- Email: [email protected]
Anemia prevalence and its determinants in Brazilian institutionalized elderly
Nutrition 28 (2012) 640–643
Contents lists available at ScienceDirect
Nutrition journal homepage: www.nutritionjrnl.com
Applied nutritional investigation
Anemia prevalence and its determinants in Brazilian institutionalized elderly sar Ferreira de Moraes Ph.D. Cand. c, d, Alika Terumi Arasaki Nakashima M.Sc. a, b, c, *, Augusto Ce a, b, c , Rosane Marina Peralta Ph.D. a Flavia Auler Ph.D. Cand. , Parana , Brazil Postgraduate Program in Health Sciences, State University of Maringa , Parana , Brazil Undergraduate Course in Nutrition, Catholic Pontificate University, Maringa c , Parana , Brazil GEPECIN–Science of Nutrition Research Group, Catholic Pontificate University, Maringa d ~o Paulo, Department of Preventive Medicine - Post-Graduate Program in Science, Sa ~o Paulo, Sa ~o Paulo, Brazil School of Medicine of the University of Sa a
b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 April 2011 Accepted 19 September 2011
Objective: To estimate the prevalence of anemia and analyze the factors associated with anemia in elderly residents of long-term care institutions. Methods: This cross-sectional study was performed in male and female elderly volunteers selected , Brazil in a two-stage random sampling from long-term care institutions in the city of Maringa (2008). A diagnosis of anemia was based on the plasma hemoglobin concentration. The independent variables analyzed were gender, age, time of residence at an institution, body mass index, and serum iron and albumin concentrations. The association between anemia and the variables was assessed using the Poisson regression with robust variance in unadjusted and adjusted analyses, considering a complex sample and a significance level of 5%. Results: The sample included 124 adults older than 60 y residing in long-term care institutions (53.0% female). The prevalence of anemia was 29% and was not significantly associated with gender, serum iron concentration, time of residence at an institution, or body mass index. Conversely, hypoalbuminemia was considered a risk factor for anemia. Conclusion: There is a high prevalence of anemia in the institutionalized elderly and hypoalbuminemia is a factor associated with this outcome. Interventions are necessary to promote improvements in the health and welfare of this population. Ó 2012 Published by Elsevier Inc.
Keywords: Nutritional status Iron Hematology Asylums Low- and middle-income countries
Introduction Of the various physiologic changes observed in the elderly, a pathologic decrease in hemoglobin concentration, characterized by anemia, is one of the most common findings [1,2]. However, there are different types of anemia, and the most frequent types in this population are chronic anemia and iron-deficiency anemia [3]. Recent research involving elderly Koreans has shown that chronic anemia currently is more prevalent than iron-deficiency anemia in this population [4]. A recent review has indicated that the prevalence of anemia varies considerably among the elderly, from 2.9% to 61.0% in men and from 3.3% to 41.0% in women [5]. Landi et al. [6] found that, according to the World Health Organization (WHO) criteria [3], 63% of the elderly residents in long-term care institutions in Rome were anemic and that the risk of mortality for anemic elderly was 1.56 (95% confidence interval [CI] 1.07–2.28). Because * Corresponding author. Tel./fax: þ55-44-3026-2322. E-mail address: [email protected] (A. T. A. Nakashima). 0899-9007/$ - see front matter Ó 2012 Published by Elsevier Inc. doi:10.1016/j.nut.2011.09.016
of the high probability of anemia in the elderly population, identifying the associated factors would be highly relevant for professional caregivers. However, it is difficult to find data and epidemiologic studies on elderly residents of long-term care institutions in Brazil. Therefore, to fill this gap at least in part, we determined the prevalence of anemia and the associated factors in elderly residents of long-term care institutions in the city of (PR, Brazil). Maringa
Materials and methods Sample size The population included male and female adults older than 60 y residing in in 2008. long-term care institutions in Maringa The sample size calculations were performed according to the following parameters: 95% CI, a power of 80%, a prevalence of 33% [7] as the most expected outcome, a margin of error of 5 percentage points, and a design effect of 2 because of the complex sample size. It was thus estimated that data would have to be collected from at least 124 residents. Because this study was part of a larger health survey, including other outcomes requiring larger samples, an extra 5% for
A. T. A. Nakashima et al. / Nutrition 28 (2012) 640–643 possible losses and refusals and an extra 15% for multivariate analysis were added, resulting in a minimum requirement of 131 subjects. The sample was obtained through a two-stage selection process: institutionally (primary sampling unit) and individually. In the first stage, we listed all institutions in the metropolitan area that treat residents in the designated age group (n ¼ 9) and then randomly selected four of them based on their probable population size. In the second stage, individuals were selected by simple random sampling; their number was proportional to the total population of elderly residents in each of the institutions, which guaranteed that the study had a power of 80%. After receiving consent from each selected care institution, we also obtained an informed consent (on the aims and methodology) of all participants and/or responsible parties (when necessary). This study was approved by the human . research ethics committee of the State University of Maringa
1
641
Time of residence at the institutions
Age
Gender
Body Mass Index
2
*Iron
3
Hypoalbuminemia
Anemia
Fig. 1. Hierarchical model of analysis for the outcome determination. * Nine individuals were excluded owing to the impossibility of quantifying the serum iron.
