Objective

We aimed to provide the normative data stratified by age, sex and educational attainment for two semantic categories (animal and fruits) in older Spanish adults.

Method

A representative sample of 2,744 non-demented older individuals with different socioeconomic background was selected from the Neurological Disorders in Central Spain (NEDICES), a population-based study. Normative data are presented in percentile ranks and divided into four age-tables with different midpoints, using the overlapping interval procedure.

Results

Correlation analyses showed that age, education and sex influence significantly the scores in both semantic tasks. Normative data presented here covered two urban (Margaritas & Lista) and one rural areas (Arévalo).

Conclusion

These norms may provide useful data for screening cognitive impairment more accurately in Spanish older adults.

Introduction

Verbal fluency tasks (VFTs) are commonly used in protocols aimed at assessing cognitive functions in older adults (Contador & Ramos, 2009; Contador, Fernández-Calvo, Ramos, Tapias-Merino & Bermejo-Pareja, 2010). The most widespread forms are semantic and phonemic VFTs, which require participants to produce in 60 s as many words as possible from given semantic categories (e.g. animals and fruits) or particular letters (e.g., FAS), respectively (Peña-Casanova et al., 2009). VFTs provide information about different processes such as semantic memory, selective attention, working memory, inhibition, processing speed and cognitive flexibility (Ruff, Light, Parker & Levin, 1997; Rende, Ramsberger & Miyake 2002). Despite the fact that semantic and phonemic VFTs share common process (Azuma, 2004), Gourovitch et al. (2000) found increased prefrontal lobe activity in phonemic verbal fluency and major activation of the temporary cortex in semantic fluency condition. Thus, it seems that semantic VFTs are less demanding than phonemic VFTs (Shao, Janse, Visser & Meyer 2014).

Semantic VFTs are quick to administer, easy to use, and helpful to detect cognitive impairment in neurodegenerative diseases (Monsch Bondi, Butters, Salmon, Katzman & Thal, 1992; Henry & Crawford, 2004). Moreover, poorer semantic VF is a common manifestation of people with mild cognitive impairment (Alladi, Arnold, Mitchell, Nestor & Hodges, 2006; Mueller et al., 2015; Serna et al., 2015). Some authors have claimed that VFTs are useful for early detection of Alzheimer's disease (AD), even at preclinical stages (Laukka, Macdonald, Fratiglioni & Bäckman, 2012; Schmid Taylor, Foldi, Berres & Monsch, 2013). Finally, semantic VF seems more dependent on the patients’ education (Buriel, Gramunt, Bohm, Rodes & Pena-Casanova, 2004; Peña-Casanova et al., 2009) and more sensitive than phonemic VF to early cognitive changes in AD (Monsch et al., 1992).

Previous studies have indicated that education is possibly the most important sociodemographic variable related to category VF scores (Benito-Cuadrado, Esteba-Castillo, Böhm, Cejudo-Bolívar & Peña-Casanova, 2002; Ostrosky-Solis, Gutierrez, Flores & Ardila, 2007). In addition, a less pronounced but significant effect of age compared to education is described in other studies (Lozano & Ostrosky-Solis, 2006; Casals-Coll et al., 2013), whereas the effect of sex seems controversial (Benito-Cuadrado et al, 2002; González, Mungas & Haan, 2005; Olabarrieta-Landa et al., 2015; see Mitrushina, Boone, Razani & D'Elia, 2005, for a review). Accordingly, different cut-off points corrected by sociodemographic variables are needed to interpret VF scores correctly and reduce the risk of misclassification in clinical practice (Alegret et al., 2013; de Paula et al., 2013). In this context, reference values based on percentile ranks may help to optimize diagnostic accuracy compared to fixed cut-off points (Busch & Chapin, 2008; O'Connell & Tuokko, 2010), especially in people with low levels of education (Marcopulos, Gripshover, Broshek, McLain & Brashear, 1999; Heaton, Ryan & Grant, 2009).

Normative data for semantic VFTs in older adults Spanish-speaking population have been published recently (Olabarrieta-Landa et al., 2015; Chávez-Oliveros et al., 2015), but specific norms are required for people living in Spain taking into account the potential influence of linguistic and cultural factors (González et al., 2005; Ostrosky-Solis et al., 2007). In Spain, the existing norms have some setbacks. For instance, Benito-Cuadrado and colleagues (2002) used a very limited sample of older adults, whereas the data of the Peña-Casanova and colleagues (2009) study are not population-based.

