Cognitive and functional impairment in an older community population from Brazil: The intriguing association with frequent pain

Archives of Gerontology and Geriatrics 66 (2016) 134–139

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Cognitive and functional impairment in an older community population from Brazil: The intriguing association with frequent pain Marcos Antonio Lopesa,* , André Junqueira Xavierb , Eleonora D’Orsic a b c

Department of Internal Medicine, Federal University of Santa Catarina, Brazil Memory Clinic, Medical School, South of Santa Catarina University, Brazil Department of Public Health, Federal University of Santa Catarina, Brazil

A R T I C L E I N F O

A B S T R A C T

Article history: Received 21 July 2015 Received in revised form 18 May 2016 Accepted 29 May 2016 Available online 3 June 2016

Objective: This cross-sectional community-based epidemiologic survey aimed to investigate the prevalence of cognitive and functional impairment (CFI) and its distribution in relation to sociodemographic and clinical factors in an older community sample in Florianópolis, Brazil. Materials and methods: The population was a representative sample aged 60 and older; the cluster sample strategy was performed. CFI, a syndromic category that does not exclude dementia, was defined according to the combination of low MMSE (Mini-Mental State Examination) score and moderate/severe dependence in a scale that measured activities of daily living. The data were submitted to multiple regression analysis using the Poisson regression method. Results: A sample of 1705 subjects was interviewed; the mean age was 70.6 years (60–104 years; SD: 8.0); 63.9% were female and 43.7% had up to 4 years of schooling. CFI was detected in 325 subjects, resulting in a raw prevalence of 19.2% (95% CI: 17.3–21.0). Older age, presence of diabetes, heart disease, stroke, urinary incontinence, arthritis, frequent pain and depression were significantly associated with CFI (p < 0.05). Conclusion: In addition to the diversity of factors associated with CFI, the present study indicated the need to investigate the role of frequent pain in the development and progression of cognitive impairment and dementia. ã 2016 Elsevier Ireland Ltd. All rights reserved.

Keywords: Cognitive impairment Functional performance Pain Prevalence

1. Introduction Cognitive impairment is quite common in the elderly population. It encompasses several syndromic clinical conditions in addition to dementia, including aging-associated cognitive decline (AACD) (Levy, 1994), cognitive and functional impairment (CFI) (Lopes et al., 2007), cognitive impairment no dementia (CIND) (Graham et al., 1997), vascular cognitive impairment (VCI) (Hachinski and Bowler, 1993) and mild cognitive impairment (Petersen et al., 1999), which is the most well-known and studied condition. In spite of different concepts, criteria and methods, the prevalence of cognitive impairment as a broad syndromic entity has varied in people aged 60/65 years and over, from 6.4% in the USA (Johnson et al., 1997) to 22.4% in Malaysia (Sherina, Rampa, & Mustaquim, 2004), with intermediate rate of 7.7% in China (Lim,

* Corresponding author at: Universidade Federal de Santa Catarina, Hospital Universitário, Departamento de Clínica Médica Rua Maria Flora Pausewang, Campus Universitário, CEP 88040-970, Florianópolis, SC, Brazil. E-mail addresses: [email protected], [email protected] (M.A. Lopes). http://dx.doi.org/10.1016/j.archger.2016.05.010 0167-4943/ã 2016 Elsevier Ireland Ltd. All rights reserved.