Variables Outcome The diagnosis of anemia was based on the plasma hemoglobin concentration. The cutoff points proposed by the WHO were used and adjusted for gender and age [2]. To test for anemia, 10 mL of blood was drawn from an elderly resident’s antecubital vein from 07:00 to 08:30, after the resident had fasted for at least 8 h. Independent variables The variables analyzed were gender, age, time of residence at an institution, body mass index (BMI), and serum iron and albumin concentrations. We evaluated nutritional status according to a BMI classification [8]. For individuals who could walk, body mass was measured with a Filizola (S~ ao Paulo, Brazil) mechanical scale (maximum capacity 150 kg, precision 0.1 kg). Height was estimated using an aluminum stadiometer that was attached to the scale (precision 0.01 m). For those with impaired mobility (bedridden or wheelchair users), height was estimated by knee height [9], which was measured with a caliper and a yardstick. Body mass was then estimated according to knee height, age, and calf and arm circumferences [9]. The analyzed biochemical variables were iron and albumin concentrations, which were assessed using colorimetric enzymatic methods and automated ~o Paulo, Brazil). The albumin levels equipment (Bioplus-2000, Bioplus, Inc., Sa were categorized according to criteria established by the WHO [3]. The iron concentration was dichotomized as lower than the 50th percentile of the sample or at least the 50th percentile. Statistical analysis The Shapiro-Wilke test was initially used to determine whether the numeric variables conformed to a Gaussian distribution, and the results indicated that not all of the numeric variables were normally distributed. Therefore, the following descriptive statistics were used: average values, medians (50th percentile), standard deviations, and 95% CIs. Subsequently, Student’s t test for independent data and the Mann-Whitney U test were used to compare average and median values, respectively, for men and women. We assessed the relation between hemoglobin and body mass using bivariate linear regression. We considered the presence/absence of anemia as an outcome variable. Gender, age, time of residence in an institution, body mass, BMI, and biochemical and hematologic variables were included as independent variables. The proportion of anemic subjects according to each independent variable was determined. The magnitude of these associations was subsequently expressed in unadjusted and adjusted prevalence ratios and their respective 95% CIs, taking into consideration the sampling strategy. These were calculated using a Poisson regression with robust variance, as recommended for high prevalence outcomes [10]. The adjusted analysis was conducted according to a previously formulated hierarchical model (Fig. 1), including three levels: 1) gender, age, and time of residence in an institution, 2) nutritional status, and 3) biochemical variables. In this model, the variables were controlled for those at the same level and the levels above [11]. For a variable to remain in the model, a significance level of P < 0.20 was required. Wald tests were used for the heterogeneity of dichotomous or nominal variables, and linear tests were used for ordinal categorical variables. The analyses took the cluster effect of the sample into consideration using the “svy” command group in STATA 8.0 (STATA Corp., College Station, TX, USA). The significance level was set at 5% for all analyses.
Results Of the 131 elderly residents randomly selected from the four long-term care institutions, 5.3% (n ¼ 7) were lost because they refused to allow the anthropometric measurements or
venipuncture. Of the 124 individuals included in the final analysis, 53% were female. The median age was 76.8 y (60.7–101.2) and 41% were older than 80 y. The median time of residence at the institutions was 3.3 y (0.1–31.7), with 40.3% having spent between 1 to 4 y in their respective institutions. The mean values of body mass, height, hemoglobin, and serum iron were higher in men than in women (P < 0.05; Table 1). The other continuous variables were not significantly different between men and women. The total prevalence of anemia was high in these elderly subjects, with no observed difference between men and women. Anemia was associated with BMI and albumin and iron concentrations (Table 2). Table 3 presents the unadjusted and adjusted analyses of the variables associated with anemia. The unadjusted analysis demonstrated that a risk of anemia was associated with BMI (underweight), plasma iron (50th percentile), and hypoalbuminemia. After adjustment in the hierarchical model, only hypoalbuminemia was associated with anemia, although not strongly. The BMI showed a borderline association with anemia in the adjusted analyses. The other variables were not significantly associated with anemia after the adjustment.
Discussion A high prevalence of anemia and its associated factors was found in the institutionalized elderly in this study.
Table 1 Description of anthropometric and biochemical variables of the institutionalized , PR, Brazil (2008) according to gender elderly in Maringa Variables
Gender
Body mass* (kg)
Male þ Male Female Male þ Male Female Male þ Male Female Male þ Male Female Male þ Male Female
Heighty (cm)
BMI (kg/m2)
Iron* (mg/dL)
Albumin (g/dL)
Female
Female
Female
Female
Female
Mean
Median
95% CI
53.2 56.1 50.6 152.8 158.5 147.8 22.6 22.3 22.9 77.8 86.8 73.0 3.7 3.8 3.7
51.4 55.1 47.4 153.4 158.4 147.9 22.3 22.4 22.0 75.9 82.0 70.1 3.6 3.7 3.6
50.4–55.9 52.7–59.5 46.5–54.6 151.3–154.4 156.7–160.3 146.1–149.5 21.6–23.7 21.1–23.6 21.3–24.5 63.8–91.8 65.4–127.0 69.1–77.0 3.5–3.9 3.6–4.0 3.4–4.0
BMI, body mass index; CI, confidence interval * Independent t test. y Mann-Whitney U test.
P 0.046*
<0.001y
0.542*
<0.001*
0.801*
642
A. T. A. Nakashima et al. / Nutrition 28 (2012) 640–643
Table 2 Anemia prevalence in institutionalized elderly in Maring a, PR, Brazil (2008) according to independent variables Variable
n (%)
Gender Male Female Age (y) 60–69 70–79 80 Time of residence <1 y 1–4 y 5–9 y 10 y Body mass index (kg/m2) Underweight Eutrophic Overweight Hypoalbuminemia No Yes Iron* 50th percentile >50th percentile All subjects
Anemia n (%)
P 0.46z
58 (46.8) 66 (53.2)
15 (25.9) 21 (31.8)
29 (23.4) 44 (35.5) 51 (41.1)
6 (20.7) 16 (36.4) 14 (27.5)
23 50 30 21
7 15 10 4
0.33y
0.72y (18.5) (40.3) (24.2) (16.9)
(30.4) (30.0) (33.3) (19.1) 0.007y
74 (59.7) 34 (27.4) 16 (12.9)
29 (39.2) 6 (17.7) 1 (6.3)
81 (65.3) 43 (34.7)
16 (19.8) 20 (46.5)
49 (42.6) 66 (57.4) 124 (100)
29 (43.9) 6 (12.2) 36 (29.0)
0.002z
<0.001z
Values are presented as number of subjects (percentage) * Nine individuals were excluded owing to the impossibility of quantifying the serum iron. y Wald test for trend. z Wald test for heterogeneity.