The objective of the current study is to provide normative data (stratified by age, sex and education) for two semantic VFTs (animals and fruits) in a large Spanish population-based cohort of non-demented older individuals living in rural and urban areas.

Methods

Participants

NEDICES is a population census-based study to detect neurological disorders and age-associated conditions in people aged 65 years and over. All participants were Caucasian and selected from three different socioeconomic areas (Margaritas—working class area; Lista—white collar area; Arévalo—rural agricultural area) to obtain a representative sample of older people living in central Spain. All individuals aged 65 years and over from local population registers were eligible if they were residents in the area on December 31, 1993, or during 6 or more months in 1993. In Margaritas and Arévalo, each eligible subject was contacted for screening, whereas a proportionate stratified random sampling was used in Lista to select subjects due to the large number of elderly residents in this area. In this survey, the household and nursing home populations of the three communities were covered, but eligible subjects who had moved out of the survey area were not traced. Two Local Ethics Committee (University Hospitals “12 de Octubre” and “La Princesa,” Madrid) approved NEDICES and written informed consent was obtained from all participants. Detailed accounts of the study population and sampling methods have been published elsewhere (Morales et al., 2004; Bermejo-Pareja et al., 2008a).

The NEDICES study consisted of two cross-sectional waves: 1994–95 (first wave) and 1997–98 (second wave). Of the 5,278 individuals who were assessed at baseline, 3,816 were alive at follow-up (1997–8) and eligible for this study. A total of 283 individuals who had received the diagnosis of dementia and 789 participants with missing data in semantic VFTs were excluded from further analyses. Thus, 2,744 individuals who completed both VFTs were included in the final sample.

Measures

In the second cohort (1997–8), participants completed a brief neuropsychological battery to assess attention, memory, naming and executive function (Bermejo-Pareja et al., 2008b; Contador, Bermejo-Pareja, Del Ser & Benito-León, 2015). Its clinical validity for dementia and mild cognitive impairment in the population has been published previously (Serna et al., 2015). In this battery, semantic Fluency Tests were applied based on Isaacs and Kennie's (1973) procedure. Thus, subjects were asked to evoke animals and/or fruits for one minute, assigning one point per correct response. Higher scores represent better performance.

Procedure

Essentially, the study was developed in two phases: door-to-door screening of eligible people (Phase 1) and neurological examination of those individuals who screened positive (Phase 2). At baseline (1994–5), participants were initially assessed using a 500-item screening questionnaire to collect data on demographics, medical conditions and current medication. In addition, a World Health Organization screening protocol for dementia (Amaducci et al., 1991; Baldereschi et al., 1994), including the MMSE-37 and the Spanish version of Pfeffer's Functional Activities Questionnaire (FAQ; Olazarán, Mouronte & Bermejo, 2005; Prieto et al., 2012) were applied. The screening was considered positive if: (i) the subject scored <24 points on the MMSE-37 and >5 points on the Pfeffer FAQ; (ii) the participants themselves or through their proxy provided information about suspicion of cognitive decline; and (iii) there were missing values (i.e., the subject failed to provide an answer, or information was not available) on the screening instruments. Participants who screened positive for dementia underwent a neurological examination at a National Health Service clinic or in their own homes for the diagnosis of neurological diseases. Basically, this examination included a clinical history, a general neurological examination, and a mental status interview. The diagnosis of dementia was made by consensus of two expert neurologists using the Diagnostic and Statistical Manual of Mental Disorders IV criteria (DSM-IV) as gold standard. All medical records of all participants who received a diagnosis of dementia were reviewed by a senior neurologist (F.B.-P.) with the aid of a psychologist (F.S.-S.). If there were doubts about any aspect of the dementia diagnosis, additional information (mainly from family doctors) was elicited. A similar procedure was carried out in both waves of the study for the incident cases of dementia. In the second cohort (1997–8), regardless of clinical diagnosis, participants underwent the brief neuropsychological battery mentioned earlier.

