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 Table of Contents  
ORIGINAL ARTICLE
Year : 2020  |  Volume : 21  |  Issue : 1  |  Page : 30-38

A study of cognitive dysfunctions in chronic smokers


1 Assistant Professor, Department of Psychiatry, Kurnool Medical College, Kurnool, Andhra Pradesh, India
2 Assistant Professor, Department of Psychiatry, Sri Venkateswara Medical College, Tirupati, Andhra Pradesh, India
3 Professor, Department of Psychiatry, Sri Venkateswara Medical College, Tirupati, Andhra Pradesh, India

Date of Submission09-Feb-2020
Date of Decision10-Feb-2020
Date of Acceptance29-Mar-2020
Date of Web Publication03-Jul-2020

Correspondence Address:
Dr. Ramya Keerthi Paradesi
Department of Psychiatry, Sri Venkateswara Medical College, Tirupati, Andhra Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/AMH.AMH_11_20

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  Abstract 

Background: Cognition is defined as the mental processes of perception, memory, and information processing. Cigarette smoking could contribute to cognitive impairment through atherosclerotic and hemodynamic processes.
Aims and Objectives: The aims and objectives were to study the cognitive dysfunctions in chronic smokers and to compare cognitive dysfunctions in chronic smokers with nonsmokers who are matched for age, sex, and socioeconomic status.
Settings and Design: This is a cross-sectional study conducted using purposive sampling on participants recruited from the Outpatient Department of Psychiatry, Narayana Medical College and Hospital.
Materials and Methods: The study was conducted on a sample of sixty patients. Out of this, thirty participants who are current smokers were taken as cases and the other thirty participants who are the attenders of the patients who never smoked cigarette were taken as controls. Informed consent was taken from the cases and controls. Brief Cognitive Rating Scales (BCRSs), digit symbol substitution test (DSST), and Trial-Making Test-A and B were used to assess the cognitive functions, and Fagerstrom Test for Nicotine Dependence (FTND) was used to confirm the diagnosis of nicotine dependence.
Statistical Analysis Used: To test the association between the groups, Chi-square test was used. To test the mean difference between the groups, Student's t-test and ANOVA were used. To test the correlation between the scores, Spearman's rank correlation was used.
Results: Smokers when compared to nonsmokers took longer time in the completion of both trail-making tests A and B and gave less correct responses on DSST. Significant impairment in all the five axes of BCRS was observed. Comparison was statistically significant between FTND score, the trail-making tests A and B, BCRS, except axis-ii.
Conclusions: The higher the nicotine dependence, the greater is the chronicity of cigarette smoking, which has significant cognitive dysfunction.

Keywords: Chronic smokers, cognitive dysfunction, nicotine dependence


How to cite this article:
Ekramulla S, Paradesi RK, Nallapaneni NR. A study of cognitive dysfunctions in chronic smokers. Arch Ment Health 2020;21:30-8

How to cite this URL:
Ekramulla S, Paradesi RK, Nallapaneni NR. A study of cognitive dysfunctions in chronic smokers. Arch Ment Health [serial online] 2020 [cited 2020 Oct 24];21:30-8. Available from: https://www.amhonline.org/text.asp?2020/21/1/30/288912


  Introduction Top


Cognition is defined as the mental processes of perception, memory, and information processing, which allows the individual to acquire knowledge, solve problems, and plan for the future. It comprises the mental processes required for day-to-day living and should not be confused with intelligence. Cognitive dysfunction is thus impairment of these processes.[1] According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition,[2] the neurocognitive domains mainly consist of (1) complex attention, (2) executive function, (3) learning and memory, (4) language, (5) perceptual-motor, and (6) social cognition. The “complex attention” includes sustained attention, divided attention, selective attention, and processing speed. The executive functions include planning, decision-making, working memory, responding to feedback/error correction, overriding habits/inhibition, and mental flexibility. Substances of abuse are known to cause neurocognitive dysfunctions as they act on brain. Tobacco is one of them. India's tobacco problem is very complex, with a large use of a variety of smoking forms and an array of smokeless tobacco products. Cigarette smoking has been implicated as a risk factor for multiple diseases, including cancers [3],[4],[5],[6],[7],[8],[9] and atherosclerosis.[10],[11],[12],[13] The exact mechanisms by which smoking accelerates risk for these diseases are unclear, although smoking may promote inflammation and increase the risk of coagulation defects.[14],[15],[16] Cigarette smoking could contribute to vascular dementia and cognitive impairment through atherosclerotic and hemodynamic processes.[17],[18],[19],[20] Conversely, some epidemiologic studies [21],[22],[23] have suggested that cigarette smoking may be associated with a lower risk of Alzheimer's disease in the elderly. Others studies [24],[25] have pointed out, however, that these findings are subject to a survival bias among older smokers, or may be confounded by genetic influences. There are very few studies in India on smoking and cognition. The present study has been chosen because there is very limited data on the cognitive dysfunction in chronic smokers in India. Hence, an attempt is made to study the same in this institution.

