Trail Making Test: Normative data for the Latin American Spanish-speaking pediatric population

2.1Participants

The sample consisted of 3,337 healthy children who were recruited from Chile, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, Puerto Rico, and Spain. Participants were selected according to the following criteria: a) were between 6 and 14 years of age, b) were born and currently lived in a country where the study was conducted, c) spoke Spanish as their mother tongue, d) an IQ ≥80 on the Test Of Non-verbal Intelligence (TONI-2, Brown, Sherbenou, & Johnsen, 2009), and e) scored <19 on the Children’s Depression Inventory (CDI, Kovacs, 1992).

Children with history of neurologic or psychiatric disorders as reported by the participant’s parent(s) were excluded due to its effects on cognitive performance. Participants in the study were from public and private schools, and signed an informed consent. Table 1 presents socio-demographic and participant characteristics for each country. Ethics Committee approval was obtained for the study in each country.

2.2Instrument administration

The Trail Making Test (TMT) provides information on visual attention, motor speed, cognitive flexibility and task alternation (Tombaugh, 2004). The test consists of two parts in which the task is to connect randomly distributed points. In part A, all objectives are numbers (1 to 15) and the participant must join them up (1-2-3 …), while in part B, must alternate between numbers and letters of the next lever (1-A, 2-B, 3-C…). The total score is the number of seconds that the participant needs to complete the task (Reitan, 1992), with a time limit of 60 and 150 seconds for TMT-A and TMT-B, respectively.

2.3Statistical analyses

Detailed statistical analyses used to generate the normative data for the TMT-A and TMT-B scores (measured in time) are described in Rivera & Arango-Lasprilla (2017). In summary, the scores were standardized using multiple linear regression analyses by means of a four-step procedure. 1) First, the TMT-A and TMT-B scores were computed separately by means of the final multiple regression models. The full regression models included as predictors: age, age², sex, and mean level of parental education (MLPE). Age was centered (= calendar age – mean age in the sample by country) before computing the quadratic age term to avoid multicollinearity (Aiken & West, 1991). Sex was coded as male = 1 and female = 0. The MLPE variable was coded as 1 if the participant’s parent(s) had >12 years of education or 0 if participant’s parent(s) had ≤12 years of education. If predicted variables were not statistically significant in the multivariate model with an alpha of 0.05, the non-significant variables were removed and the model was run again. A final regression model was conducted yˆi=B0+B1·(Age-x¯Agebycountry)i+B2·(Age-x¯Agebycountry)i2+B3·Sexi+B4·MLPEi . 2) Residual scores were calculated based on the final model ( ei=yi-yˆi ). 3) Residuals were standardized using the residual Standard Deviation (SD_e) value provided by the regression model: z_i = e_i/SD_e. 4) Standardized residuals were converted to percentile values using the standard normal cumulative distribution function. This four-step process was applied for TMT-A and TMT-B scores separately for each country.

For all multiple linear regression models, the following assumptions were evaluated: a) multicollinearity by the values of the Variance Inflation Factor (VIF), which must not exceed 10, and the collinearity tolerance values, which must not exceed the value of 1 (Kutner, Nachtsheim, Neter, & Li, 2005), and b) the existence of influential values by calculating the Cook’s distance. The maximum Cook’s distance value was related to a F (p, n - p) distribution. Influential values are considered when percentile value is equal or higher than 50 (Cook, 1977; Kutner et al., 2005). All analyzes were performed using SPSS version 23 (IBM Corp., Armonk, NY).

3.1TMT-A

The final multivariate linear regression models for the ten country-specific TMT-A scores were significant (see Table 2). In all countries, the TMT-A scores were affected by age so that children needed less time to complete the test while they become older. The TMT-A score for all countries except, Cuba, Guatemala, and Puerto Rico was affected by a quadratic age effect. Children from Mexico and Paraguay whose parent(s) had a MLPE >12 years of education needed less time to complete TMT-A test than children whose parent(s) had a MLPE ≤12 years. The child’s sex affected TMT-A score for Chile, Cuba, Mexico, and Peru, such that boys needed less time to complete the task than girls. The amount of variance these predictors explained in the TMT-A score ranged from 11.4% (in Cuba) to 39.4% (in Ecuador).