Data Analysis

The results of the study were analyzed with the Statistical Package for Social Sciences (SPSS) version 22 (IBM®, SPSS Statistics version 22). Means and standard deviations were calculated for clinical and sociodemographic characteristics of the sample. Because the distribution of scores in both tests was asymmetrical and leptokurtic, Spearman correlations between semantic VFTs (animals and fruits) and sociodemographic variables (age, sex and educational level) were used to determine the association between pairs of variables, whereas the Mann–Whitney U-contrast was performed to test significant differences between pairs of groups. Moreover, stratified percentile ranks were calculated (1, 2, 5, 10, 15, 25, 50, 75, 84, 95 and 99) according to the sociodemographic variables that were significantly related to the category fluency tasks. These percentiles correspond to the Z-scores −2.33, −2.00, −1.65, −1.28, −1.04, −0.67, 0.00, 0.66, 1.0, 1.65 and 2.33, respectively. The percentiles can be interpreted as follows: 0–2 = impaired or defective performance; 3–9 = borderline performance; 10–25 = low average; 26–74 = average (Howieson & Lezak, 2010). To interpret a VFT score, the evaluators selects the table with the midpoint age nearest to the age of the assessed participant.

Following the procedure described by Pauker (1988), tables with overlapping standard intervals were used to maximize the available data in each extract and its clinical utility. These methods of overlapping intervals are frequently used in the literature, particularly in normative studies in which age and education effects are present (Lavoie et al., 2013). Basically, this method consists of creating relatively large, adjacent and partially overlapping intervals that collect the same subject in more than one interval to obtain normative data. We estimated each normative value from a 14-year age range (i.e., ±7 years from the midpoint age) for each cell, whereas the youngest and oldest groups had ranges of 11 and 19 years, respectively. Each of these ranges encompassed all ages closest to a given midpoint age. Thus, four tables of overlapping intervals with midpoints set at five-year intervals were developed (72, 77, 82 and 89 years old). Each midpoint age provides normative data for that age ± 2 years except in extreme cases (i.e., below 75 years old and subjects 85 years old or older). For instance, if we take a midpoint age of 82 years, all participants included are aged from 75 to 89 years (±7 years from the midpoint age). Consequently, the normative scores of the age groups of 67–77, 70–84, 75–89, and 80–98 years were used to calculate specific data for people ranging in age 67–74, 75–79, 80–84 and 85 or more, respectively.

Results

Characteristics of the Sample

In comparison with the subjects who were excluded because they did not complete the VFS, the eligible sample obtained a better cognitive performance (30.03 ± 4.75 vs. 28.10 ± 6.45 vs., p < 0.001) and included a lower proportion of illiterate people (15.4% vs. 10.5%, p < 0.001) but significant differences emerged in terms of age and sex. The characteristics of the sample (N = 2,744) are presented in Table 1. In terms of sex, women were older (U = 968.50, p = 0.028) and less educated than men (χ2 = 63.5, p < 0.001).

Table 1.

Demographic characteristics of the sample

 Total sample Age ranges 
 67–98 67–74 75–79 80–84 85+ 
N 2744 1908 (69.5) 2078 (75.7) 1248 (45.5) 633 (23.1) 
Age (midpoint) 82.5 72 77 82 89 
Age 75.3 ± 5.9 72.1 ± 2.8 75.3 ± 4.1 79.8 ± 3.9 84.08 ± 3.54 
Sex      
 Men 1183 (43.1) 843 (44.2) 888 (42.8) 511 (41.0) 257 (40.6) 
 Woman 1561 (56.9) 1065 (55.8) 1190 (57.2) 737 (59.0) 376 (59.4) 
Education      
 Illiteracy 275 (10.0) 183 (9.6) 198 (9.5) 136 (10.9) 75 (11.8) 
 Read & write 1156 (42.1) 796 (41.7) 855 (41.1) 521 (41.7) 273 (43.1) 
 Primary school 924 (33.7) 663 (34.7) 734 (35.3) 423 (39.9) 188 (29.7) 
 Secondary or higher 389 (14.2) 266 (13.9) 291 (14.0) 168 (13.5) 97 (15.3) 
Living area      
 Lista 842 (30.7) 528 (27.7) 644 (31.0) 421 (33.7) 244 (38.5) 
 Arévalo 1068 (39.8) 780 (40.9) 844 (40.6) 465 (37.2) 217 (34.3) 
 Margaritas 834 (30.4) 600 (31.4) 590 (28.4) 362 (29.1) 172 (27.2) 
MMSE-37 29.76 ± 5.05 30.3 ± 4.8 29.8 ± 5.0 28.9 ± 5.2 28.18 ± 5.52 
 Total sample Age ranges 
 67–98 67–74 75–79 80–84 85+ 
N 2744 1908 (69.5) 2078 (75.7) 1248 (45.5) 633 (23.1) 
Age (midpoint) 82.5 72 77 82 89 
Age 75.3 ± 5.9 72.1 ± 2.8 75.3 ± 4.1 79.8 ± 3.9 84.08 ± 3.54 
Sex      
 Men 1183 (43.1) 843 (44.2) 888 (42.8) 511 (41.0) 257 (40.6) 
 Woman 1561 (56.9) 1065 (55.8) 1190 (57.2) 737 (59.0) 376 (59.4) 
Education      
 Illiteracy 275 (10.0) 183 (9.6) 198 (9.5) 136 (10.9) 75 (11.8) 
 Read & write 1156 (42.1) 796 (41.7) 855 (41.1) 521 (41.7) 273 (43.1) 
 Primary school 924 (33.7) 663 (34.7) 734 (35.3) 423 (39.9) 188 (29.7) 
 Secondary or higher 389 (14.2) 266 (13.9) 291 (14.0) 168 (13.5) 97 (15.3) 
Living area      
 Lista 842 (30.7) 528 (27.7) 644 (31.0) 421 (33.7) 244 (38.5) 
 Arévalo 1068 (39.8) 780 (40.9) 844 (40.6) 465 (37.2) 217 (34.3) 
 Margaritas 834 (30.4) 600 (31.4) 590 (28.4) 362 (29.1) 172 (27.2) 
MMSE-37 29.76 ± 5.05 30.3 ± 4.8 29.8 ± 5.0 28.9 ± 5.2 28.18 ± 5.52 