Lim, Anthony, Yeo, & Sahadevan, 2003). When functional impairment has been combined with cognitive impairment, the rates have been more uniform. Specifically, two studies that were conducted in Brazil (Lopes et al., 2007; Hototian et al., 2008) and one that was conducted in Spain (Rodríguez-Sánchez et al., 2011) found a prevalence in the range of 16–19.4%; the exception was one study from Brazil, that used a very low MMSE cut-off point for cognitive impairment and found the low rate of 3.4% (Lebrão & Laurenti, 2005). In resume, approximately 1 in every 5 older people has been affected by cognitive impairment. The consequence is an increasing burden in their daily living and clinical outcomes. Higher risk for falls (Muir, Gopaul, & Odasso, 2012) and hip fractures (Seitz, Adunuri, & Gill, 2011) and greater length of hospital stay (Fulop, Strain, Fahs, Schmeidler, & Snyder, 1998) have been associated with cognitive impairment, decreasing the elderly population’s quality of life. Epidemiological (Qiu, Xu, & Fratiglioni, 2010) and physiological findings (vascular mechanisms, oxidative damage and inflammation) (De la Torre, 2012) have been proposed to explain the occurrence of major diseases that accompany cognitive impairment, particularly Alzheimer’s disease, and offer information

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to guide prevention. However, nutritional approaches and the control of vascular risk factors have not evidently decreased the rates of cognitive impairment and dementia (Rocca et al., 2011). Hence, the investigation of factors that are associated with cognitive impairment must be expanded. Additional epidemiological studies conducted with large samples and a wide set of conditions investigated should make an essential contribution to successfully approach elderly population at risk of developing cognitive impairment and dementia. The present study aimed to investigate the prevalence of cognitive and functional impairment (CFI) and its distribution in relation to socio-demographic and clinical factors in an older community sample in Florianópolis, Brazil. The purpose of investigating CFI, a syndromic and comprehensive category, was to allow the inclusion of all clinical conditions that impair cognition and functional performance in older people. 2. Methods 2.1. Study design and population The data were drawn from the EPIFLORIPA IDOSO study, a crosssectional community-based epidemiologic survey on the health of older people (aged 60 years and older) from Florianópolis, Brazil. The study was conducted between 2009 and 2010. The sampling strategy was published previously (Medeiros et al., 2012). Briefly, Florianópolis had a population of approximately 400.000. According to the 2010 census, 10.8% of these people were aged 60 years and older (FIBGE, 2010). The cluster sample strategy was initially based on the average monthly income of the head of the family from 420 census tracts, of which 80 were systematically selected. Sixty houses were further drawn from each census tract, and all older people in those houses were invited to participate. The population’s sample size was calculated using Epi-Info-6.04 software. The result was multiplied by two (to account for the cluster sampling) and then increased by 20% for attrition and 15% for multivariable analysis adjusting, leading to a sample of 1599 older people. The parameters were as follows: population of 44.460 older individuals, unknown prevalence (50%), sampling accuracy or error (d) of 4.0% and confidence level of 95%. Due to the multiple objectives of the EPIFLORIPA IDOSO study and the availability of financial resources, the sample size was increased to 1911 individuals. 2.2. Instruments and procedure Data were collected using a standard questionnaire that was completed by trained research assistants, who interviewed the elderly and a relative, if necessary. The questionnaire included instruments that measured CFI (dependent variable), sociodemographic and clinical items (both assessed through the elderly or relative direct report; depression and alcohol consumption, specifically, were assessed through scales). The main purpose was to compare participants with CFI and participants without CFI (controls) in relation to socio-demographic and clinical characteristics. The instruments that were used to measure CFI are as follows: - Mini-Mental State Examination (MMSE) (Folstein, Folstein, & McHugh, 1975): a well-known cognitive assessment that was originally created to quantify cognitive deficit and has been widely used to screen for dementia. - Activities of Daily Living scale (ADL scale) (Fillenbaum, 1984): this scale assesses functional performance and dependence/ disability in seven basic and eight instrumental activities of daily living, resulting in a classification of absent/mild dependence in