Aims and objectives

  1. To study the cognitive dysfunction in chronic smokers
  2. To compare cognitive dysfunction in chronic smokers with nonsmokers who are matched for age, sex, and socioeconomic status.



  Materials and Methods Top


This study was conducted in the outpatient Department (OPD) of Psychiatry, Narayana Medical College and Hospital, Nellore.

Design

The study design used is a cross-sectional study design; all the assessments in this study were carried out only once.

Samples

The sample of patients was obtained from those attending the outpatient clinic, Department of Psychiatry, Narayana Medical College and Hospital, Nellore. The study was conducted on a sample of sixty patients. Out of this, thirty participants who are current smokers with a history of cigarette smoking minimum for the past 1 year were taken as cases and the other thirty participants who are the attenders of the patients who never smoked cigarette were taken as controls.

Sampling method

Purposive sampling was used to derive the sample.

Selection criteria

Inclusion criteria for cases (chronic smokers)

  1. Age between 18 and 65 years
  2. Current smokers with a history of cigarette smoking for a minimum of past 1 year
  3. Patients who are willing to participate in the study
  4. Patients who are literate.


Inclusion criteria for controls (nonsmokers)

  1. Age between 18 and 65 years
  2. People who do not smoke
  3. People who are willing to participate in the study
  4. Patients who are literate.


Exclusion criteria for cases (chronic smokers)

  1. Age <18 years and age >65 years
  2. Patients with neurodegenerative disorder
  3. Poor communicative skills.
  4. Patients with other psychoactive substance use and other psychiatric disorders
  5. Patients who are not willing to participate in the study.


Exclusion criteria for controls (nonsmokers)

  1. Age <18 years and age >65 years
  2. People with neurodegenerative disorder
  3. People with current psychiatric illness
  4. Poor communicative skills
  5. Patients who are not willing for the study
  6. Patients with other psychoactive substance use
  7. Smokers.


The sociodemographic data were individually collected from them. In our study, the Brief Cognitive Rating Scale (BCRS), digit symbol substitution test (DSST), and trial-making test (TMT)-A and B were used to assess the cognitive functions, and Fagerstrom Test for Nicotine Dependence (FTND) was used to confirm the diagnosis of nicotine dependence.

Materials

Brief Cognitive Rating Scale

DSST [26],[27] is abbreviated as BCRS. The BCRS is an assessment tool to be used with the Global Deterioration Scale (GDS). It was developed by Dr. Barry Reisberg. This assessment tool tests five different areas known as axis (four cognitive and one functional). Each axis of the BCRS is scored independently. Each axis is designed to be optimally concordant with the other axes and with the numerically corresponding GDS stage. Consequently, each axis of the BCRS conveys important staging-related information. For the first four axes, the tester will ask a variety of questions to determine the level of impairment. The results of the 5th axis (functioning) are determined primarily by observation. After a score is determined for each axis, the results were added and divided by 5. The answer will result in a stage corresponding on the GDS. For staging, the GDS stage is very closely equivalent to the average score of the BCRS axis.

Digit symbol substitution test

The DSST [26] is a neuropsychological test sensitive to brain damage and is abbreviated as DSST. This is a subtest from the corpus of intelligence tests authored by David Wechsler. The DSST contained in the Wechsler Adult Intelligence Scale is called “Digit Symbol” (WAIS-R), “Digit-Symbol-Coding” (WAIS-III), or most recently, “Coding” (WAIS-IV). Since Wechsler's publication of the Bellevue Intelligence Scale (BIS; Wechsler, 1939), the basic format and concept of this test has changed very little. The test is timed. Various versions from the Wechsler corpus allow 90 or 120 s. The test requires the examinee to transcribe a unique geometric symbol with its corresponding Arabic number. The examinee is initially shown a key containing the numbers from 1 to 9. Under each number, there is a corresponding geometric symbol. The examinee is then shown a series of boxes containing numbers in the top boxes and blank boxes below them. After a short practice trial, they are then asked to copy the corresponding geometric symbol under each number. The raw score is the number of correct items completed within the prescribed time limit. The most obvious application of digit symbol substitution is to measure memory. The test requires memory to remember where each symbol matches a digit. There is also a speed of processing component because very small amount of time is given to enter the correct symbol.