Notes: Values are frequencies (%) or mean (M) ± standard deviation (SD); MMSE-37 = Mini-Mental State Examination

Effect of Age, Sex and Education on Verbal Fluency Scores

Total raw mean scores obtained by the entire sample were 13.3 (SD = 4.4) for Animals and 10.3 (SD = 3.0) for Fruits. Table 2 shows the mean raw scores stratified by age, sex and educational level.

Table 2.

Participants performance on animals and fruits verbal fluency: stratification by age, sex and education (N = 2744)

 Animals verbal fluency Fruits verbal fluency 
 Age range Age range 
 Total score 67–74 75–79 80–84 85+ Total score 67–74 75–79 80–84 85+ 
 13.3 (4.4) n = 1,908 = 2,078 n = 1,248 = 633 10.3(3.0) n = 1,908 n = 2,078 n = 1,248 = 633 
Education/sex           
Illiteracy           
 Men  13.4 (3.7) 13.4(4.0) 12.4(4.4) 11.4(4.1)  9.13 (2.7) 9.2 (2.4) 8.8 (2.7) 8.5 (3.2) 
 Women  11.0 (4.0) 10.5(3.8) 9.7 (3.7) 9.1 (4.0)  9.22 (2.7) 8.8 (2.5) 8.6 (2.6) 8.3 (2.8) 
Read & write           
 Men  14.0 (4.1) 13.6(4.2) 12.7(4.1) 12.0(4.1)  10.0 (2.7) 9.7 (2.6) 9.1 (2.7) 8.9 (2.7) 
 Women  13.7 (4.0) 12.8(4.1) 11.9(3.8) 11.1(3.7)  11.1 (2.9) 10.5(2.9) 9.9 (2.8) 9.2 (2.9) 
Primary  school           
 Men  13.8 (4.5) 13.5(4.4) 13.4(4.4) 12.9(4.2)  9.9 (2.8) 9.6 (2.9) 9.2(2.9) 8.9(3.0) 
 Women  13.3 (4.1) 12.7(4.1) 12.1(4.0) 11.5(3.8)  11.0 (2.8) 10.5(2.8) 10.0(2.6) 9.4(2.6) 
Secondary or higher           
 Men  17.1 (5.1) 16.6(5.0) 15.2(4.8) 15.2(4.8)  12.1 (3.6) 11.5(3.4) 10.7(3.4) 10.2(3.2) 
 Women  15.2 (4.5) 14.5(4.6) 13.4(4.6) 13.0(4.2)  12.6 (3.1) 12.2(3.1) 11.8(3.1) 11.3(2.8) 
 Animals verbal fluency Fruits verbal fluency 
 Age range Age range 
 Total score 67–74 75–79 80–84 85+ Total score 67–74 75–79 80–84 85+ 
 13.3 (4.4) n = 1,908 = 2,078 n = 1,248 = 633 10.3(3.0) n = 1,908 n = 2,078 n = 1,248 = 633 
Education/sex           
Illiteracy           
 Men  13.4 (3.7) 13.4(4.0) 12.4(4.4) 11.4(4.1)  9.13 (2.7) 9.2 (2.4) 8.8 (2.7) 8.5 (3.2) 
 Women  11.0 (4.0) 10.5(3.8) 9.7 (3.7) 9.1 (4.0)  9.22 (2.7) 8.8 (2.5) 8.6 (2.6) 8.3 (2.8) 
Read & write           
 Men  14.0 (4.1) 13.6(4.2) 12.7(4.1) 12.0(4.1)  10.0 (2.7) 9.7 (2.6) 9.1 (2.7) 8.9 (2.7) 
 Women  13.7 (4.0) 12.8(4.1) 11.9(3.8) 11.1(3.7)  11.1 (2.9) 10.5(2.9) 9.9 (2.8) 9.2 (2.9) 
Primary  school           
 Men  13.8 (4.5) 13.5(4.4) 13.4(4.4) 12.9(4.2)  9.9 (2.8) 9.6 (2.9) 9.2(2.9) 8.9(3.0) 
 Women  13.3 (4.1) 12.7(4.1) 12.1(4.0) 11.5(3.8)  11.0 (2.8) 10.5(2.8) 10.0(2.6) 9.4(2.6) 
Secondary or higher           
 Men  17.1 (5.1) 16.6(5.0) 15.2(4.8) 15.2(4.8)  12.1 (3.6) 11.5(3.4) 10.7(3.4) 10.2(3.2) 
 Women  15.2 (4.5) 14.5(4.6) 13.4(4.6) 13.0(4.2)  12.6 (3.1) 12.2(3.1) 11.8(3.1) 11.3(2.8) 

