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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 57:M181-M185 (2002)
© 2002 The Gerontological Society of America

Predictors of Decline in MMSE Scores Among Older Mexican Americans

Ha T. Nguyena, Sandra A. Blacka,b, Laura A. Rayb, David V. Espinoc and Kyriakos S. Markidesa,b

a Center on Aging, University of Texas Medical Branch, Galveston
b Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston
c Department of Family Practice, University of Texas Health Science Center, San Antonio

Kyriakos S. Markides, Department of Preventive Medicine & Community Health, University of Texas Medical Branch, Galveston, TX 77555-1153 E-mail: Kmarkide{at}utmb.edu.


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Background. The purpose of this analysis was to examine the association of sociodemographic variables and health-related conditions with 5-year declines in cognitive function among Mexican American elderly persons.

Methods. The cognitive function of 1759 participants was assessed by using the Mini-Mental State Examination (MMSE) in 1993/1994 and again in 1998/1999. Cognitive decline was defined by two sets of criteria: (1) a drop to 17 or below on the MMSE at follow-up, and (2) a decline of at least three points, the mean change in MMSE scores among respondents who obtained scores at or above the 5th percentile distribution at baseline.

Results. Cognitive decline was significantly associated with sociodemographic variables including age, education, marital status, and household composition. In addition, respondents with reported vision impairment, stroke, and diabetes were at increased risk for cognitive decline after controlling for multiple potential confounders.

Conclusion. Although age and education have been reported as the more salient predictors of cognitive deterioration, other sociodemographic and several medical conditions including stroke and diabetes should be considered as part of cognitive aging studies among Mexican American elders.

RECENT literature has focused on understanding factors predicting decline in cognitive function among normal older adults (1)(2)(3)(4). This research has focused predominantly upon non-Hispanic white elders. Little attention has been paid to other ethnic minority groups, particularly Hispanic elders. The Hispanic population of the United States is one of the fastest growing minorities and is expected to surpass African Americans to become the largest minority group sometime this decade (5). Understanding cognitive decline in this expanding population is of major public health importance (2)(6).

Data from longitudinal studies have suggested that, in general, older age (7), lower education level (8), higher initial levels of cognitive performance (9)(10), and higher depressive symptomatology (11)(12) are associated with higher rates of cognitive decline. In addition, several studies have suggested that certain medical conditions, such as hypertension (13), stroke (14), diabetes (4), and hearing and visual impairment (15), are associated with cognitive decline and indirectly with the incidence of possible dementia.

Although the aforementioned findings may generally hold for non-Hispanic whites, it is unclear as to how these findings can be generalized to other subpopulations, such as Mexican Americans. A limited number of studies have found a significantly higher prevalence of cognitive impairment among Hispanics compared to non-Hispanic whites (4)(16). In a recent study of older Mexican Americans, Black and colleagues (17) documented significant associations between sociodemographic variables, comorbid disease states, and cognitive performance. These findings, however, were based on cross-sectional data.

The purpose of the present study was to extend the earlier work reported by Black and colleagues (17) by examining cognitive decline in a large probability-based community sample of older Mexican Americans residing in the Southwestern United States. Specifically, we are interested in examining (1) changes in cognitive function in older Mexican Americans over a 5-year period; (2) demographic variables such as age, gender, education, immigration status, household composition, and marital status that may predict cognitive decline; and (3) whether a group of health-related conditions measured at baseline can predict cognitive decline at 5-year follow-up. That is, do health-related characteristics, including depressive symptoms and medical conditions, add to the prediction of cognitive decline after the effects of sociodemographic variables have been controlled?