Regarding the sampling characteristics (representative population-based sample), the findings corroborated data available in the literature [12,13]. The significant differences in BMI can be explained, at least in part, by the fact that the women
had a smaller arm circumference and a higher percentage of body fat than the men [14]. The prevalence of anemia in the participants (29%) was markedly higher than that found in non-institutionalized elderly people in developed (10.9%) [15] and developing countries (3.6– 11%) [1,12]. However, the occurrence was lower than that found by Landi et al. [6] (63%) who used the same criteria to evaluate an institutionalized elderly population in Rome, Italy. Moreover, these investigators observed that, of the 130 deaths that occurred in their sample, there was a mortality risk of 1.56 (1.07–2.28) for those with a positive diagnosis of anemia. These results are clear evidence that the institutionalized elderly are especially vulnerable to anemia. Thus, identifying its associated factors is relevant to public health. No significant associations between gender and the prevalence of anemia were found in the present study. In contrast, Marın et al. [16] observed that gender influenced the occurrence of anemia in adults (i.e., >18 y old). However, the results of Guralnik et al. [15], who analyzed a sample of adults older than 65 y old, differed from those of Marın et al., with a similar prevalence in men and women (11% and 12%, respectively). The occurrence of anemia increases with advancing age [15] and affects a large number of elderly people, apparently without gender distinction. We verified that, among the associated factors, body mass was significantly related to hemoglobin values and that there was a marginal association between underweight and a greater occurrence of anemia. The association between anemia and BMI in the elderly has been analyzed previously, and low BMI values have been found to correlate with low hemoglobin values [17]. Such results indicate that special care must be taken with the diet of the institutionalized elderly and that increasing the body mass of this population may have a positive effect on hemoglobin concentrations. Because nearly one-third of the
Table 3 Unadjusted and adjusted PRs (95% CI) of independent variables in relation to anemia for institutionalized elderly in Maring a, PR, Brazil (2008) Levelz
Variable
1
Gender Male Female Age (y) 60–69 70–79 80 Time of residence <1 y 1–4 y 5–9 y 10 y Body mass index (kg/m2) Eutrophic Underweight Overweight Hypoalbuminemia No Yes Altered iron* 50th percentile >50th percentile
Subjects (%)
Unadjusted analysis PR (95% CI)
2
3
Adjusted analysis P
PR (95% CI)
0.47x 15 (25.9) 21 (31.8)
0.81 (0.46–1.42) 1.00
6 (20.7) 16 (36.4) 14 (27.5)
1.00 1.75 (0.77–3.97) 1.32 (0.57–3.08)
7 15 10 4
(30.4) (30.0) (33.3) (19.1)
1.00 0.98 (0.46–2.09) 1.09 (0.49–2.44) 0.62 (0.21–1.84)
29 (39.2) 6 (17.7) 1 (6.3)
1.00 2.22 (1.01–4.85) 0.35 (0.04–2.72)
16 (19.8) 20 (46.5)
1.00 2.35 (1.36–4.06)
29 (43.9) 6 (12.2)
1.00 3.58 (1.61–7.99)
Py 0.49x
0.79 (0.44–1.41) 1.00 0.67{
0.73{ 1.00 1.75 (0.77–3.97) 1.29 (0.55–3.01)
0.5{
0.4{ 1.00 0.93 (0.44–1.97) 1.06 (0.49–2.27) 0.56 (0.19–1.69)
0.04{
0.05{ 1.00 2.15 (0.98–4.77) 0.32 (0.04–2.39)
0.002x
0.03x 1.00 1.82 (1.03–3.20)
0.002x
0.08x 1.00 1.73 (0.92–3.24)
CI, confidence interval; PR, prevalence ratio * Nine individuals were excluded owing to the impossibility of quantifying the serum iron. y Variables with P > 0.2 were excluded from the model. z The effect of each variable on the outcome was adjusted for other variables in the same level or above in the hierarchical model. x Wald test for heterogeneity. { Wald test for trend.
A. T. A. Nakashima et al. / Nutrition 28 (2012) 640–643
investigated individuals (27%) were underweight, it is important that this association be taken into consideration. Residents with plasma iron levels no higher than the 50th percentile had a greater prevalence of anemia in the raw analysis, although the significance of this association disappeared when the hierarchical setting was applied. Previous studies have also failed to find an association between serum iron levels and low levels of hemoglobin [4,18]. Low serum iron concentrations can be an important cause of anemia [19,20], but the values found in the present study were within the normal limits [3]. Thus, other factors that we did not analyze could be responsible for anemia, such as a deficiency of folate, B6, or B12. A strong association between anemia and the plasma concentrations of albumin has been described in the literature [21, 22]. The results of the present study confirm such an association, inasmuch as the participants with hypoalbuminemia presented a greater probability of having anemia (prevalence ratio 1.82, 95% CI 1.03–3.20, adjusted analysis) than those with normal albumin levels. A low food intake causes a decrease of albumin synthesis in the liver and a decreased production of other serum proteins, including hemoglobin [23,24]. Therefore, the association between hypoalbuminemia and anemia caused by decreases in liver protein synthesis could result from a decreased food intake, malnutrition, advancing age, or sarcopenia. The fact that we evaluated neither associated pathologies (digestive system diseases, nephropathies, or tumors) nor the use of medication that could interfere with the nutritional status or laboratory tests (owing to inconsistencies in institutional record keeping) was a limitation of this study. Moreover, we did not assess whether anemia was caused by chronic disease or nutritional deficiencies. These are important topics for further studies and could assist in the formulation of preventive measures in long-term care institutions. Limited food quality and availability are risk factors for low protein and micronutrient absorption, which decrease the synthesis of albumin and iron absorption. Intervention studies have shown that improving the food quality in long-stay institutions for the elderly has a positive effect on albumin [25], hemoglobin, and serum folate [26] concentrations. Thus, to improve the health status of residents in such institutions, the food quality should be improved by analyzing its composition, and periodical monitoring of patients’ nutritional status should be carried out. In summary, we found that the prevalence of anemia in this institutionalized elderly population was high, that it was independent of gender, and that it was associated with hypoalbuminemia. Thus, there is a real need for strategies to minimize this problem and improve the health of the elderly living in the studied long-term care institutions.