Notes: Values are means and standard deviation (between parenthesis).

The statistical analyses revealed that the variables age (r = −0.232, p < 0.001) and education (r = 0.217, p < 0.001) were significantly correlated with the animal fluency score. Similarly, the fruit fluency scores were significantly correlated with age (r = −0.238, p < 0.001) and education (r = 0.203, p < 0.001). The adjusted partial correlation indicates that sex had a minor but significant influence on both VFTs after controlling for the effect of education and age [animal fluency (r = −0.131, p = 0.001) and fruit fluency (r = 0.109, p < 0.001)]. These results indicate that a correct interpretation of both semantic VFTs requires consideration of age, sex and education.

Normative Date

The mean, median, standard deviation and percentiles ranks for each VFTs are shown in Tables 36 for the five age groups, stratified by sex and educational level: 67–74, 75–79, 80–84 and, 85 or more years old. The age range and sample size used to create norms for each group are provided above each table.

Table 3.

Normative data for the animals and fruits fluency task estimated from the ages 67–77 years and used for the ages 67–74 years (= 1908)

 Men Women 
Education Illiteracy Read & write Primary Secondary/higher Illiteracy Read & write Primary Secondary/ higher 
n 48 357 289 149 135 439 374 117 
 Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits 
M (Mdn) 13.4 (14) 9.1(9) 14.0(14) 10.0 (10) 13.8(13) 9.9 (10) 17.1(17) 12.1 (12) 11 (11) 9.2(9) 13.7(13) 11.1 (11) 13.3(13) 11.0 (11) 15.2(15) 12.61(13) 
SD 3.7 2.7 4.1 2.7 4.5 2.8 5.1 3.6 2.7 4.0 2.9 4.1 2.8 4.5 3.12 
Percentile                 
 1st 
 2nd 
 5th 
 10th 10 
 16th 10 10 10 12 10 10 
 25th 11 11 11 13 10 11 10 12 11 
 50th 14 14 10 13 10 17 12 11 13 11 13 11 15 13 
 75th 16 11 16 12 16 11 21 14 14 11 16 13 16 13 18 15 
 84th 17 12 18 13 18 13 22 15 15 12 18 14 17 14 20 15 
 95th 20 14 21 15 22 15 26 18 17 14 21 16 20 16 22 18 
 99th 21 15 26 17 26 17 32 26 21 17 25 20 24 18 29 22 
 Men Women 
Education Illiteracy Read & write Primary Secondary/higher Illiteracy Read & write Primary Secondary/ higher 
n 48 357 289 149 135 439 374 117 
 Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits 
M (Mdn) 13.4 (14) 9.1(9) 14.0(14) 10.0 (10) 13.8(13) 9.9 (10) 17.1(17) 12.1 (12) 11 (11) 9.2(9) 13.7(13) 11.1 (11) 13.3(13) 11.0 (11) 15.2(15) 12.61(13) 
SD 3.7 2.7 4.1 2.7 4.5 2.8 5.1 3.6 2.7 4.0 2.9 4.1 2.8 4.5 3.12 
Percentile                 
 1st 
 2nd 
 5th 
 10th 10 
 16th 10 10 10 12 10 10 
 25th 11 11 11 13 10 11 10 12 11 
 50th 14 14 10 13 10 17 12 11 13 11 13 11 15 13 
 75th 16 11 16 12 16 11 21 14 14 11 16 13 16 13 18 15 
 84th 17 12 18 13 18 13 22 15 15 12 18 14 17 14 20 15 
 95th 20 14 21 15 22 15 26 18 17 14 21 16 20 16 22 18 
 99th 21 15 26 17 26 17 32 26 21 17 25 20 24 18 29 22 