   Methods
Top
 Abstract
 Methods
Results
 Discussion
 References
 
Sample
Data used in this study were part of the Hispanic Established Population for the Epidemiological Study of the Elderly (Hispanic EPESE), a population-based survey of older Mexican Americans residing in the Southwestern United States. A full description of the rationale, methods, and subject characteristics of this study can be found elsewhere (18). Area probability sampling yielded a sample of 3050 Mexican Americans aged 65 and older representing approximately 500,000 older Mexican Americans living in Texas, New Mexico, Colorado, Arizona, and California in 1993/1994. At 5-year follow-up, 628 subjects were deceased, 312 were lost to follow-up, and 127 refused to be reinterviewed. In addition, 224 subjects were excluded from the analyses because they were unable to answer one or more cognitive-related items as a result of physical limitations (such as being too ill, being blind, or being impaired by arthritis). Thus, the final sample consisted of 1759 subjects with complete data at both baseline and follow-up. All interviews were conducted either in Spanish or English, depending on the respondent's preference, by interviewers who were fully bilingual and predominantly of Hispanic origin.

Measures
The primary outcome measure was the Mini-Mental State Examination (MMSE). The MMSE is among the most frequently used cognitive screening measures in studies of older adults (19). This instrument has been used extensively in community surveys, nursing home studies, longitudinal studies of aging, and in clinical investigation (20). Using conventional cut-points, the MMSE has consistently yielded correct classification rates of 80% to 90% when compared with physician assessments of cognitive impairment and dementia (21).

The English and Spanish versions of the MMSE were adopted from the Diagnostic Interview Scale (DIS) used in prior community surveys (22). This Spanish version of the MMSE has met standard criteria for development of translated tests, including formal translation, back-translation, and consensus by committee for final item content. Additionally, the Spanish MMSE has been successfully used in community surveys of Mexican Americans (23). Owing to reported poor item equivalency, however, the serial-sevens item was not used in the present version. Bilingual interviewers were thoroughly trained in administration and scoring of the MMSE, both through workshops and videotaped instruction. As has been recommended in the literature (24), responses of "don't know" and refusals were counted as errors.

In addition to sociodemographic characteristics (gender, age, education, literacy, marital status, immigration status, and household composition), respondents were also assessed for the presence of depressive symptomatology using the self-reported Center for Epidemiological Studies–Depression (CES-D) scale (25). A score of 16 or greater on the CES-D was used to delineate high levels of depressive symptoms. Respondents were also measured for sensory impairment in terms of hearing and vision. Hearing was assessed using the 10-item Hearing Handicap Inventory for the Elderly–Screening version (HHIE-S) (26). Scores ranged from 0 to 40 with <10 indicating no impairment. Near vision was measured using cards, each with seven-digit numbers of three different type sizes: 7-, 10-, and 23-point. Subjects held these cards at least 7 inches from their eyes and were then asked to read the numbers. Participants who could only read the 23-point were considered to have near-vision impairment. Functional distance vision was measured using a modified Snellen test employing directional Es at 4 meters to assess acuity from 20/40 to 20/200 (27). Those with vision worse than 20/60 to 20/200 were considered impaired. In addition, self-reported medical diseases were assessed by asking respondents if they had ever been told by a doctor that they had any of the following conditions: diabetes, stroke, and hypertension.

Because there are no established normative guidelines for determining cognitive decline, we used two sets of criteria to identify individuals who had declined to severe impairment (Criterion-1) and individuals with a more moderate impairment decline (Criterion-2). Criterion-1 was defined by using a fixed MMSE cutoff score derived from the literature on experimental and clinical populations (28), and respondents were classified as "cognitively impaired" if their MMSE scores at baseline were less than 18. At 5-year follow-up, "unimpaired" subjects at baseline were categorized as having "declined" if their MMSE scores had dropped to less than 18 points, indicating a decline to severe impairment.

Analyses using Criterion-1 are based on subjects with scores higher than 17 at baseline (n = 1716), as opposed to the entire total sample. Of the 1716 subjects with baseline MMSE scores of 18 or higher, 314 declined to 17 or below at follow-up. Of those who declined, 115 had baseline scores between 18 and 21, 115 had scores between 22 and 25, and 84 had scores between 26 and 30 (see Table 1 for distribution of cutoff scores at baseline and at follow-up).


