value of different screening thresholds, and explore the cross-cultural sociodemographic and metabolic factors affecting the development of cognitive decline.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this research highlights the discordance among widely used cognitive screening instruments and emphasizes that the choice of tool and its cut-off critically determine the estimated prevalence of cognitive impairment. The findings support the adoption of a more conservative Montreal Cognitive Assessment cut-off (\u0026lt;\u0026thinsp;21) to improve agreement with other brief instruments and reduce false positives in Spanish older adults. Additionally, the observed association between hypercholesterolemia and lower cognitive performance reinforces the relevance of vascular-metabolic health in cognitive aging and highlights the importance of integrated preventive screening strategies in primary care and community settings, combining sensitive cognitive tools with cardiovascular risk management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegression coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCognitive Impairment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMemory Impairment Screen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMoCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMontreal Cognitive Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPMSQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort Portable Mental Status Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSVF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSemantic Verbal Fluency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe are deeply grateful to \u003cem\u003eFarmacia Ana Rubio\u0026nbsp;\u003c/em\u003eand \u003cem\u003eFarmacia Cid 51\u003c/em\u003e for their invaluable cooperation in the recruitment and assessment of participants. We also extend our thanks to the \u003cem\u003eColegio Oficial de Farmacéuticos de Albacete\u0026nbsp;\u003c/em\u003efor its collaboration and the participants themselves, whose willingness to take part made this research posible.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Excelentísima Diputación de Albacete with the award of a research grant from the \u003cem\u003eJuan Carlos Izpisúa Belmonte\u0026nbsp;\u003c/em\u003eprogram\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eThe research protocol was approved by the Ethics Committee of the Albacete Integrated Care Management System (approval reference: 2023-155) and was conducted in accordance with the Declaration of Helsinki. All participants voluntarily enrolled the study and signed written informed consent.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003eAuthors’ contribution\u003c/p\u003e\n\u003cp\u003eAll authors made a substantial contribution to the manuscript and agree with the final published version. LSG and LAML were responsible for the study conceptualization and design. The methodology was developed by LSG, including the statistical analysis strategy. LSG and CGG conducted the investigation and data acquisition, being responsible for participant recruitment and test administration. Data curation and formal statistical analysis (concordance and regression models) were primarily the responsibility of LSG. LCV, GBA and JACdL provided project supervision, resource management, and funding acquisition. LSG wrote the original draft preparation, while LCV and LAML performed the critical review and editing of the manuscript for intellectual content and clarity. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eData are available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePetersen RC. Mild Cognitive Impairment. Continuum (N Y) 2016;22:404\u0026ndash;18. https://doi.org/10.1212/CON.0000000000000313.\u003c/li\u003e\n\u003cli\u003eAlzola P, Carnero C, Bermejo-Pareja F, S\u0026aacute;nchez-Benavides G, Pe\u0026ntilde;a-Casanova J, Puertas-Mart\u0026iacute;n V, et al. Neuropsychological Assessment for Early Detection and Diagnosis of Dementia: Current Knowledge and New Insights. J Clin Med 2024;13:3442. https://doi.org/10.3390/jcm13123442.\u003c/li\u003e\n\u003cli\u003eValles-Salgado M, Matias-Guiu JA, Delgado-\u0026Aacute;lvarez A, Delgado-Alonso C, Gil-Moreno MJ, Valiente-Gordillo E, et al. Comparison of the Diagnostic Accuracy of Five Cognitive Screening Tests for Diagnosing Mild Cognitive Impairment in Patients Consulting for Memory Loss. J Clin Med 2024;13. https://doi.org/10.3390/jcm13164695.