Notes: M = mean; Mdn = median; SD = standard deviation

Table 4.

Normative data for the Animals and Fruits fluency task estimated from the ages 70–84 years and used for the ages 75–79 years (n = 2078)

 Men Women 
Education Illiteracy Read & write Primary Secondary or higher Illiteracy Read & write Primary Secondary or higher 
n 48 373 304 163 150 482 430 128 
 Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits 
M (Mdn) 13.4 (13) 9.2 (9) 13.6 (13 ) 9.7 (10) 13.5 (13) 9.6 (10) 16.6 (17) 11.5 (12) 10.5 (10) 8.8 (9) 12.8 (12) 10.5 (10) 12.7 (12) 10.5 (10) 14.5 (15) 12.2 (12.5) 
SD (4.0) (2.4) (4.2) (2.6) (4.4) (2.9) (5.0) (3.4) (3.8) (2.5) (4.1) (2.9) (4.1) (2.8) (4.6) (3.1) 
Percentile                 
 1st 
 2nd 
 5th 
 10th 10 
 16th 10 10 12 10 
 25th 10 11 11 13 10 10 11 10 
 50th 13 13 10 13 10 17 12 10 12 10 12 10 15 13 
 75th 17 11 16 11 16 11 20 14 13 10 15 12 15 12 17 14 
 84th 18 12 18 12 17 12 21 15 15 11 17 13 17 13 19 15 
 95th 21 13 21 14 22 15 25 17 17 13 20 15 20 15 22 18 
 99th   26 15 27 18 29 19 21 15 25 18 24 18 29 22 
 Men Women 
Education Illiteracy Read & write Primary Secondary or higher Illiteracy Read & write Primary Secondary or higher 
n 48 373 304 163 150 482 430 128 
 Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits 
M (Mdn) 13.4 (13) 9.2 (9) 13.6 (13 ) 9.7 (10) 13.5 (13) 9.6 (10) 16.6 (17) 11.5 (12) 10.5 (10) 8.8 (9) 12.8 (12) 10.5 (10) 12.7 (12) 10.5 (10) 14.5 (15) 12.2 (12.5) 
SD (4.0) (2.4) (4.2) (2.6) (4.4) (2.9) (5.0) (3.4) (3.8) (2.5) (4.1) (2.9) (4.1) (2.8) (4.6) (3.1) 
Percentile                 
 1st 
 2nd 
 5th 
 10th 10 
 16th 10 10 12 10 
 25th 10 11 11 13 10 10 11 10 
 50th 13 13 10 13 10 17 12 10 12 10 12 10 15 13 
 75th 17 11 16 11 16 11 20 14 13 10 15 12 15 12 17 14 
 84th 18 12 18 12 17 12 21 15 15 11 17 13 17 13 19 15 
 95th 21 13 21 14 22 15 25 17 17 13 20 15 20 15 22 18 
 99th   26 15 27 18 29 19 21 15 25 18 24 18 29 22 

Notes: M = mean; Mdn = median; SD = standard deviation

Table 5.