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  		Table 1. Distribution of MMSE Scores at Baseline and at 5-Year Follow-up

 
Criterion-2 used a cutoff MMSE score based on the distribution of scores from the sample itself rather than on cutoff scores derived from other non-Hispanic populations. We adapted the NINCDS-ADRDA Work Group's work in determining cognitive impairment when respondents' scores fell at or below the 5th percentile of their normal control group (3)(29). We extended this control group guideline to include our entire total population sample and to identify individuals with more moderate cognitive decline relative to others within their own group. The sensitivity and specificity of this criterion have been examined and reported in an earlier study (11). Based on the 95th percentile distribution, 1689 participants in our study obtained MMSE scores of 18 or higher at baseline, and the mean change in MMSE scores from baseline to 5-year follow-up for this group was 3.35 points. Hence, respondents were considered to have cognitively declined if, at follow-up, their scores had decreased by at least 3 points since baseline. Under Criterion-2, 921 subjects were classified as having cognitively declined, and of these, 24 had baseline scores less than 18, 107 had scores between 18 and 21, 262 had scores between 22 and 25, and 528 had scores between 26 and 30. Table 1 also shows the distribution of cutoff scores using this criterion.

Analyses
Multiple logistic regression models were fit to calculate odds ratios for the relationship between selected noncognitive variables at baseline and cognitive scores at follow-up. Two separate logistic equations were used to model the two dichotomous measures of cognitive change. Predictors included age, gender, education, marital status, immigration status, and household composition (i.e., number of people living in the household). After controlling for these variables, baseline depressive symptoms and selected health conditions were then added to each model. Descriptions of baseline variables are given in Table 2 . The dependent variable was the presence or absence of cognitive decline (as defined by Criteria 1 and 2) at follow-up.


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  		Table 2. Distribution of Correlates at Baseline (N = 1759)

 

   Results
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 Abstract
 Methods
 Results
Discussion
 References
 
Criterion-1 Data
The results of the logistic regression models are presented in Table 3 . With increasing age, there was a significant increase in the odds of cognitive decline during the 5-year study interval. Subjects with less than 5 years of education were significantly more likely to experience decline. Compared with participants who were living alone, respondents who lived with others had almost twice the odds of experiencing decline. Married respondents were less likely to decline. Gender and immigration status were not significant predictors of decline.


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  		Table 3. Selected Baseline Characteristics as Predictors of Cognitive Decline (N = 1756)

 
We next added diabetes, stroke, hypertension, hearing and vision impairment, and depressive symptoms to the analysis. Results indicated that baseline diabetes, stroke, and near-vision impairment were significant predictors of decline. Hypertension, hearing impairment, distant vision impairment, and depressive symptoms were not predictive of decline as defined by Criterion-1. Furthermore, including these health characteristics did not alter the significant effects of age, education, marital status, and household composition.

Criterion-2 Data
We conducted additional analyses to examine whether sociodemographic and health-related variables would predict cognitive change among respondents who had dropped at least three points or more on the MMSE since baseline. Older age and lower education were significant predictors of decline whereas gender, marital status, immigration status, and household composition were not (see Table 3 ). Under Criterion-1, subjects who had a history of stroke or diabetes were found to decline over the follow-up interval. However, under Criterion-2, diabetes and stroke did not significantly predict decline. After adjusting for sociodemographic and health characteristics, elderly persons with near-vision impairment remained significantly more likely to decline than those without near-vision impairment. Finally, respondents reporting high levels of depressive symptoms (CES-D >=16) were significantly more likely to decline than those reporting lower levels of depressive symptoms (CES-D <16). Further analyses were conducted to examine the interaction effects of age with education, stroke, diabetes, hypertension, and depressive symptoms. None proved to be significant for either Criterion-1 or Criterion-2 analyses.