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ADL (partial or total disability in 0–3 activities) and moderate/ severe dependence in ADL (partial or total disability in 4–15 activities). The basic daily activities are as follows: getting in and out of bed, eating, grooming, walking on a leveled surface, bathing or showering, dressing, using the toilet on time and climbing one set of stairs. The instrumental daily activities include the following: taking medications on time, walking nearby home, shopping, preparing meals, cutting toe nails, taking buses or taxis and cleaning up the house. This classification was adapted from the study by Rosa, Benício, Latorre, and Ramos, (2003). CFI was defined according to the combination of low MMSE score and moderate/severe dependence in ADL scale. The strategy of combining cognitive and functional instruments to screen for probable cases of dementia has been used previously (Bottino et al., 2009), demonstrating increased accuracy. In the present study, the cut-off scores that were used to define low MMSE were based on years of schooling, as follows: <20 for illiterate; <25 for 1–4 years of schooling; <27 for 5–8 years of schooling; <28 for 9 years of schooling. These cut-off points were adapted from Brucki et al.’s study (Brucki, Nitrini, Caramelli, Bertolucci, & Okamoto, 2003) and used in the present study to increase the sensitivity of the instrument and include subjects with dementia, principally, and other clinical conditions that affect cognition but are not dementia (Bottino et al., 2009). Moreover, two previous studies that used CFI as screening procedure for dementia diagnosis (twophases studies) (Bottino et al., 2008; Lopes, Ferrioli, Nakano, Litvoc, & Bottino, 2012) observed sensitivity of 94.4% and 98.5% to identify dementia cases, respectively. The Geriatric Depression Scale (GDS) was used to detect depression. A score of >5 points, from a total of 15, was defined as positive for depression (Sheikh & Yesavage, 1986). The Alcohol Use Disorders Identification Test (AUDIT) was used to investigate alcohol consumption (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). The first two questions were utilized to assess frequency and number of doses in a typical day, resulting in a classification of abstinence (no alcohol use), mild/moderate alcohol use (1–7 doses weekly) and heavy alcohol use (8 doses weekly). The socio-demographic variables were age (60–64, 65– 69, 70–74, 75–79, 80–84, 85), gender, marital status (married, single, divorced, widow), education (measured as years of schooling: illiterate, 1–4 years, 5–8 years, 9years) and familial per capita income in quartiles. The other clinical variables included self-reported chronic morbidities (hypertension, diabetes, heart disease, stroke, bronchitis, tuberculosis, liver cirrhosis, chronic renal failure, gastric ulcer, arthritis, tendinitis, spinal problems, fibromyalgia and cancer), symptoms (urinary incontinence and pain) habit (smoking) and use of analgesic medications. Pain was categorized as diffuse pain (self-reported pain in several parts of the body in the last month) and frequent pain (any pain on most days). Smoking history was categorized as never, past and current smokers. The use of analgesic medications (only continued use) was assessed from a list of all medications, which were previously collected and organized according to the Anatomical Therapeutic Chemical classification system (World Health Organization). In resume, this set of clinical variables was chosen to globally assess the health of older people in the EPIFLORIPA IDOSO study. 2.3. Statistical analysis Comparisons between the frequency of CFI cases and controls in relation to the socio-demographic and clinical variables were carried out using Poisson regression (bivariate analysis). Multiple regression analysis was performed using the Poisson regression method to evaluate the relationship between the

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independent variables (socio-demographic and clinical) and the dependent variable (CFI). The Stata 11.0 software was used for data analysis. Variables that presented a p value that was less than 0.05 on the bivariate analysis were included in the multiple regression analysis. The first model included all the variables; following this, the variables with p value greater than 0.05 were excluded one by one, using backward stepwise method. The prevalence ratio, the corresponding 95% confidence interval (95% CI) and the p value based on the Wald test were calculated. 2.4. Ethical consideration The investigation was approved of by the local Ethics Committee, and all the subjects and their relatives agreed to participate in the study by signing the informed consent term. 3. Results Of the total eligible individuals (1911), 1705 subjects were interviewed, resulting in a response rate of 89%. The mean age was 70.6 years (60–104; SD: 8.0); 63.9% were female and 43.7% had up to 4 years of schooling. The socio-demographic characteristics of the subjects who were interviewed are shown in Table 1. Three hundred and twenty-five subjects presented cognitive and functional impairment (CFI), corresponding to a raw CFI prevalence of 19.2% (95% CI: 17.3–21.0) in people aged 60 years and older and 23.3% (95% CI: 20.9–25.6) in people aged 65 years and older (286 of 1225 subjects). Table 1 also shows the comparison between subjects with and without CFI with respect to socio-demographic characteristics. Older age, female gender, widowhood, lower education and lower familial income were significantly associated with higher rates of