\u003c/li\u003e\n\u003cli\u003eChun CT, Seward K, Patterson A, Melton A, MacDonald-Wicks L. Evaluation of Available Cognitive Tools Used to Measure Mild Cognitive Decline: A Scoping Review. Nutrients 2021;13:3974. https://doi.org/10.3390/nu13113974.\u003c/li\u003e\n\u003cli\u003eNasreddine ZS, Phillips NA, B\u0026eacute;dirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005;53:695\u0026ndash;9. https://doi.org/10.1111/j.1532-5415.2005.53221.x.\u003c/li\u003e\n\u003cli\u003eBulgarelli L, Gyr E, Villanueva J, Mej\u0026iacute;a K, Mej\u0026iacute;a C, Paredes R, et al. Normative data of the Spanish version of the Montreal Cognitive Assessment (MoCA) in older individuals from Peru. ACTA Paulista de Enfermagem 2025;19. https://doi.org/10.1590/1980-5764-dn-2024-0261.\u003c/li\u003e\n\u003cli\u003eRossetti HC, Lacritz LH, Cullum CM, Weiner MF. Normative data for the Montreal Cognitive Assessment (MoCA) in a population-based sample. Neurology 2011;77:1272\u0026ndash;5. https://doi.org/10.1212/WNL.0b013e318230208a.\u003c/li\u003e\n\u003cli\u003eCarson N, Leach L, Murphy KJ. A re‐examination of Montreal Cognitive Assessment (MoCA) cutoff scores. Int J Geriatr Psychiatry 2018;33:379\u0026ndash;88. https://doi.org/10.1002/gps.4756.\u003c/li\u003e\n\u003cli\u003eDelgado C, Araneda A, Behrens MI. Validaci\u0026oacute;n del instrumento Montreal Cognitive Assessment en espa\u0026ntilde;ol en adultos mayores de 60 a\u0026ntilde;os. Neurolog\u0026iacute;a 2019;34:376\u0026ndash;85. https://doi.org/10.1016/j.nrl.2017.01.013.\u003c/li\u003e\n\u003cli\u003eLivingston G, Huntley J, Liu KY, Costafreda SG, Selb\u0026aelig;k G, Alladi S, et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. The Lancet 2024;404:572\u0026ndash;628. https://doi.org/10.1016/S0140-6736(24)01296-0.\u003c/li\u003e\n\u003cli\u003eSolomon A, Kivipelto M, Wolozin B, Zhou J, Whitmer RA. Midlife Serum Cholesterol and Increased Risk of Alzheimer\u0026rsquo;s and Vascular Dementia Three Decades Later. Dement Geriatr Cogn Disord 2009;28:75\u0026ndash;80. https://doi.org/10.1159/000231980.\u003c/li\u003e\n\u003cli\u003eWhitmer RA, Sidney S, Selby J, Johnston SC, Yaffe K. Midlife cardiovascular risk factors and risk of dementia in late life. Neurology 2005;64:277\u0026ndash;81. https://doi.org/10.1212/01.WNL.0000149519.47454.F2.\u003c/li\u003e\n\u003cli\u003eDeckers K, Cadar D, van Boxtel MPJ, Verhey FRJ, Steptoe A, K\u0026ouml;hler S. Modifiable Risk Factors Explain Socioeconomic Inequalities in Dementia Risk: Evidence from a Population-Based Prospective Cohort Study. Journal of Alzheimer\u0026rsquo;s Disease 2019;71:549\u0026ndash;57. https://doi.org/10.3233/JAD-190541.\u003c/li\u003e\n\u003cli\u003eAnstey KJ, Ee N, Eramudugolla R, Jagger C, Peters R. A Systematic Review of Meta-Analyses that Evaluate Risk Factors for Dementia to Evaluate the Quantity, Quality, and Global Representativeness of Evidence. J Alzheimer\u0026rsquo;s Dis 2019;70:S165\u0026ndash;86. https://doi.org/10.3233/JAD-190181.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a C, Moreno L, Alacreu M, Mu\u0026ntilde;oz FJ, Mart\u0026iacute;nez LA. Addressing Psychosocial Factors in Cognitive Impairment Screening from a Holistic Perspective: The DeCo-Booklet Methodology Design and Pilot Study. Int J Environ Res Public Health 2022;19:12911. https://doi.org/10.3390/ijerph191912911.\u003c/li\u003e\n\u003cli\u003eOjeda N, del Pino R, Ibarretxe-Bilbao N, Schretlen DJ, Pe\u0026ntilde;a D. Test de evaluaci\u0026oacute;n cognitiva de Montreal: normalizaci\u0026oacute;n y estandarizaci\u0026oacute;n de la prueba en poblaci\u0026oacute;n espa\u0026ntilde;ola. Rev Neurol 2016:488\u0026ndash;96. PMID: 27874165\u003c/li\u003e\n\u003cli\u003eBermejo-Pareja F, Benito-Le\u0026oacute;n J, Vega-Q S, D\u0026iacute;az-Guzm\u0026aacute;n J, Rivera-Navarro J, Molina JA, et al. [The NEDICES cohort of the elderly. Methodology and main neurological findings]. Rev Neurol n.d.;46:416\u0026ndash;23. PMID: 18389461\u003c/li\u003e\n\u003cli\u003eLozano Gallego M, Hern\u0026aacute;ndez Ferr\u0026aacute;ndiz M, Turr\u0026oacute; Garriga O, Pericot Nierga I, L\u0026oacute;pez-Pausa S, Vilalta Franch J. Validaci\u0026oacute;n del Montreal Cognitive Assessment (MoCA): test de cribado para el deterioro cognitivo leve. Datos preliminares. Alzheimer Real Invest Demenc 2009;43:4\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003ePfeiffer E. A Short Portable Mental Status Questionnaire for the Assessment of Organic Brain Deficit in Elderly Patients\u0026dagger;. J Am Geriatr Soc 1975;23:433\u0026ndash;41. https://doi.org/10.1111/j.1532-5415.1975.tb00927.x.