Normative data for the animals and fruits fluency task estimated from the ages 75–89 years and used for the ages 80–84 years (n = 1248)

 Men Women 
Education Illiteracy Read & write Primary Secondary or higher Illiteracy Read & write Primary Secondary or higher 
n 32 216 174 89 104 305 249 79 
 Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits 
M (Mdn) 12.4 (12) 8.8 (8.5) 12.7 (12) 9.1 (9.0) 13.4 (13) 9.2 (9) 15.2 (16) 10.7 (11) 9.7 (9) 8.6 (8) 11.8 (12) 9.9 (9) 12.1 (12) 10.0 (10) 13.4 (13) 11.8 (11) 
SD (4.4) (2.7) (4.1) (2.7) (4.4) (2.9) (4.8) (3.4) (3.7) (2.6) (3.8) (2.8) (4.0) (2.6) (4.6) (3.1) 
Percentile                 
 1st 
 2nd 
 5th 
 10th 
 16th 8,7 10 
 25th 10 10 12 10 
 50th 12 12 13 16 11 12 12 10 13 11 
 75th 16 11 15 11 16 11 18 13 12 10 14 12 15 11 16 14 
 84th 18 11 17 12 17 12 20 14 13 11 16 13 16 13 18 15 
 95th 20 14 19 14 22 14 23 17 17 14 19 15 19 14 22 17 
 99th   24 15 28 18   23 17 23 17 23 16   
 Men Women 
Education Illiteracy Read & write Primary Secondary or higher Illiteracy Read & write Primary Secondary or higher 
n 32 216 174 89 104 305 249 79 
 Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits 
M (Mdn) 12.4 (12) 8.8 (8.5) 12.7 (12) 9.1 (9.0) 13.4 (13) 9.2 (9) 15.2 (16) 10.7 (11) 9.7 (9) 8.6 (8) 11.8 (12) 9.9 (9) 12.1 (12) 10.0 (10) 13.4 (13) 11.8 (11) 
SD (4.4) (2.7) (4.1) (2.7) (4.4) (2.9) (4.8) (3.4) (3.7) (2.6) (3.8) (2.8) (4.0) (2.6) (4.6) (3.1) 
Percentile                 
 1st 
 2nd 
 5th 
 10th 
 16th 8,7 10 
 25th 10 10 12 10 
 50th 12 12 13 16 11 12 12 10 13 11 
 75th 16 11 15 11 16 11 18 13 12 10 14 12 15 11 16 14 
 84th 18 11 17 12 17 12 20 14 13 11 16 13 16 13 18 15 
 95th 20 14 19 14 22 14 23 17 17 14 19 15 19 14 22 17 
 99th   24 15 28 18   23 17 23 17 23 16   

Notes: M = mean; Mdn = median; SD = standard deviation

Table 6.

Normative data for the animals and fruits fluency task estimated from the ages 80–98 years and used for the ages ≥85 years (n = 633)

 Men Women 
Education Illiteracy Read & write Primary Secondary or higher Illiteracy Read & write Primary Secondary or higher 
n 17 113 74 52 58 160 114 44 
 Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits 
M (Mdn) 11.4 (10) 8.5 (8) 12.0 (12) 8.9 (9) 12.9 (12) 8.9 (8) 15.3 (16) 10.3 (10) 9.1 (8.5) 8.3 (7.5) 11.1 (11) 9.2 (9) 11.5 (11) 9.4 (9) 13.0 (12) 11.0 (11) 
SD (4.8) (3.2) (4.1) (2.7) (4.2) (3.0) (4.7) (3.2) (4.0) (2.8) (3.7) (2.8) (3.8) (2.6) (4.2) (3.0) 
Percentile                 
 1st 
 2nd 
 5th 
 10th 
 16th 10 
 25th 10 13 10 
 50th 10 12 12 16 10 11 11 12 11 
 75th 15 11 15 11 15 11 18 12 11 10 13 11 14 11 16 13 
 84th 18 11 16 12 16 12 20 14 13 11 15 12 15 12 16 15 
 95th   19 14 22 14 23 17 17 14 18 15 19 14 22 16 
 99th   25 17       21 16 22 18  
 Men Women 
Education Illiteracy Read & write Primary Secondary or higher Illiteracy Read & write Primary Secondary or higher 
n 17 113 74 52 58 160 114 44 
 Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits Animals Fruits 
M (Mdn) 11.4 (10) 8.5 (8) 12.0 (12) 8.9 (9) 12.9 (12) 8.9 (8) 15.3 (16) 10.3 (10) 9.1 (8.5) 8.3 (7.5) 11.1 (11) 9.2 (9) 11.5 (11) 9.4 (9) 13.0 (12) 11.0 (11) 
SD (4.8) (3.2) (4.1) (2.7) (4.2) (3.0) (4.7) (3.2) (4.0) (2.8) (3.7) (2.8) (3.8) (2.6) (4.2) (3.0) 
Percentile                 
 1st 
 2nd 
 5th 
 10th 
 16th 10 
 25th 10 13 10 
 50th 10 12 12 16 10 11 11 12 11 
 75th 15 11 15 11 15 11 18 12 11 10 13 11 14 11 16 13 
 84th 18 11 16 12 16 12 20 14 13 11 15 12 15 12 16 15 
 95th   19 14 22 14 23 17 17 14 18 15 19 14 22 16 
 99th   25 17       21 16 22 18  