Table 1 Total sample, participants with CFI and their distribution in relation to sociodemographic characteristics compared to participants without CFI. Variables

Age 60–64 65–69 70–74 75–79 80–84 85 Gender Female Male

Total Sample %

95%CI

N

%

PR

467 383 339 270 128 105

27.6 22.6 20.0 15.9 7.5 6.2

25.4–29.6 20.5–24.5 18.0–21.8 14.2–17.6 6.4–8.9 5.1–7.4

39 51 58 65 47 65

8.3 13.3 17.1 24.0 36.7 61.9

1 1.63 2.12 3.24 4.12 7.58

p value

4. Discussion The current study showed that the prevalence of cognitive and functional impairment was high and independently associated with older age, diabetes, heart disease, stroke, urinary incontinence, arthritis, frequent pain and depression. The high prevalence of CFI corroborated the findings of previous studies, particularly those that combined cognitive and functional impairment. In relation to studies that used only the cognitive parameter, the lowest rates, 6.4% and 7.7% (Johnson et al., 1997; Lim, Lim, Anthony, Yeo, & Sahadevan, 2003), were much lower than that observed in the current study. These differences are likely due to methodological reasons. The American study examined a specific population, Amish individuals, who generally display higher MMSE scores. The Chinese investigation utilized the Elderly Cognitive Assessment Questionnaire (ECAQ), which is sensitive only in severe cases of dementia, according to the questionnaire authors. Moreover, the Brazilian study that combined cognitive and functional assessment and observed a prevalence of 3.4% also

Table 2 Total sample, participants with CFI and their distribution in relation to clinical variables (hypertension, diabetes, heart disease, stroke, bronchitis, chronic renal failure and urinary incontinence) compared to participants without CFI. Variables

95%CI

Total Sample N

1.079 613

Marital Status Married 986 Single 99 Divorced 132 Widow/er 475 Education Illiterate 1–4 5–8 9

Participants with CFI

N

CFI. Tables 2 and 3 present the total sample and the comparison between groups in terms of clinical characteristics. The bivariate analysis showed that hypertension, diabetes, heart disease, stroke, bronchitis, chronic renal failure, urinary incontinence, gastric ulcer, arthritis, spinal problems, diffuse pain, frequent pain, depression and alcohol abstinence (comparison with mild/ moderate and heavy alcohol use) were significantly associated with higher rates of CFI. The results of the multiple regression analysis are presented in Table 4. Older age and presence of diabetes, heart disease, stroke, urinary incontinence, arthritis, frequent pain and depression remained significantly associated with higher rates of CFI.

159 584 321 628

Familial Income 163.7 420 163.8–350 433 350.1–750 423 750.1 416

63.7 36.2

58.2 5.8 7.8 28.0

9.4 34.5 18.9 37.1

24.8 25.5 25.0 24.5

61.4–66.0 33.9–38.5

55.9–60.6 4.7–6.9 6.5–9.0 25.9–30.2

8.0–10.7 32.2–36.7 17.1–20.8 34.8–39.4

22.7–26.8 23.5–27.6 22.9–27.0 22.5–26.6

234 91

149 17 20 139

43 143 46 93

93 108 71 53

21.6 14.8

15.1 17.1 15.1 29.2

27.0 24.4 14.3 14.8

22.1 24.9 16.7 12.7

1 0.69

1 1.28 1.06 2.01

1 0.82 0.51 0.48

1 1.19 0.67 0.58

1.01–2.64 1.38–3.28 2.07–5.08 2.63–6.43 5.34–10.74

0.51–0.93

0.76–2.15 0.66–1.71 1.56–2.59

0.56–1.20 0.34–0.77 0.33–0.69

0.89–1.57 0.51–0.88 0.39–0.88

0.045 0.001 <0.001 <0.001 <0.001

0.017

0.335 0.791 <0.001

0.322 0.002 <0.001

0.224 0.006 0.011

Education: years of schooling; Familial Income: per person, U$, quartiles; CFI = cognitive and functional impairment; PR = prevalence ratio, in relation to participants without CFI; 95%CI = 95% confidence interval.