\u003c/li\u003e\n\u003cli\u003eGornemann I, Zunzunegui MV, Martı́nez C, del Carmen Onı́s M. Screening for impaired cognitive function among the elderly in Spain: reducing the number of items in the Short Portable Mental Status Questionnaire. Psychiatry Res 1999;89:133\u0026ndash;45. https://doi.org/10.1016/S0165-1781(99)00089-X.\u003c/li\u003e\n\u003cli\u003eModrego PJ, Gazulla J. The Predictive Value of the Memory Impairment Screen in Patients With Subjective Memory Complaints. Prim Care Companion CNS Disord 2013. https://doi.org/10.4088/PCC.12m01435.\u003c/li\u003e\n\u003cli\u003eBuschke H, Kuslansky G, Katz M, Stewart WF, Sliwinski MJ, Eckholdt HM, et al. Screening for dementia with the Memory Impairment Screen. Neurology 1999;52:231\u0026ndash;231. https://doi.org/10.1212/WNL.52.2.231.\u003c/li\u003e\n\u003cli\u003eCarnero Pardo C, Lend\u0026iacute;nez Gonz\u0026aacute;lez A. Utilidad del test de fluencia verbal sem\u0026aacute;ntica en el diagn\u0026oacute;stico de demencia. Rev Neurol 1999;29:709. https://doi.org/10.33588/rn.2908.99233.\u003c/li\u003e\n\u003cli\u003eRami L, Valls-Pedret C, Bartr\u0026eacute;s-Faz D, Caprile C, Sol\u0026eacute;-Padull\u0026eacute;s C, Castellvi M, et al. Cognitive reserve questionnaire. Scores obtained in a healthy elderly population and in one with Alzheimer\u0026rsquo;s disease. Rev Neurol 2011;52:195\u0026ndash;201. PMID: 21312165\u003c/li\u003e\n\u003cli\u003eStern Y. Cognitive reserve in ageing and Alzheimer\u0026rsquo;s disease. Lancet Neurol 2012;11:1006\u0026ndash;12. https://doi.org/10.1016/S1474-4422(12)70191-6.\u003c/li\u003e\n\u003cli\u003eMartino P, Caycho Rodr\u0026iacute;guez T, Valencia PD, Politis D, Gallegos M, De Bortoli M\u0026Aacute;, et al. Cuestionario de reserva cognitiva: an\u0026aacute;lisis psicom\u0026eacute;trico desde la teor\u0026iacute;a de respuesta al \u0026iacute;tem. Rev Neurol 2022;75:173. https://doi.org/10.33588/rn.7507.2022113.\u003c/li\u003e\n\u003cli\u003eElkana O, Tal N, Oren N, Soffer S, Ash EL. Is the Cutoff of the MoCA too High? Longitudinal Data From Highly Educated Older Adults. J Geriatr Psychiatry Neurol 2020;33:155\u0026ndash;60. https://doi.org/10.1177/0891988719874121.\u003c/li\u003e\n\u003cli\u003eTierney MC, Yao C, Kiss A, McDowell I. Neuropsychological tests accurately predict incident Alzheimer disease after 5 and 10 years. Neurology 2005;64:1853\u0026ndash;9. https://doi.org/10.1212/01.WNL.0000163773.21794.0B.\u003c/li\u003e\n\u003cli\u003eLiampas I, Folia V, Zoupa E, Siokas V, Yannakoulia M, Sakka P, et al. Qualitative Verbal Fluency Components as Prognostic Factors for Developing Alzheimer\u0026rsquo;s Dementia and Mild Cognitive Impairment: Results from the Population-Based HELIAD Cohort. Medicina (Kaunas) 2022;58. https://doi.org/10.3390/medicina58121814.\u003c/li\u003e\n\u003cli\u003eClark LR, Schiehser DM, Weissberger GH, Salmon DP, Delis DC, Bondi MW. Specific measures of executive function predict cognitive decline in older adults. J Int Neuropsychol Soc 2012;18:118\u0026ndash;27. https://doi.org/10.1017/S1355617711001524.\u003c/li\u003e\n\u003cli\u003eReinvang I, Grambaite R, Espeseth T. Executive Dysfunction in MCI: Subtype or Early Symptom. Int J Alzheimers Dis 2012;2012:936272. https://doi.org/10.1155/2012/936272.\u003c/li\u003e\n\u003cli\u003eHayes CA, Young CB, Abdelnour C, Reeves A, Odden MC, Nirschl J, et al. The impact of arteriolosclerosis on cognitive impairment in decedents without severe dementia from the National Alzheimer\u0026rsquo;s Coordinating Center. Alzheimer\u0026rsquo;s \u0026amp; Dementia 2025;21. https://doi.org/10.1002/alz.70059.\u003c/li\u003e\n\u003cli\u003eCaselli RJ, Langlais BT, Dueck AC, Chen Y, Su Y, Locke DEC, et al. Neuropsychological decline up to 20 years before incident mild cognitive impairment. Alzheimers Dement 2020;16:512\u0026ndash;23. https://doi.org/10.1016/j.jalz.2019.09.085.\u003c/li\u003e\n\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cognitive impairment, older adults, community-dwellers, neuropsychological tests, screening, prevalence, hypercholesterolemia, community pharmacy","lastPublishedDoi":"10.21203/rs.3.rs-8143343/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8143343/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCognitive impairment is a major public health concern due to its impact on functional independence and its risk of progression to dementia. Early detection is critical, but the estimated prevalence varies substantially depending on the screening tool used and the role of modifiable metabolic risk factors, in accelerating cognitive aging.