Trial-making tests

Trail-making test [26],[27] has been extensively used in neuropsychological assessment. It was introduced by Partington and Leiter in 1949. It consists of two parts in which the participant is instructed to connect a set of 25 dots as fast as possible while still maintaining accuracy. It can provide information about visual search speed, scanning, speed of processing, mental flexibility, as well as executive functioning. It consists of two parts – A and B. In part A, the circles are numbered 1–25, and the patient should draw lines to connect the numbers in ascending order. In part B, the circles include both numbers (1–13) and letters (A-L), and the patient draws lines to connect the circles in an ascending pattern, but with the added task of alternating between the numbers and letters (i.e., 1-A-2-B-3-C, etc.). Part A is used primarily to examine cognitive processing speed, and part B is used to examine executive functioning. The time taken to complete the test is used as the primary performance metric. Error rate is not recorded in the paper and pencil version of the test, however, it is assumed that if errors are made, it will be reflected in the completion time.

Fagerström Test for Nicotine Dependence

The FTND [28] is a standard instrument for assessing the intensity of physical addiction to nicotine. The test was designed to provide an ordinal measure of nicotine dependence related to cigarette smoking. It contains six items that evaluate the quantity of cigarette consumption, the compulsion to use, and dependence. The Fagerström Tolerance Questionnaire was developed by Karl–Olov Fagerström in 1978. This instrument was modified to the FTND by Heatherton et al. in 1991. The FTND has a sensitivity of 0.75 and a specificity of 0.80. Further, FTND has a reliability index of 0.72. In scoring the FTND, yes/no items are scored from 0 to 1 and multiple-choice items are scored from 0 to 3. The items are summed to yield a total score of 0–10. The higher the total Fagerström score, the more intense is the patient's physical dependence on nicotine.

Methodology

After getting the approval of the institutional ethics committee, the study was commenced. All patients fulfilling the selection criteria were approached and explained about the purpose of the study. Written informed consent was obtained from all potential participants.

The study was conducted on a sample of sixty patients. Out of this, thirty participants who are current smokers were taken as cases and the other thirty participants who are the attenders of the patients who never smoked cigarette were taken as controls. The BCRSs, DSST, and TMT A and B were used to assess the cognitive functions, and FTND was used to confirm the diagnosis of nicotine dependence.

Statistical analysis

The data were entered into MS-Excel, and statistical analysis was done by using IBM SPSS Version 20.0 (Year 2019 manufactured, New York, Armonk, United states). For categorical variables, the values were represented as number and percentages. To test the association between the groups, Chi-square test was used. For continuous variables, the values were represented as mean and standard deviation. To test the mean difference between the two groups, Student's t-test (independent/paired) was used and to test the mean difference between three or more groups, ANOVA (analysis of variance) with post hoc test was used. To test the correlation between the scores, Spearman's rank correlation was used. P < 0.05 was considered statistically significant.


  Results Top


[Table 1] shows the sociodemographic variables of smokers and nonsmokers. From the table, it is clear that majority of the participants were educated up to primary school, unemployed, having income < 1865 per month, Hindu by religion, married, and belong to nuclear family. Further, the participants were equally distributed in all the three age groups.
Table 1: Sociodemographic variables of smokers

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[Table 2] shows the comparison of sociodemographic variables between smokers and nonsmokers. Although there were slight differences noted between the two groups, it was not statistically significant.
Table 2: Comparison of sociodemographic variables between smokers and nonsmokers

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[Table 3] shows the comparison of time taken for the completion of TMT-A between smokers and nonsmokers. For smokers, the mean value was 39.37 ± 20.790 s and for nonsmokers, the mean value was 23.70 ± 7.831 s. It is clear from the above results that smokers take more time when compared to nonsmokers and is statistically significant with P < 0.0001. It also shows the comparison of time taken for the completion of TMT-B between smokers and nonsmokers. For smokers, the mean value was 149.73 ± 93.749 s and for nonsmokers, the mean value was 86.83 ± 43.072 s. From this, it is clear that the time taken for smokers is more than nonsmokers and is statistically significant with P < 0.002. It shows the comparison of errors of TMT-A and TMT-B between smokers and nonsmokers. The mean value of errors of TMT-A between smokers and nonsmokers was 1.46 ± 0.07 and 1.06 ± 0.78, respectively. The mean value of errors of TMT-B between smokers and nonsmokers was 6.5 ± 0.40 and 4.70 ± 0.04, respectively. However, it was not statistically significant. It also shows the comparison of correct responses of DSST between smokers and nonsmokers. For smokers, the value was 61.77 ± 18.667 and for nonsmokers, the value was 85.30 ± 14.164. From the above results, it is clear that smokers are doing less correct responses when compared to nonsmokers, and the difference is statistically significant with P < 0.0001.
Table 3: Comparison for time taken and errors of trial-making test-A and test-B and comparison of correct responses of digit symbol substitution test between smokers and nonsmokers