   Discussion
Top
 Abstract
 Methods
 Results
 Discussion
References
 
In this analysis, we examined the effect of noncognitive variables on cognitive decline over a 5-year period using data from the Hispanic EPESE. The most noteworthy finding evident in the present study is the different predictive patterns across the two criteria. Results for both criteria indicated that gender and immigration status were not predictive of cognitive decline; however, increasing decline with increasing age and decreasing level of education is consistent with findings reported earlier (30). Living with others was predictive of changes in cognitive impairment among participants who had dropped to 17 or below (Criterion-1), but was not associated with Criterion-2 decline (>=3 points). An explanation for this finding could be attributed to the level of manifestations of decline. Subjects who dropped to an MMSE score of 17 or below at follow-up declined to severe cognitive impairment, which made them dependent on others. They were also more likely to have experienced cognitive and other difficulties at baseline and, thus, were more likely to live with family members. Contrarily, most subjects who declined under Criterion-2 were likely to have only declined to a moderate level of cognitive impairment. Thus, they may have experienced some diminution in cognitive capacity yet at a moderate level that would allow them to function without the assistance of others. This would support the idea that Mexican American families try to manage cognitively impaired elders in their home environments and have them relocate to the family home only when they become less manageable due to decline or catastrophic event (e.g., hip fracture, pneumonia).

Although hypertension was not predictive of cognitive decline, stroke and diabetes were significant predictors of decline to the severe category (Criterion-1). They were not, however, predictive of decline of three or more points (Criterion-2). These results are in agreement with previously published findings that the lack of significance may reflect the fact that stroke and diabetes are more likely to associate with a higher degree of cognitive decline (31)(32). We did find that diabetes and stroke predicted decline to the severe category. Other studies have suggested that increased duration of diabetes is associated with increased cognitive impairment (31)(33) and that the risk for cognitive impairment decreases as the interval between stroke and impairment increases. For example, the odds for cognitive impairment 3 months after onset of stroke were found to be higher than the odds of cognitive impairment after 52 months of follow-up (34). Hypertension was not associated with cognitive decline, although it has been found to be inversely related with memory in older adults (35). The discrepancy in such findings can be partly explained by different measures of hypertension. The absence of an accurate blood pressure measure may reduce the ability of our models to identify the association between hypertension and cognitive decline.

Depressive symptomatology was independently associated with cognitive decline. Previous research with the same data has shown high rates of depressive symptoms in older Mexican Americans, especially women (36). It is possible that depression sometimes mimics early dementia by causing a decline in scores on cognitive status tests (12)(37).

Near-vision impairment was significantly associated with cognitive decline under both criteria. Vision impairment has been found to influence the level and quality of interactive experiences of older adults, thereby reducing their capacity to develop and maintain relationships and to participate in activities that may improve their physical, mental, and psychosocial well-being (38). Thus, it is possible that visual impairment could exacerbate cognitive impairment indirectly if it predisposes an individual to depression and social isolation. Hearing impairment was not predictive of decline in the present study, which is consistent with prior research suggesting that this condition per se is not an independent risk factor for cognitive decline (39). However, when evaluating degree of dementia, the presence of severe hearing loss coupled with cognitive impairment can result in a much greater degree of dementia (38).

Certain limitations to the present study should be noted. First, the study employed self-reported data of depressive symptoms and, thus, may not be equivalent to a clinical diagnosis of depression. We believe that the use of a standard clinical diagnosis of depression is necessary in order to yield a more accurate relationship between depression and cognitive decline. Second, the medical conditions are self-reports of subjects about their health status. However, prior research has found consistency between self-reported medical conditions and conditions reported in medical records (32). Despite these limitations, the present study adds to our understanding of the complex interrelationships between depressive symptoms, chronic diseases, and cognitive functioning in older adults. Our findings also suggest that, as a screener, the MMSE may substantially overestimate cognitive impairment in older Mexican Americans if these correlates are not taken into account. More important, increasing the awareness of these strong associations can help clinicians, particularly those who do not regularly work with Mexican Americans, better monitor cognitive decline, which has been associated with mortality and the onset of disability (40).


   Acknowledgments
 
This study was supported by Grant AG10939 (Hispanic Established Population for the Epidemiological Study of the Elderly) and Grant T32AG00270 (Health of Older Minorities), both funded by the National Institute on Aging. The field work was conducted by Harris Interactive, New York. We appreciate the helpful comments by two anonymous reviewers.

Received March 5, 2001

Accepted July 31, 2001


   References
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 Methods
 Results
 Discussion
 References
 

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