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13] [14]
[15]
[16]
[17]
[18]
[19] [20]
[21]
[22]
[23] [24]
References [1] Barbosa DL, Arruda IK, Diniz AS. [Prevalence and characteristics of anemia in an elderly population attending a Health Family Program]. Rev Bras Hematol Hemoter 2006;28:288–92. [2] Lopez-Contreras MJ, Zamora-Portero S, Lopez MA, Marin JF, Zamora S, Perez-Llamas F. Dietary intake and iron status of institutionalized elderly
[25]
[26]
643
people: relationship with different factors. J Nutr Health Aging 2010;14:816–21. Programs WH, editor. Iron deficiency anaemia assessment, prevention, and control: a guide for programme managers. Geneva: World Health Organization; 2001. Choi C, Lee J, Park K, Choi I, Kim S, Seo J, et al. Incidence of anemia in older Koreans: community-based cohort study. Arch Gerontol Geriatr 2005;41: 303–9. C, Wilson A, Ershler W. Prevalence and outcomes of anemia in Beghe geriatrics: a systematic review of the literature. Am J Med 2004; 116(suppl):3S–10S. Landi F, Russo A, Danese P, Liperoti R, Barillaro C, Bernabei R, Onder G. Anemia status, hemoglobin concentration, and mortality in nursing home older residents. J Am Med Dir Assoc 2007;8:322–7. De Maria R, Ripamonti V, Sandri R, Ceretti A, Ferratini M. The negative prognostic synergism of anemia and heart disease in female nursing home residents. Am J Cardiol 2005;96:1460–2. ~o M, Marucci M, Barbosa A. Estado nutricional e capacidade fısica. In: Lebra de, Bem-estar e Envelhecimento–O Projeto Duarte Y, editors. SABE–Sau Sabe no municıpio de S~ ao Paulo: uma abordagem inicial. Brasılia: OPAS; 2003. p. 93–118. Chumlea WC, Guo SS, Steinbaugh ML. Prediction of stature from knee height for black and white adults and children with application to mobility-impaired or handicapped persons. J Am Diet Assoc 1994;94: 1385–90. Barros A, Hirakata V. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol 2003;3:21. Victora C, Huttly S, Fuchs S, Olinto M. The role of conceptual frameworks in epidemiological analysis: a hierarchical approach. Int J Epidemiol 1997;26: 224–7. Olivares M, Hertrampf E, Capurro M, Wegner D. Prevalence of anemia in elderly subjects living at home: role of micronutrient deficiency and inflammation. Eur J Clin Nutr 2000;54:834–9. Rauen M, Moreira E, Calvo M, Lobo A. [Nutritional status assessment of institutionalized elderly]. Rev Nutr 2008;3:303–10. Organization of Pan-American Health. Salud, bien estar y envejecimento rica Latina y El Caribe. Washington, DC: Organization Pan(SABE) em Ame American Health; 2001. Guralnik J, Eisenstaedt R, Ferrucci L, Klein H, Woodman R. Prevalence of anemia in persons 65 years and older in the United States: evidence for a high rate of unexplained anemia. Blood 2004;104:2263–8. Marın G, Rivadulla P, Negro L, Gelemur M, Etchegoyen G. [Population study of the prevalence of anaemia in the adult population of Buenos Aires, Argentina]. Aten Primaria 2008;40:133–8. Choi C, Lee J, Park K, Yoon S, Choi I, Oh S, et al. Prevalence and characteristics of anemia in the elderly: cross-sectional study of three urban Korean population samples. Am J Hematol 2004;77:26–30. Ramel A, Jonsson P, Bjornsson S, Thorsdottir I. Anemia, nutritional status, and inflammation in hospitalized elderly. Nutrition 2008;24: 1116–22. McCormick L, Stott D. Anaemia in elderly patients. Clin Med 2007;7:501–4. Shi Z, Hu X, He K, Yuan B, Garg M. Joint association of magnesium and iron intake with anemia among Chinese adults. Nutrition 2008;24: 977–84. Mitrache C, Passweg J, Libura J, Petrikkos L, Seiler W, Gratwohl A, et al. Anemia: an indicator for malnutrition in the elderly. Ann Hematol 2001;80:295–8. Zakai N, Katz R, Hirsch C, Shlipak M, Chaves P, Newman A, Cushman M. A prospective study of anemia status, hemoglobin concentration, and mortality in an elderly cohort: the Cardiovascular Health Study. Arch Intern Med 2005;165:2214–20. Rothschild M, Oratz M, Schreiber S. Albumin synthesis. 1. N Engl J Med 1972;286:748–57. Rothschild M, Oratz M, Schreiber S. Albumin synthesis (second of two parts). N Engl J Med 1972;286:816–21. Manders M, de Groot CP, Blauw YH, Dhonukshe-Rutten RA, van HoeckelPrüst L, Bindels JG, et al. Effect of a nutrient-enriched drink on dietary intake and nutritional status in institutionalised elderly. Eur J Clin Nutr 2009;63:1241–50. pez-Sobaler M, Bermejo LM, Aparicio A, Rodrıguez-Rodrıguez E, Lo s P, Ortega RM. Dietary strategies for improving folate status in Andre institutionalized elderly persons. Br J Nutr 2009;101:1611–5.