Participants with CFI

p value

%

95%CI

N

%

PR

95%CI (PR)

Hypertension 694 No Yes 998

41.0 58.9

38.6–43.3 56.6–61.3

96 229

13.8 22.9

1 1.64

1.34–2.00

<0.001

Diabetes No Yes

1.319 373

77.9 22.0

75.9–79.9 20.0–24.0

217 108

16.5 29.0

1 1.99

1.50–2.64

<0.001

Heart disease No 1.216 Yes 476

71.8 28.1

69.7–74.0 25.9–30.2

188 137

15.5 28.8

1 2.02

1.60–2.55

<0.001

Stroke No Yes

1.543 149

91.1 8.8

89.8–92.5 7.4–10.1

258 67

16.7 45.0

1 2.86

2.26–3.62

<0.001

Bronchitis No 1.425 Yes 267

84.2 15.7

82.4–85.9 14.0–17.5

249 76

17.5 28.5

1 1.77

1.40–2.24

<0.001

Chronic renal failure No 1.614 95.4 Yes 77 4.5

94.4–96.4 3.5–5.5

300 24

18.6 31.2

1 1.68

1.12–2.54

0.006

Urinary incontinence No 1.196 70.8 Yes 493 29.1

68.6–72.9 27.0–31.3

155 168

12.9 34.0

1 2.51

2.05–3.07

<0.001

CFI = cognitive and functional impairment; PR = prevalence ratio, in relation to participants without CFI; 95%CI = 95% confidence interval; Tuberculosis, liver cirrhosis, tendinitis, fibromyalgia and cancer were not associated with CFI.

M.A. Lopes et al. / Archives of Gerontology and Geriatrics 66 (2016) 134–139 Table 3 Total sample, participants with CFI and their distribution in relation to clinical variables (gastric ulcer, arthritis, spinal problems, diffuse pain, frequent pain, depression and alcohol use) compared to participants without CFI. Variables

Total Sample N

%

Participants with CFI 95%CI

N

%

PR

1

Gastric ulcer No

1.488 87.9

274

18.4

Yes

204

86.3– 89.4 12.0 10.5– 13.6

51

25.0 1.34

Arthritis No

1.114

170

15.2

Yes

573

66.0 63.7– 68.2 33.9 31.7– 36.2

153

26.7 2.08 1.60–2.71

50.2 47.8– 52.5 49.7 47.4– 52.1

132

15.5

193

22.9 1.58

15.8

Spinal problems 849 No

<0.001

1.43–2.31

<0.001

141

13.1

184

29.7 2.28 1.81–2.89

<0.001

1.230 75.5 73.4– 77.5 399 24.4 22.4– 26.5

134

10.8

158

39.6 4.15

1.099 65.0 62.7– 67.3 28.4 26.2– 480 30.5 111 6.5 5.3–7.7

268 24.3 1

1.259 74.4

199

Yes

433

72.3– 76.4 25.5 23.5– 27.6

126 29.1

1.82

50.2 47.8– 52.5 49.7 47.4– 52.1

1

Frequent pain No 1.074

Alcohol use abstinence mild/ moderate heavy

<0.001

1.23–2.03

Diffuse pain No

>5

0.032

1

842

Depression 0 5

1.02–1.76

1

Yes

Yes

p value

95%CI (PR)