Notes: M = mean; Mdn = median; SD = standard deviation.

Discussion

The objective of the present study was to provide normative data (stratified by age, sex and education) for two semantic VFTs in older Spaniards (aged 65 years and over) without dementia. Previous studies have reported normative data for VFTs in Spain (Benito-Cuadrado et al., 2002; Peña-Casanova et al., 2009), but we collected a larger and representative population-based sample exclusively composed of older adults who lived in urban (Margaritas & Lista) and rural areas (Arévalo). As we have previously claimed, these conditions and the selection procedure (see methods) support the validity of the data (Contador et al., 2016a).

Similar to previous studies (González et al., 2005), we found that performance on VFTs was significantly influenced by sociodemographic factors (i.e., age, education and sex). Thus, participants with low education had a poorer performance in both semantic VFTs (Benito-Cuadrado et al., 2002), and older individuals also obtained lower scores (Lozano & Ostrosky-Solis, 2006; Peña-Casanova et al., 2009). It is well-known that greater age and lower education are consistently associated with poorer performance in different cognitive domains (Mitrushina, Boone, Razani & D'Elia, 2005; Ganguli et al., 2010). These effects are basically explained by age- and education-related changes in the brain and cognitive efficacy (Glisky, 2007; Ardila et al., 2010). In addition, sex was significantly associated with both VFT scores, even after adjusting for age and education. Intriguingly, we found a significant advantage for women in fruits but men had a better performance in animals. Likewise, Peña-Casanova et al. (2009) described a similar women advantage in generating fruits names, whereas Capitani, Laiacona and Barbarotto (1999) found than men performed better at naming tools. Possibly, socio-cultural aspects related to semantic knowledge (e.g., women's greater involvement in housework vs. men's in hunting) could explain this dissociation, but other factors such as literacy or socioeconomic status may also influence VF scores (Contador et al., 2015). In fact, differences on fruits are diminished or inverted when comparing men to women in the illiterates’ stratum, perhaps because this category is more specifically related to everyday life (e.g., food) and many of the participants were landowners. Likewise, discrepancies in literacy skills may potentially arbitrate the more pronounced sex differences in scores at the upper and lower education levels in the animal category task.

There are some limitations of the study that should be mentioned. First, the level of education was usually low because most of the older Spaniards had null or limited access to a qualified education due to difficult socioeconomic conditions (e.g., Spanish Civil War). However, the figures are fairly similar to those obtained in the current older population (Tola-Arribas et al., 2012). Second, some participants could not accurately recall their years of education so they were classified in four levels of education (literacy skills included), obtained from the local registry office in each municipality. We note that literacy is a better estimator of cognitive performance than years of education (Contador et al., 2015, 2016b). Third, the influence of linguistic and cultural factors on VFTs were not analyzed, which limits the possibilities of generalization. Future studies should address the influence of these factors and other potential variables (e.g., income and premorbid intelligence) on VFTs.

Standardized cognitive measures are required to accurately measure the presence of cognitive impairment in older adults, especially in individuals with a low level of education. This study provides reliable data for semantic VFTs in a large and diverse sample of older Spanish adults. Our findings indicate that correct interpretation of semantic VFTs requires the consideration of variables such as education, age and sex. These norms may be a useful resource for clinicians to improve detection of patients with mild cognitive impairment and AD at earlier stages.

Funding

The NEDICES study was supported through grants from the World Health Organization Age-Associated Dementia Project (WHO-AAD), the EPICARDIAN study (PB1225-C04), the official Spanish Research Agencies (FIS 93/0773; 96/1993; 00-0011-01; CAM 94/0032) and the Spanish Office of Science and Technology (PB 1225-C04). Detailed information about collaborators and institutions are available on www.ciberned.es/estudio-nedices.

Conflict of Interest

Nothing to declare.

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