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[Table 4] shows the comparison of concentration between smokers and nonsmokers. It shows that smokers are having more deficits in concentration when compared to nonsmokers, and the comparison is significant with P < 0.001. It shows the comparison of recent memory between smokers and nonsmokers. It shows that smokers are having more deficits in recent memory when compared to nonsmokers and the comparison is statistically significant with P < 0.0001, and it shows the comparison of past memory between smokers and nonsmokers. It shows that smokers are having more deficits in past memory when compared to nonsmokers, and the comparison is statistically significant with P < 0.0001. It shows the comparison of orientation between smokers and nonsmokers. It shows that smokers are having more deficits in orientation when compared to nonsmokers, and the comparison is statistically significant with P < 0.0001, and it also shows the comparison of functioning and self-care between smokers and nonsmokers. It shows that smokers are having more deficits in functioning and self-care when compared to nonsmokers and the comparison is statistically significant with P < 0.0001.
Table 4: Comparison of Brief Cognitive Rating Scales axis-1 (concentration), Brief Cognitive Rating Scales axis-II (recent memory), Brief Cognitive Rating Scales axis-III (past memory), Brief Cognitive Rating Scales axis-IV (orientation), and Brief Cognitive Rating Scales axis-V (functioning and self-care) between smokers and nonsmokers

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[Table 5] shows the correlation between pack years of smoking to that of TMT-A and B and DSST in smokers. There was a positive correlation with TMT-A and B and the errors, but a negative correlation was found with DSST, and it shows the correlation between FTND score to that of TMT-A and B and DSST in smokers. There was a positive correlation with TMT-A and B and TMT-B errors. However, a negative correlation was found with DSST.
Table 5: Correlation of pack years of smoking and Fagerström Test for Nicotine Dependence score to that of trial-making test-A, trial-making test-B, and digit symbol substitution test in smokers

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[Table 6] shows the comparison between concentration to pack years of smoking and FTND score in smokers. There is a significant deficit in concentration noted with P < 0.0001. It shows the comparison between recent memory to pack years of smoking and FTND score in smokers. Although there were slight differences noted, they were not statistically significant. It also shows the comparison between past memory to pack years of smoking and FTND score in smokers. There were significant deficits in past memory noted with P < 0.0001 and 0.023, respectively, for pack years of smoking and FTND score, respectively, and it shows the comparison between orientation to pack years of smoking and FTND score in smokers. There were significant deficits in orientation noted with P < 0.0001 and 0.035 for pack years of smoking and FTND score, respectively. It shows the comparison between functioning and self-care to pack years of smoking and FTND score in smokers. There were significant deficits in functioning and self-care noted with P < 0.0001 and 0.013 for pack years of smoking and FTND score, respectively.
Table 6: Comparison between Brief Cognitive Rating Scale axis-I (concentration), Brief Cognitive Rating Scale axis-II (recent memory), Brief Cognitive Rating Scale axis-III (past memory), Brief Cognitive Rating Scale axis-IV (orientation), Brief Cognitive Rating Scale axis-V (functioning and self-care) to that of pack years of smoking and Fagerström Test for Nicotine Dependence score in smokers

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  Discussion Top


Sociodemographic variables

In the present study, both cases and controls were males. This indicates that both the cases and controls were matched for gender. In a study by Rani et al.,[29] the prevalence of smoking among males and females in India was 29.3%, i.e., 94 million and 2.4%, i.e., 7.6 million, respectively. This shows that when compared to male smokers, female smokers were very few. As the prevalence of smoking among females is less, this may be the reason for not having the female smokers in the present study. In a study by Weiser et al.,[30] all the participants recruited in the study were male, which is similar to the present study.