3.2TMT-B

The final multivariate linear regression models for the ten country-specific TMT-B scores were significant (see Table 3). TMT-B scores were affected by age so that children needed less time to complete the test while they become older. The TMT-B scores for all countries except for Guatemala and Puerto Rico were affected by a quadratic age effect. Children from Ecuador, Mexico, Paraguay, and Spain whose parents had a MLPE >12 years of education needed less time to complete TMT-B test than children whose parents had a MLPE ≤12 years of education. The child’s sex did not affect TMT-B score for any country. The amount of variance these predictors explained in the TMT-B scores ranged from 8.9% (in Cuba) to 48.6% (in Spain).

The assumptions of multiple linear regression analysis were met for all final models. There was not multicollinearity (the VIF values were below 10; VIF ≤1.051; collinearity tolerance values did not exceed the value of 1) or influential cases (the maximum Cook’s distance value was 0.094 in a F _(2,146)  distribution which correspond to percentile 9).

3.3Normative procedure

Norms (e.g., a percentile score) for the different TMT scores by country were established using the four-step procedure described in the statistical analysis section. An example will be provided to facilitate an improved understanding of the procedure used to obtain the percentile associated with a score on this test. Let’s assume we need to find the percentile score for a 7-year-old Peruvian girl who scored 30 seconds on the TMT-A and whose parent(s) have a mean of 12 years of education (MLPE). The steps to obtain the percentile for this score are: 1) Find Peru in Table 2, which provides the final regression models by country for the TMT-A scores. Use the B weights to create an equation that will allow you to obtain the predicted TMT-A score for this child using the coding provided in the statistical analysis section. The corresponding B weights are multiplied by the centered age (= calendar age – mean age in the Peruvian sample which is equal to 10.5 years), centered age² (= calendar age – mean age in the Peruvian sample which is equal to 10.5 years)², and sex, which was coded as male = 1 and female = 0. MLPE was not a significant predictor, and therefore is not included in this model. See Table 1 the mean age of each country’s sample. Finally, the result is added to the constant generated by the model in order to calculate the predicted value.

In the case of the Peruvian girl, the predicted TMT-A score would be calculated using the following equation: yˆi=28.479+[-1.190·(Agei-10.5)]+[0.447·(Agei-10.5)2]+[(-3.016)·Sex] . The girl’s age is 7. Sex was coded as male = 1 and female = 0, so in this case as the child is a female, the sex value is 0. Thus, the predicted value equation is: yˆi=28.479+[-1.190·(7-10.5)]+[0.447·(7-10.5)2]+(-3.016·0)=38.116 . 2) In order to calculate the residual value (indicated with an e_i in the equation), we subtract the actual TMT-A score (she scored 30) from the predicted value we just calculated (ei=yi-yˆi) . In this case, it would be e_i = 30 - 38.116 = -8.116. 3) Next, consult the SD _e column in Table 2 to obtain the country-specific SD _e (residual) value. For Peru it is 11.798. Using this value, we can transform the residual value to a standardized z score using the equation z_i = e_i/SD_e. In this case, we have -8.116/11.798 = -0.688. This is the standardized z score for a 7-year-old Peruvian girl who scored a 30 on the TMT-A who has parent(s) with 12 years of education (MLPE). 4) The last step is to use the tables available in most statistical reference books (e.g., Strauss et al., 2006) or use a trusted online calculator like the one available at http://www.measuringu.com/pcalcz/. In the online calculator, you would enter the z score and choose a one sided test and note the 100-percent of area after hitting the submit button to convert z scores to percentiles. In this example, the z score (probability) of -0.688 corresponds to the 75th percentile. It is important to remember to use the appropriate tables that correspond to each test (TMT-A and TMT-B) when performing these calculations.