Contents lists available at ScienceDirect
Nutrition journal homepage: www.nutritionjrnl.com
Applied nutritional investigation
Anemia prevalence and its determinants in Brazilian institutionalized elderly sar Ferreira de Moraes Ph.D. Cand. c, d, Alika Terumi Arasaki Nakashima M.Sc. a, b, c, *, Augusto Ce a, b, c , Rosane Marina Peralta Ph.D. a Flavia Auler Ph.D. Cand. , Parana , Brazil Postgraduate Program in Health Sciences, State University of Maringa , Parana , Brazil Undergraduate Course in Nutrition, Catholic Pontificate University, Maringa c , Parana , Brazil GEPECIN–Science of Nutrition Research Group, Catholic Pontificate University, Maringa d ~o Paulo, Department of Preventive Medicine - Post-Graduate Program in Science, Sa ~o Paulo, Sa ~o Paulo, Brazil School of Medicine of the University of Sa a
b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 April 2011 Accepted 19 September 2011
Objective: To estimate the prevalence of anemia and analyze the factors associated with anemia in elderly residents of long-term care institutions. Methods: This cross-sectional study was performed in male and female elderly volunteers selected , Brazil in a two-stage random sampling from long-term care institutions in the city of Maringa (2008). A diagnosis of anemia was based on the plasma hemoglobin concentration. The independent variables analyzed were gender, age, time of residence at an institution, body mass index, and serum iron and albumin concentrations. The association between anemia and the variables was assessed using the Poisson regression with robust variance in unadjusted and adjusted analyses, considering a complex sample and a significance level of 5%. Results: The sample included 124 adults older than 60 y residing in long-term care institutions (53.0% female). The prevalence of anemia was 29% and was not significantly associated with gender, serum iron concentration, time of residence at an institution, or body mass index. Conversely, hypoalbuminemia was considered a risk factor for anemia. Conclusion: There is a high prevalence of anemia in the institutionalized elderly and hypoalbuminemia is a factor associated with this outcome. Interventions are necessary to promote improvements in the health and welfare of this population. Ó 2012 Published by Elsevier Inc.
Keywords: Nutritional status Iron Hematology Asylums Low- and middle-income countries
Introduction Of the various physiologic changes observed in the elderly, a pathologic decrease in hemoglobin concentration, characterized by anemia, is one of the most common findings [1,2]. However, there are different types of anemia, and the most frequent types in this population are chronic anemia and iron-deficiency anemia [3]. Recent research involving elderly Koreans has shown that chronic anemia currently is more prevalent than iron-deficiency anemia in this population [4]. A recent review has indicated that the prevalence of anemia varies considerably among the elderly, from 2.9% to 61.0% in men and from 3.3% to 41.0% in women [5]. Landi et al. [6] found that, according to the World Health Organization (WHO) criteria [3], 63% of the elderly residents in long-term care institutions in Rome were anemic and that the risk of mortality for anemic elderly was 1.56 (95% confidence interval [CI] 1.07–2.28). Because * Corresponding author. Tel./fax: þ55-44-3026-2322. E-mail address: [email protected] (A. T. A. Nakashima). 0899-9007/$ - see front matter Ó 2012 Published by Elsevier Inc. doi:10.1016/j.nut.2011.09.016
of the high probability of anemia in the elderly population, identifying the associated factors would be highly relevant for professional caregivers. However, it is difficult to find data and epidemiologic studies on elderly residents of long-term care institutions in Brazil. Therefore, to fill this gap at least in part, we determined the prevalence of anemia and the associated factors in elderly residents of long-term care institutions in the city of (PR, Brazil). Maringa
Materials and methods Sample size The population included male and female adults older than 60 y residing in in 2008. long-term care institutions in Maringa The sample size calculations were performed according to the following parameters: 95% CI, a power of 80%, a prevalence of 33% [7] as the most expected outcome, a margin of error of 5 percentage points, and a design effect of 2 because of the complex sample size. It was thus estimated that data would have to be collected from at least 124 residents. Because this study was part of a larger health survey, including other outcomes requiring larger samples, an extra 5% for
A. T. A. Nakashima et al. / Nutrition 28 (2012) 640–643 possible losses and refusals and an extra 15% for multivariate analysis were added, resulting in a minimum requirement of 131 subjects. The sample was obtained through a two-stage selection process: institutionally (primary sampling unit) and individually. In the first stage, we listed all institutions in the metropolitan area that treat residents in the designated age group (n ¼ 9) and then randomly selected four of them based on their probable population size. In the second stage, individuals were selected by simple random sampling; their number was proportional to the total population of elderly residents in each of the institutions, which guaranteed that the study had a power of 80%. After receiving consent from each selected care institution, we also obtained an informed consent (on the aims and methodology) of all participants and/or responsible parties (when necessary). This study was approved by the human . research ethics committee of the State University of Maringa
1
641
Time of residence at the institutions
Age
Gender
Body Mass Index
2
*Iron
3
Hypoalbuminemia
Anemia
Fig. 1. Hierarchical model of analysis for the outcome determination. * Nine individuals were excluded owing to the impossibility of quantifying the serum iron.