618

1

1 3.38–5.10

<0.001

47

9.7

0.50 0.35–0.71

<0.001

10

9.0

0.30 0.11–0.80

0.017

Diffuse pain: pain in several parts of the body, in the last month; Frequent pain: any pain, most of the days; Depression: Geriatric depression scale, cut-off score 5; Alcohol use: Alcohol Use Disorders Identification Test, first and second questions (abstinence: no alcohol use, mild-moderate: 1–7 doses weekly, heavy: 8 doses weekly); CFI = cognitive and functional impairment; PR = prevalence ratio, in relation to participants without CFI; 95%CI = 95% confidence interval; Smoking and use of analgesic medications were not associated with CFI.

used a low MMSE cut-off score, 12 (Lebrão & Laurenti, 2005). Similar to the current study, the other Brazilian investigations that combined instruments used different MMSE cut-off scores for different levels of education and found similar rates (Lopes et al., 2007; Hototian et al., 2008). Considering these methodological issues and the previous prevalence studies of CIND and dementia, the prevalence of CFI that was observed in the present study does not seem to suffer from bias and under-detection. Consequently, this prevalence rate probably represents an actual high regional rate. The effect of age on CFI rates confirmed previous findings, but did not exhibit the levelling-off prevalence over the age of 80 years or the over-effect in a specific age group, as observed in a metaanalysis of world data (Ritchie & Kildea, 1995) and in a review of Latin American studies on the prevalence of dementia (Nitrini et al., 2009). Although the current sample included a small group of

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the oldest subjects, which might have caused inconsistency in the analysis, it was representative of the original population. The results concerning heart disease, stroke and diabetes reinforced the contribution of vascular risk factors to cognitive impairment and dementia. Of note, stroke and diabetes exhibited independent and age-controlled effects over CFI rates, corroborating previous studies that showed that diabetes increases the risk for both stroke and dementia (Biller & Love, 1993; Arvanitakis, Wilson, Bienias, Evans, & Bennett, 2004). Moreover, in addition to vascular disorders, neurodegenerative mechanisms have been proposed to connect diabetes and dementia, particularly the association between AD pathology and hyperglycemia and insulindegrading enzyme (Arvanitakis et al., 2004). Stroke, on the other hand, can be considered as a marker of cumulative exposure to vascular risk factors, besides diabetes, affecting cognitive function. Despite the difficulties of establishing a cause-effect relationship in a cross-sectional survey, it is interesting to note two factors that reflected different patterns of association. Based on the present findings, urinary incontinence is likely a consequence of cognitive disorders, mainly dementia, rather than a causal factor. The results concerning depression reaffirmed the strong and complex interaction with cognitive impairment and dementia. Depression may be a risk factor or a consequence that is physical or psychological and may appear during a prodromal state of dementia (Modrego & Ferrández, 2004). The most surprising finding was the direct and no ageassociated relation between CFI and frequent pain. Although pain and dementia are age-related and it is expected that a rising population of demented people suffer from pain (Scherder et al., 2009), the relationship between the two conditions is complex and sometimes controversial. Although the findings are conflicting, pain has been observed to be less frequent in subjects with cognitive impairment and dementia than in controls (Shega, Paice, Rockwood, & Dale, 2010; Achterberg et al., 2010). Two hypotheses have been proposed to explain this low prevalence. First, pain is unrecognized and undertreated in demented people due to barriers in assessment related to communication difficulties and the non-use of appropriate tools (McAuliffe, Nay, O’Donnell, & Fetherstonhaugh, 2009). Second, there is a primary change illustrated by decreased motivational and affective components of pain in AD (Scherder, Sergeant, & Swaab, 2003). On the other hand, a reasonable amount of data has demonstrated an association between chronic pain of different origins and cognitive deficits. Executive function, including mental flexibility, attention, working memory and global measure (MMSE) composed the principal cognitive domains that were affected by chronic pain in revised studies (Moriarty, McGuire, & Finn, 2011). Integrating the studies, it is interesting to note that attention there seems to be affected in chronic pain patients only in complex tests, which require executive function (Moriarty, McGuire, & Finn, 2011), small to moderate dysfunction in executive tasks has been consistently observed (Liu, Li, Tang, Wu, & Hu, 2014) and memory impairment was only partially attributable to attention, depressive mood and pain intensity (Moriarty, McGuire, & Finn, 2011; Berryman et al., 2014). Indeed, global measure like MMSE had a very weak effect in few studies and there was no obvious pattern of cognitive deficit (Moriarty, McGuire, & Finn, 2011). Caution must be applied, however, as inconsistences involving sample procedures remain (heterogeneous and small samples, bias selection), there has been lack of comparative group, pain has not been adequately characterized and confounders like comorbidities, medication and sleep have not been systematically controlled (Moriarty, McGuire, & Finn, 2011; Liu, Li, Tang, Wu, & Hu, 2014; Berryman et al., 2014). The findings of the present study demonstrated the association between global cognitive impairment and frequent pain, independently of the presence of depression, stroke, diabetes