In the present study, 15 (50%) cases (smokers) were urban dwellers and 15 (50%) cases (smokers) were rural dwellers. Sixteen (53.3%) controls (nonsmokers) were rural dwellers and 14 (46.67%) controls (nonsmokers) were urban dwellers. In a study by Rani et al.,[29] the prevalence of smoking in males among the urban and the rural in India was 21.4% and 32.5%, respectively. This is different from the present study. This difference may be due to the reason that the present study is a hospital-based study. Both the cases and controls were matched for education. In a study by Rani et al.,[29] which is similar to the present study, the prevalence of smoking is more among cases who finished their primary schooling and the prevalence of smoking is less among the professional groups.

The difference in the prevalence of smoking among different religions may be due to the different types of study designs as the present study is a hospital-based study. In a country like India, the proportion of religion in different states and districts is different, which may be the reason for the difference in the prevalence of smoking among the religion between the two studies. In a study by Rani et al.,[29] the prevalence of smoking in males among different economic groups was as follows, 30% of the population 15 years or older – 47% of men and 14% of women – either smoked or chewed tobacco, which translates to almost 195 million people – 154 million men and 41 million women in India. However, the prevalence may be underestimated by almost 11% and 1.5% for chewing tobacco among men and women, respectively, and by 5% and 0.5% for smoking among men and women, respectively, because of the use of household informants. Tobacco consumption was significantly higher in poor, less educated, scheduled castes, and scheduled tribe populations. The prevalence of tobacco consumption increased up to the age of 50 years and then leveled or declined. The prevalence of smoking and chewing also varied widely between different states and had a strong association with individual's sociocultural characteristics, and it is similar to the present study, in which the prevalence of smoking was highest in low-income group, and the prevalence decreases as the income increases. Cases and controls were matched for occupation.

Comparison of trail-making test-A and trail-making test-B of smokers to that of nonsmokers

The smokers performed poorly compared to that of nonsmokers in the completion of TMT-A. In the completion of TMT-A, smokers committed errors with a mean of 1.46 ± 0.07 when compared to nonsmokers who committed errors with a mean of 1.06 ± 0.78. The P value is 0.105 which is statistically not significant. The smokers performed poorly compared to that of nonsmokers in the completion of TMT-B. In the completion of TMT-B, smokers committed errors with a mean of 6.5 ± 0.40 when compared to nonsmokers who committed errors with a mean of 4.70 ± 0.04. The P value is 0.071 which is statistically not significant. In a study by Spilich et al.,[31] it was found that while cigarette smoking had no negative effect upon performance for simple perceptual tasks, smoking was found to exert measurable negative effects upon performance for more complex information-processing tasks. This is similar to the present study. In a study by Paul et al.,[32] smokers performed more poorly than nonsmokers on one measure of executive function. A significant age and smoking status interaction was identified, with older smokers performing more poorly than older nonsmokers, which implies that cigarette smoking is associated with isolated and subtle cognitive difficulties among very healthy individuals, which is similar to the present study.

Comparison of correct responses of digit symbol substitution test of smokers to that of nonsmokers

Cases (smokers) performed poorer than the controls (nonsmokers) in giving the correct responses within a time period of 120 s on DSST. In a study by Stewart et al.,[33] the results showed that smokers performed poorer on DSST and Mill–Hill Vocabulary Scale, which is similar to the present study in which smokers performed poorer in comparison with nonsmokers.

Comparison of Brief Cognitive Rating Scale between smokers and nonsmokers

The smokers had significant cognitive impairment when compared to nonsmokers on BCRS AXIS-I (concentration), BCRS AXIS-II (recent memory), BCRS AXIS-III (past memory), BCRS AXIS-IV (orientation), and BCRS AXIS-V (functioning and self-care). In a study by Sumitra Sudharkodhy et al.,[34] there was a negative correlation with Hindi mental status examination (HMSE) and with BCRS, there was a positive correlation which was statistically significant with P < 0.05, which is similar to the present study in which smokers had significant cognitive impairments on all the five axes of BCRS when compared to that of nonsmokers.