Variables Outcome The diagnosis of anemia was based on the plasma hemoglobin concentration. The cutoff points proposed by the WHO were used and adjusted for gender and age [2]. To test for anemia, 10 mL of blood was drawn from an elderly resident’s antecubital vein from 07:00 to 08:30, after the resident had fasted for at least 8 h. Independent variables The variables analyzed were gender, age, time of residence at an institution, body mass index (BMI), and serum iron and albumin concentrations. We evaluated nutritional status according to a BMI classification [8]. For individuals who could walk, body mass was measured with a Filizola (S~ ao Paulo, Brazil) mechanical scale (maximum capacity 150 kg, precision 0.1 kg). Height was estimated using an aluminum stadiometer that was attached to the scale (precision 0.01 m). For those with impaired mobility (bedridden or wheelchair users), height was estimated by knee height [9], which was measured with a caliper and a yardstick. Body mass was then estimated according to knee height, age, and calf and arm circumferences [9]. The analyzed biochemical variables were iron and albumin concentrations, which were assessed using colorimetric enzymatic methods and automated ~o Paulo, Brazil). The albumin levels equipment (Bioplus-2000, Bioplus, Inc., Sa were categorized according to criteria established by the WHO [3]. The iron concentration was dichotomized as lower than the 50th percentile of the sample or at least the 50th percentile. Statistical analysis The Shapiro-Wilke test was initially used to determine whether the numeric variables conformed to a Gaussian distribution, and the results indicated that not all of the numeric variables were normally distributed. Therefore, the following descriptive statistics were used: average values, medians (50th percentile), standard deviations, and 95% CIs. Subsequently, Student’s t test for independent data and the Mann-Whitney U test were used to compare average and median values, respectively, for men and women. We assessed the relation between hemoglobin and body mass using bivariate linear regression. We considered the presence/absence of anemia as an outcome variable. Gender, age, time of residence in an institution, body mass, BMI, and biochemical and hematologic variables were included as independent variables. The proportion of anemic subjects according to each independent variable was determined. The magnitude of these associations was subsequently expressed in unadjusted and adjusted prevalence ratios and their respective 95% CIs, taking into consideration the sampling strategy. These were calculated using a Poisson regression with robust variance, as recommended for high prevalence outcomes [10]. The adjusted analysis was conducted according to a previously formulated hierarchical model (Fig. 1), including three levels: 1) gender, age, and time of residence in an institution, 2) nutritional status, and 3) biochemical variables. In this model, the variables were controlled for those at the same level and the levels above [11]. For a variable to remain in the model, a significance level of P < 0.20 was required. Wald tests were used for the heterogeneity of dichotomous or nominal variables, and linear tests were used for ordinal categorical variables. The analyses took the cluster effect of the sample into consideration using the “svy” command group in STATA 8.0 (STATA Corp., College Station, TX, USA). The significance level was set at 5% for all analyses.
Results Of the 131 elderly residents randomly selected from the four long-term care institutions, 5.3% (n ¼ 7) were lost because they refused to allow the anthropometric measurements or
venipuncture. Of the 124 individuals included in the final analysis, 53% were female. The median age was 76.8 y (60.7–101.2) and 41% were older than 80 y. The median time of residence at the institutions was 3.3 y (0.1–31.7), with 40.3% having spent between 1 to 4 y in their respective institutions. The mean values of body mass, height, hemoglobin, and serum iron were higher in men than in women (P < 0.05; Table 1). The other continuous variables were not significantly different between men and women. The total prevalence of anemia was high in these elderly subjects, with no observed difference between men and women. Anemia was associated with BMI and albumin and iron concentrations (Table 2). Table 3 presents the unadjusted and adjusted analyses of the variables associated with anemia. The unadjusted analysis demonstrated that a risk of anemia was associated with BMI (underweight), plasma iron (50th percentile), and hypoalbuminemia. After adjustment in the hierarchical model, only hypoalbuminemia was associated with anemia, although not strongly. The BMI showed a borderline association with anemia in the adjusted analyses. The other variables were not significantly associated with anemia after the adjustment.
Discussion A high prevalence of anemia and its associated factors was found in the institutionalized elderly in this study.
Table 1 Description of anthropometric and biochemical variables of the institutionalized , PR, Brazil (2008) according to gender elderly in Maringa Variables
Gender
Body mass* (kg)
Male þ Male Female Male þ Male Female Male þ Male Female Male þ Male Female Male þ Male Female
Heighty (cm)
BMI (kg/m2)
Iron* (mg/dL)
Albumin (g/dL)
Female
Female
Female
Female
Female
Mean
Median
95% CI
53.2 56.1 50.6 152.8 158.5 147.8 22.6 22.3 22.9 77.8 86.8 73.0 3.7 3.8 3.7
51.4 55.1 47.4 153.4 158.4 147.9 22.3 22.4 22.0 75.9 82.0 70.1 3.6 3.7 3.6
50.4–55.9 52.7–59.5 46.5–54.6 151.3–154.4 156.7–160.3 146.1–149.5 21.6–23.7 21.1–23.6 21.3–24.5 63.8–91.8 65.4–127.0 69.1–77.0 3.5–3.9 3.6–4.0 3.4–4.0
BMI, body mass index; CI, confidence interval * Independent t test. y Mann-Whitney U test.
P 0.046*
<0.001y
0.542*
<0.001*
0.801*
642
A. T. A. Nakashima et al. / Nutrition 28 (2012) 640–643
Table 2 Anemia prevalence in institutionalized elderly in Maring a, PR, Brazil (2008) according to independent variables Variable
n (%)
Gender Male Female Age (y) 60–69 70–79 80 Time of residence <1 y 1–4 y 5–9 y 10 y Body mass index (kg/m2) Underweight Eutrophic Overweight Hypoalbuminemia No Yes Iron* 50th percentile >50th percentile All subjects
Anemia n (%)
P 0.46z
58 (46.8) 66 (53.2)
15 (25.9) 21 (31.8)
29 (23.4) 44 (35.5) 51 (41.1)
6 (20.7) 16 (36.4) 14 (27.5)
23 50 30 21
7 15 10 4
0.33y
0.72y (18.5) (40.3) (24.2) (16.9)
(30.4) (30.0) (33.3) (19.1) 0.007y
74 (59.7) 34 (27.4) 16 (12.9)
29 (39.2) 6 (17.7) 1 (6.3)
81 (65.3) 43 (34.7)
16 (19.8) 20 (46.5)
49 (42.6) 66 (57.4) 124 (100)
29 (43.9) 6 (12.2) 36 (29.0)
0.002z
<0.001z
Values are presented as number of subjects (percentage) * Nine individuals were excluded owing to the impossibility of quantifying the serum iron. y Wald test for trend. z Wald test for heterogeneity.