3.4User-friendly normative data

The four-step normative procedures explained above offers the clinician the ability to determine an exact percentile for a child who has a specific score on the TMT. However, this method can be prone to human error due to the number of required computations by hand. To enhance user-friendliness, the authors have completed these steps for a range of raw scores based on age, sex, and MLPE and created tables for clinicians to more easily obtain a percentile range/estimate associated with a given raw score on this test. These tables are available by country and type of test in the Appendix. In order to obtain an approximate percentile for the above example (converting a raw score of 30 seconds on the TMT-A for a Peruvian girl who is 7 years old and whose parent(s) have 12 years of education) using the simplified normative tables provided in the Appendix, the following steps must be followed. (1) First, identify the appropriate table ensuring the appropriate country and test (TMT-A and TMT-B). In this case, the table for TMT-A score for girls from Peru can be found in Table A12. (2) Find the appropriate age of the child, in this case, 7 years old. (3) Next, look in the 7 years’ age column to find the approximate location of the raw score obtained on the test. Within the 7 years’ column, the score of 30 obtained by this Peruvian girl corresponds to an approximate percentile of 70.

The percentile obtained using this user-friendly table sometimes could be slightly different than the hand-calculated, more accurate method (75^th vs. 70^th) because the user-friendly table is based on a limited number of percentile values. Individual percentiles cannot be presented in these tables due to space limitations. If the exact score is not listed in the column, you must estimate the percentile value from the list of raw scores available.

4.1Implications

One of the most worrying aspects in the professional practice of neuropsychology in Latin American countries and Spain is the lack of neuropsychological tests that have been validated and standardized in Spanish (Arango-Lasprilla et al., 2016; Olabarrieta-Landa et al., 2016). For this reason, many neuropsychologist in these countries use norms and tests created in other countries. All this leads to inadequate diagnosis due to socio-demographic and cultural characteristics of the population in which the test is performed. These characteristics are fundamental when interpreting the results (Lezak, 2004; Strauss et al., 2006). Therefore, the results of the present study are very important and will contribute to improve the clinical practice in these countries. This study will allow neuropsychologists to have reliable and valid tools, in addition to normative data by country in order to adequately use in their neuropsychological evaluations of children and adolescents between the ages of 6 and 14.

4.2Limitations

Despite the great importance of this study, as it is the largest in the world on validation and standardization of the TMT with Spanish-speaking populations, the results of the study should be interpreted in the light of the following limitations: This study presents normative data for the TMT for nine countries in Latin America and Spain. For this reason, it is not advisable to use these norms in the pediatric population of those Spanish-speaking countries where the study was not performed. Future studies should be conducted to standardize this test in other Spanish-speaking countries.

Although the norms of the present study could be used by neuropsychologists in other countries to evaluate Spanish-speaking immigrant children from the countries where the sample was collected for this study, they should be used with caution since other variables such as level of acculturation, bilingualism, the number of years living in the country, and so on, could influence performance. In addition, the quality of education of both the child and the parent(s) is another aspect that may influence the cognitive performance of children.

It is very important to keep in mind that no clinical diagnosis should be made based solely on the TMT scores. This test should be integrated as part of a much larger battery that evaluates these processes in more detail. Because there are a limited number of tests and norms in Latin America and Spain to evaluate these processes, more efforts should be made in the future to have other similar tools.

Although the size of the sample was adequate in each of the countries where the study was conducted, it is very important to note that only the sample in Chile, Mexico, Paraguay, Puerto Rico, and Spain was obtained from several regions of the country, while in the remaining countries were collected from only one geographic area. Future studies should expand the sample in other geographical areas of these countries with the objective to be able to have a greater representativeness of the sample.

The children who participated part in the present study had Spanish as their first language. Although Spanish is the first language of the majority of the population in Latin America and Spain, it is important to keep in mind there is a great cultural and linguistic richness to the point that in many of these countries, sometimes the first language of many children may be completely different from Spanish (e.g., Portuguese, Euskera, Catalan, Guaraní, Maya, Quechua). For this reason, caution should be used when using these norms in children whose first language is not Spanish.

Finally, it is important to keep in mind that the present study was performed with normal healthy population. Therefore, future studies should be performed with clinical population to establish the sensitivity and specificity of this test.

4.3Conclusions

The TMT is one of the neuropsychological tests currently used to evaluate attention, psychomotor speed, visual scanning and sequencing, and mental flexibility in children and adolescents in Latin America and Spain. In this study, it was found that older age, higher parental education level and sex influences TMT scores. This is the first study to develop norms for children and adolescents between the ages 6 and 14 for nine countries from Latin America and Spain. The results of this study are expected to contribute to the improvement of quality standards in the neuropsychological evaluation of pediatric population in these countries.