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to evaluate the concordance among the Montreal Cognitive Assessment (MoCA) using two alternative cut-offs and three other validated cognitive screening tools, and to analyze factors associated with lower cognitive performance in community-dwelling older adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted with N\u0026thinsp;=\u0026thinsp;166 community-dwelling patients aged over 60 years, recruited from community pharmacies in Albacete, Spain. Cognitive status was assessed using the MoCA (cut-offs\u0026thinsp;\u0026lt;\u0026thinsp;26 and \u0026lt;\u0026thinsp;21), the Short Portable Mental Status Questionnaire, the Memory Impairment Screen, and the Semantic Verbal Fluency Test (animals). Comorbidities were assessed using active medication prescriptions as proxy variables. Cohen\u0026rsquo;s Kappa coefficients were computed to assess concordance, and a binary logistic regression was performed to identify independent predictors of cognitive impairment, defined as a MoCA score\u0026thinsp;\u0026lt;\u0026thinsp;21.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe estimated prevalence of cognitive impairment varied from 65.1% using the highest MoCA cut-off (\u0026lt;\u0026thinsp;26) to 18.1 using the more conservative MoCA\u0026thinsp;\u0026lt;\u0026thinsp;21 threshold. Concordance analysis revealed low agreement between MoCA\u0026thinsp;\u0026lt;\u0026thinsp;26 and the other instruments (Kappa\u0026thinsp;\u0026lt;\u0026thinsp;0.008). However, using the MoCA\u0026thinsp;\u0026lt;\u0026thinsp;21 cut-off, the observed agreement improved substantially to over 84% (all Kappa values statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The adjusted binary logistic regression model demonstrated that older age (OR\u0026thinsp;=\u0026thinsp;1.168, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the diagnosis of hypercholesterolemia (OR\u0026thinsp;=\u0026thinsp;3.558, p\u0026thinsp;=\u0026thinsp;0.018) significantly increased the odds of cognitive impairment, whereas higher cognitive reserve was a protective factor (OR\u0026thinsp;=\u0026thinsp;0.807, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe estimated prevalence of suspected cognitive impairment is highly dependent on the screening instrument and threshold selected. The findings support the adoption of a more conservative MoCA cut-off \u0026lt;\u0026thinsp;21 to improve concordance with other brief instruments and reduce false positives in this population. Additionally, the independent association between hypercholesterolemia and lower cognitive performance highlights the importance of integrated preventive strategies in primary care, combining sensitive cognitive screening with cardiovascular risk management.\u003c/p\u003e","manuscriptTitle":"Identifying individuals at risk of cognitive decline: Cross-sectional analysis of variability in neuropsychological test scores among community-dwelling older adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:34:20","doi":"10.21203/rs.3.rs-8143343/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-18T15:49:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T21:14:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101059327829066661162476411242329815938","date":"2026-01-30T11:48:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-29T11:59:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192266567927791566007554210959130991834","date":"2026-01-26T10:11:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T07:32:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-05T06:55:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T09:11:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T09:07:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-18T08:53:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad5181cc-7e2c-43f7-910c-20813cb0ef0e","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:02:20+00:00","versionOfRecord":{"articleIdentity":"rs-8143343","link":"https://doi.org/10.1186/s12889-026-27246-y","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-04-09 15:58:50","publishedOnDateReadable":"April 9th, 2026"},"versionCreatedAt":"2026-01-29 00:34:20","video":"","vorDoi":"10.1186/s12889-026-27246-y","vorDoiUrl":"https://doi.org/10.1186/s12889-026-27246-y","workflowStages":[]},"version":"v1","identity":"rs-8143343","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8143343","identity":"rs-8143343","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}