Regarding the sampling characteristics (representative population-based sample), the findings corroborated data available in the literature [12,13]. The significant differences in BMI can be explained, at least in part, by the fact that the women
had a smaller arm circumference and a higher percentage of body fat than the men [14]. The prevalence of anemia in the participants (29%) was markedly higher than that found in non-institutionalized elderly people in developed (10.9%) [15] and developing countries (3.6– 11%) [1,12]. However, the occurrence was lower than that found by Landi et al. [6] (63%) who used the same criteria to evaluate an institutionalized elderly population in Rome, Italy. Moreover, these investigators observed that, of the 130 deaths that occurred in their sample, there was a mortality risk of 1.56 (1.07–2.28) for those with a positive diagnosis of anemia. These results are clear evidence that the institutionalized elderly are especially vulnerable to anemia. Thus, identifying its associated factors is relevant to public health. No significant associations between gender and the prevalence of anemia were found in the present study. In contrast, Marın et al. [16] observed that gender influenced the occurrence of anemia in adults (i.e., >18 y old). However, the results of Guralnik et al. [15], who analyzed a sample of adults older than 65 y old, differed from those of Marın et al., with a similar prevalence in men and women (11% and 12%, respectively). The occurrence of anemia increases with advancing age [15] and affects a large number of elderly people, apparently without gender distinction. We verified that, among the associated factors, body mass was significantly related to hemoglobin values and that there was a marginal association between underweight and a greater occurrence of anemia. The association between anemia and BMI in the elderly has been analyzed previously, and low BMI values have been found to correlate with low hemoglobin values [17]. Such results indicate that special care must be taken with the diet of the institutionalized elderly and that increasing the body mass of this population may have a positive effect on hemoglobin concentrations. Because nearly one-third of the
Table 3 Unadjusted and adjusted PRs (95% CI) of independent variables in relation to anemia for institutionalized elderly in Maring a, PR, Brazil (2008) Levelz
Variable
1
Gender Male Female Age (y) 60–69 70–79 80 Time of residence <1 y 1–4 y 5–9 y 10 y Body mass index (kg/m2) Eutrophic Underweight Overweight Hypoalbuminemia No Yes Altered iron* 50th percentile >50th percentile
Subjects (%)
Unadjusted analysis PR (95% CI)
2
3
Adjusted analysis P
PR (95% CI)
0.47x 15 (25.9) 21 (31.8)
0.81 (0.46–1.42) 1.00
6 (20.7) 16 (36.4) 14 (27.5)
1.00 1.75 (0.77–3.97) 1.32 (0.57–3.08)
7 15 10 4
(30.4) (30.0) (33.3) (19.1)
1.00 0.98 (0.46–2.09) 1.09 (0.49–2.44) 0.62 (0.21–1.84)
29 (39.2) 6 (17.7) 1 (6.3)
1.00 2.22 (1.01–4.85) 0.35 (0.04–2.72)
16 (19.8) 20 (46.5)
1.00 2.35 (1.36–4.06)
29 (43.9) 6 (12.2)
1.00 3.58 (1.61–7.99)
Py 0.49x
0.79 (0.44–1.41) 1.00 0.67{
0.73{ 1.00 1.75 (0.77–3.97) 1.29 (0.55–3.01)
0.5{
0.4{ 1.00 0.93 (0.44–1.97) 1.06 (0.49–2.27) 0.56 (0.19–1.69)
0.04{
0.05{ 1.00 2.15 (0.98–4.77) 0.32 (0.04–2.39)
0.002x
0.03x 1.00 1.82 (1.03–3.20)
0.002x
0.08x 1.00 1.73 (0.92–3.24)
CI, confidence interval; PR, prevalence ratio * Nine individuals were excluded owing to the impossibility of quantifying the serum iron. y Variables with P > 0.2 were excluded from the model. z The effect of each variable on the outcome was adjusted for other variables in the same level or above in the hierarchical model. x Wald test for heterogeneity. { Wald test for trend.
A. T. A. Nakashima et al. / Nutrition 28 (2012) 640–643
investigated individuals (27%) were underweight, it is important that this association be taken into consideration. Residents with plasma iron levels no higher than the 50th percentile had a greater prevalence of anemia in the raw analysis, although the significance of this association disappeared when the hierarchical setting was applied. Previous studies have also failed to find an association between serum iron levels and low levels of hemoglobin [4,18]. Low serum iron concentrations can be an important cause of anemia [19,20], but the values found in the present study were within the normal limits [3]. Thus, other factors that we did not analyze could be responsible for anemia, such as a deficiency of folate, B6, or B12. A strong association between anemia and the plasma concentrations of albumin has been described in the literature [21, 22]. The results of the present study confirm such an association, inasmuch as the participants with hypoalbuminemia presented a greater probability of having anemia (prevalence ratio 1.82, 95% CI 1.03–3.20, adjusted analysis) than those with normal albumin levels. A low food intake causes a decrease of albumin synthesis in the liver and a decreased production of other serum proteins, including hemoglobin [23,24]. Therefore, the association between hypoalbuminemia and anemia caused by decreases in liver protein synthesis could result from a decreased food intake, malnutrition, advancing age, or sarcopenia. The fact that we evaluated neither associated pathologies (digestive system diseases, nephropathies, or tumors) nor the use of medication that could interfere with the nutritional status or laboratory tests (owing to inconsistencies in institutional record keeping) was a limitation of this study. Moreover, we did not assess whether anemia was caused by chronic disease or nutritional deficiencies. These are important topics for further studies and could assist in the formulation of preventive measures in long-term care institutions. Limited food quality and availability are risk factors for low protein and micronutrient absorption, which decrease the synthesis of albumin and iron absorption. Intervention studies have shown that improving the food quality in long-stay institutions for the elderly has a positive effect on albumin [25], hemoglobin, and serum folate [26] concentrations. Thus, to improve the health status of residents in such institutions, the food quality should be improved by analyzing its composition, and periodical monitoring of patients’ nutritional status should be carried out. In summary, we found that the prevalence of anemia in this institutionalized elderly population was high, that it was independent of gender, and that it was associated with hypoalbuminemia. Thus, there is a real need for strategies to minimize this problem and improve the health of the elderly living in the studied long-term care institutions.