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Table 4 Multiple analysis (final model), by Poisson regression method, with the sociodemographic and clinical variables on CFI rates. VARIABLES

PR

95% CI

p value

Age 70–74 75–79 80–84 85

1.74 2.17 2.78 4.85

1.15–2.63 1.44–3.27 1.76–4.40 3.08–7.64

0.009 <0.001 <0.001 <0.001

Gender (male) Diabetes Heart disease Stroke Urinary incontinence Arthritis Frequent pain Depression

0.85 1.52 1.29 1.43 1.36 1.40 1.37 2.33

0.63–1.14 1.17–1.99 1.01–1.65 1.09–1.87 1.09–1.69 1.02–1.92 1.05–1.80 1.76–3.09

0.291 0.002 0.042 0.010 0.006 0.035 0.020 <0.001

Age: comparison with 60–64 years old; Gender was exhibited regarding its relevance; Diabetes and other clinical variables: comparison with no presence; CFI = cognitive and functional impairment; PR = prevalence ratio; 95%CI = 95% confidence interval; Marital status, education, familial income, hypertension, bronchitis, chronic renal failure, gastric ulcer, spinal problems, diffuse pain and alcohol use were not associated with CFI.

and analgesic medications. This might be interpreted as either difficulties in the assessment of pain in more severe cases or evidence of an independent effect of frequent pain on cognitive impairment. Finally, despite the connection between pain and cognitive disturbance, only negative outcomes related to poorer performance in activities of daily living and occurrence of behavioral changes have been shown in previous studies. Interestingly, there is an apparent lack of data on pain as a risk factor for dementia, or at least as a contributor of cognitive decline in demented subjects. In addition to a low cognitive state chronically leading to a maladaptive stress response, a physical mechanism that connects pain and cognitive impairment has been presented. This mechanism is illustrated by the overlap of neuroanatomical and neurochemical substrates related to pain and cognitive impairment (Moriarty, McGuire, & Finn, 2011; Hart, Wade, & Martelli, 2003). Three methodological limitations in the current study should be considered. First, due to the inherent property of a cross-sectional survey, the current study cannot define the precise cause-effect relationship. Second, the use of a broad and syndromic category, CFI, did not permit the investigation of specific diseases that affect cognition and functional state. On the other hand, CFI has been previously employed, indicating good validity, and seems to be an interesting strategy to examine all clinical conditions that might affect cognition and functional performance in the elderly population. Moreover, the current findings on prevalence rate and associated factors justified the use of CFI. Finally, in the present study, most of the clinical variables were assessed through the elderly or relative direct report, including frequent pain assessment. Among the strengths of the present study, it should be emphasized that the study was conducted with a representative sample of elderly persons from a Brazilian capital, ensuring the extrapolation of results to the population as a whole. We also highlight the high response rate, which contributed to the internal validity of the study and decreased the likelihood of systematic errors. 5. Conclusion The present study confirmed the contribution of diabetes, stroke and depression to the occurrence of cognitive and functional impairment in older subjects. In addition, the current findings

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