Correlation between Fagerstrom Test for Nicotine Dependence score and pack years of smoking to that of trail-making test-A, trail-making test-B, and digit symbol substitution test in smokers

This study shows that the higher the FTND score, more time is taken to complete the TMT-B. It signifies that smokers with higher FTND score have significant deficits in visual attention, task switching, visual search speed, scanning, speed of processing, cognitive flexibility, and executive functions, which results in more time to complete the TMT-B, i.e., FTND score is directly related to time taken for the completion of TMT-B. The correlation between TMT-B errors and FTND total score is statistically significant (r = 0.507, P = 0.004). There is a negative correlation between DSST and FTND score (r = −0.557, P < 0.05), which is statistically significant. This study shows that higher FTND score is associated with lesser number of correct responses on DSST, i.e., FTND score is inversely related to the number of correct responses in DSST. There is a positive correlation between TMT-A and number of pack years (r = 0.781, P < 0.0001), which is statistically significant. This study shows that the higher the number of pack years, more time is taken to complete the TMT-A. It signifies that smokers with higher number of pack years have significant deficits, which results in more time to complete the TMT-A, i.e., number of pack years is directly related to time taken for the completion of TMT-A. There is a positive correlation between TMT-A errors and pack years of smoking (r = 0.756, P < 0.05), which is statistically significant. There is a positive correlation between TMT-B and number of pack years (r = 0.851*, P < 0.0001), which is statistically significant. This study shows that the higher the number of pack years, more time is taken to complete the TMT-B. It signifies that smokers with higher number of pack years have significant deficits, which results in more time to complete the TMT-B task, i.e., number of pack years is directly related to time taken for the completion of TMT-B task. There is a positive correlation between TMT-B errors and pack years of smoking (r = 0.778, P < 0.05), which is statistically significant. There is a negative correlation between DSST and number of pack years of smoking (r = −0.889, P < 0.0001), which is statistically significant. This study shows that higher number of pack years of smoking is associated with lesser number of correct responses on DSST, i.e., number of pack years of smoking is inversely related to the number of correct responses in DSST.

Comparison between Fagerstrom Test for Nicotine Dependence score and pack years of smoking to that of Brief Cognitive Rating Scale in smokers

On comparison of FTND score to that of BCRS axis-i (concentration), the P value was < 0.0001, which is statistically significant. On comparison of FTND score to that of BCRS axis-ii (recent memory), the P value was 0.2, which is not statistically significant. On comparison of FTND score to that of BCRS axis-iii (past memory), the P value was 0.023, which is statistically significant. On comparison of FTND score to that of BCRS axis-iv (orientation), the P value was 0.035, which is statistically significant. On comparison of FTND score to that of BCRS axis-v (functioning and self-care), the P value was 0.013, which is statistically significant. These results show that the higher the FTND score, greater is the impairment on BCRS axes except BCRS axis 2 (recent memory). On comparison of pack years of cigarette smoking to that of BCRS axis-i (concentration), the P value was < 0.0001, which is statistically significant. On comparison of pack years of cigarette smoking to that of BCRS axis-ii (recent memory), the P value was 0.186, which is not statistically significant. On comparison of pack years of cigarette smoking to that of BCRS axis-iii (past memory), the P value was < 0.0001, which is statistically significant. On comparison of pack years of cigarette smoking to that of BCRS axis-iv (orientation), the P value was < 0.0001, which is statistically significant. On comparison of pack years of cigarette smoking to that of BCRS axis-v (functioning and self-care), the P value was 0.0001, which is statistically significant. These results show that the higher the pack-years of cigarette smoking, greater is the impairment on BCRS axes except BCRS axis 2 (recent memory). In a study by Fried et al.,[35] the results suggest that regular smoking during early adulthood is associated with cognitive impairments that signify, the greater the smoking duration, more will be the cognitive impairment, which is similar to the present study. In a study by Schinka et al.,[36] the results showed that heavy users and higher pack years of smoking are associated with poor performance on cognitive tests, which is similar to the present study.

Limitations of the study

  1. The sample size was relatively small. Thus, comparing variables with such small sample size will reduce the effect size of the results
  2. The study samples have been taken from patients attending the psychiatric OPD of Narayana Medical College and Hospital. Because it is a general hospital psychiatry setting, the results could not be extrapolated to community samples
  3. In this study, cognitive dysfunction was observed in male smokers only. In our sample, there were no female smokers. Hence, the cognitive deficits in female smokers cannot be assessed in our study
  4. In this study, illiterates are not taken. Hence, the cognitive deficits in them were not assessed.



  Conclusions Top


The results clearly indicate that chronic cigarette smoking causes significant cognitive dysfunctions in the domains of visual search speed, scanning, speed of processing, mental flexibility, executive functioning, concentration, recent memory, past memory, orientation, functioning, and self-care as assessed by TMTs A and B, DSST, and BCRS. Higher the nicotine dependence greater the cognitive dysfunction. This finding suggests that in chronic smokers, there are significant abnormalities in neurocognition.

Financial support and sponsorship

Nil.

Conflict of interest

There are no conflict of interest.



 
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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

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