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13] [14]
[15]
[16]
[17]
[18]
[19] [20]
[21]
[22]
[23] [24]
References [1] Barbosa DL, Arruda IK, Diniz AS. [Prevalence and characteristics of anemia in an elderly population attending a Health Family Program]. Rev Bras Hematol Hemoter 2006;28:288–92. [2] Lopez-Contreras MJ, Zamora-Portero S, Lopez MA, Marin JF, Zamora S, Perez-Llamas F. Dietary intake and iron status of institutionalized elderly
[25]
[26]
643
people: relationship with different factors. J Nutr Health Aging 2010;14:816–21. Programs WH, editor. Iron deficiency anaemia assessment, prevention, and control: a guide for programme managers. Geneva: World Health Organization; 2001. Choi C, Lee J, Park K, Choi I, Kim S, Seo J, et al. Incidence of anemia in older Koreans: community-based cohort study. Arch Gerontol Geriatr 2005;41: 303–9. C, Wilson A, Ershler W. Prevalence and outcomes of anemia in Beghe geriatrics: a systematic review of the literature. Am J Med 2004; 116(suppl):3S–10S. Landi F, Russo A, Danese P, Liperoti R, Barillaro C, Bernabei R, Onder G. Anemia status, hemoglobin concentration, and mortality in nursing home older residents. J Am Med Dir Assoc 2007;8:322–7. De Maria R, Ripamonti V, Sandri R, Ceretti A, Ferratini M. The negative prognostic synergism of anemia and heart disease in female nursing home residents. Am J Cardiol 2005;96:1460–2. ~o M, Marucci M, Barbosa A. Estado nutricional e capacidade fısica. In: Lebra de, Bem-estar e Envelhecimento–O Projeto Duarte Y, editors. SABE–Sau Sabe no municıpio de S~ ao Paulo: uma abordagem inicial. Brasılia: OPAS; 2003. p. 93–118. Chumlea WC, Guo SS, Steinbaugh ML. Prediction of stature from knee height for black and white adults and children with application to mobility-impaired or handicapped persons. J Am Diet Assoc 1994;94: 1385–90. Barros A, Hirakata V. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol 2003;3:21. Victora C, Huttly S, Fuchs S, Olinto M. The role of conceptual frameworks in epidemiological analysis: a hierarchical approach. Int J Epidemiol 1997;26: 224–7. Olivares M, Hertrampf E, Capurro M, Wegner D. Prevalence of anemia in elderly subjects living at home: role of micronutrient deficiency and inflammation. Eur J Clin Nutr 2000;54:834–9. Rauen M, Moreira E, Calvo M, Lobo A. [Nutritional status assessment of institutionalized elderly]. Rev Nutr 2008;3:303–10. Organization of Pan-American Health. Salud, bien estar y envejecimento rica Latina y El Caribe. Washington, DC: Organization Pan(SABE) em Ame American Health; 2001. Guralnik J, Eisenstaedt R, Ferrucci L, Klein H, Woodman R. Prevalence of anemia in persons 65 years and older in the United States: evidence for a high rate of unexplained anemia. Blood 2004;104:2263–8. Marın G, Rivadulla P, Negro L, Gelemur M, Etchegoyen G. [Population study of the prevalence of anaemia in the adult population of Buenos Aires, Argentina]. Aten Primaria 2008;40:133–8. Choi C, Lee J, Park K, Yoon S, Choi I, Oh S, et al. Prevalence and characteristics of anemia in the elderly: cross-sectional study of three urban Korean population samples. Am J Hematol 2004;77:26–30. Ramel A, Jonsson P, Bjornsson S, Thorsdottir I. Anemia, nutritional status, and inflammation in hospitalized elderly. Nutrition 2008;24: 1116–22. McCormick L, Stott D. Anaemia in elderly patients. Clin Med 2007;7:501–4. Shi Z, Hu X, He K, Yuan B, Garg M. Joint association of magnesium and iron intake with anemia among Chinese adults. Nutrition 2008;24: 977–84. Mitrache C, Passweg J, Libura J, Petrikkos L, Seiler W, Gratwohl A, et al. Anemia: an indicator for malnutrition in the elderly. Ann Hematol 2001;80:295–8. Zakai N, Katz R, Hirsch C, Shlipak M, Chaves P, Newman A, Cushman M. A prospective study of anemia status, hemoglobin concentration, and mortality in an elderly cohort: the Cardiovascular Health Study. Arch Intern Med 2005;165:2214–20. Rothschild M, Oratz M, Schreiber S. Albumin synthesis. 1. N Engl J Med 1972;286:748–57. Rothschild M, Oratz M, Schreiber S. Albumin synthesis (second of two parts). N Engl J Med 1972;286:816–21. Manders M, de Groot CP, Blauw YH, Dhonukshe-Rutten RA, van HoeckelPrüst L, Bindels JG, et al. Effect of a nutrient-enriched drink on dietary intake and nutritional status in institutionalised elderly. Eur J Clin Nutr 2009;63:1241–50. pez-Sobaler M, Bermejo LM, Aparicio A, Rodrıguez-Rodrıguez E, Lo s P, Ortega RM. Dietary strategies for improving folate status in Andre institutionalized elderly persons. Br J Nutr 2009;101:1611–5.