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Between 2000 and 2050 the number of inhabitants aged 60 years or older in the World and in Costa Rica will double*. A considerable number of them will become frail. Frailty is considered a clinical state in which there is an increase in an individual´s vulnerability for developing increased dependency and/or mortality when exposed to a stressor.1,2
Although the operational definition of frailty is controversial, two approaches to defining frailty are widely accepted.2 The deficit model, which consists of adding an individual´s number of impairments and conditions to create a frailty index, and the second model, the phenotype model, which describes frailty as a clinical syndrome resulting from a combination of variables, such as weight loss, fatigue, weakness, diminished gait speed, and poor physical activity that reflects an underlying physiologic state of multisystem dysregulation.1,3
Multiple population studies have shown the relationship between frailty and poor outcomes, such as increased mortality rates, hospital admittances, functional decline, falls, fractures, and others,4 and some of these studies have been performed in Latin American populations and Costa Rica.4-10The present study’s objective was to identify the adverse outcomes of frailty (using the phenotype model) in the older Costa Rican population after a 4-year follow-up.
Methods
CRELES Study
A longitudinal study design was established using the Costa Rican Longevity and Health Aging Study (CRELES) study, which is a dataset of Costa Rican older adults born in or before 1946; it included a representative sample of ≥ 60-year-old adults from Costa Rica. A full description of sampling methods may be found elsewhere**. For the purpose of this report, data from the first (2005) and third (2009) waves were used. Both interviews assessed the same information, with the exception of mortality data in 2009, including a next-of-kin interview for the deceased participants. The research protocol of CRELES was approved ethical science committee of the University of Costa Rica in the March 17, 2004 session (reference: VI-763-CEC-23 -04), research project number 828-A2 -825.
Construction of the frailty variable
The frailty indicator is constructed based on the five variables from Fried’s phenotypical model3 with certain modifications. This model is one of the most used to study this condition and is first used in the population in Costa Rica. The variables were built as follows:
Weakness: the measurement of the grip strength was used. It was performed with a dynamometer in the CRELES study. The older people were asked to extend his or her dominant arm to full length alongside his or her body, and when instructed by the interviewer, the person was to grip the handle with all his or her strength and immediately loosen the grip. Then, he or she would rest for 3 minutes, repeating the test for a second time. The result of the second grip strength test was used for this study.
A regression model was calculated in which hand strength was the dependent variable and the independent variables were sex and body mass index. Then, the residual values were added to the adjusted average value to create a standardized grip strength variable. The 20th percentile was calculated, resulting in a cut-off value. The patient was considered to have an altered grip strength test if his or her result was below the 20th percentile or could not perform the test at all.
Slowness: the variable of gait speed was used. The information was obtained from the stand-up and walk test. This test consists of asking the person to stand-up from a chair and walk at his or her usual rhythm for a distance of 3 metres. This distance was divided by the elapsed time, resulting in the speed of the individual. Afterwards, a regression model was adjusted where the dependent variable was the calculated speed and the independent variables were height and sex, obtained from the CRELES database. The predicted values were calculated, and an average value was obtained. The residual values were added to this average value, which resulted in a new speed variable from which the 20th percentile was calculated.
An altered gait speed was considered if one of the following conditions was present: the patient had a gait speed lower tan the 20th percentile value, the person could not do the test at all, or if the person answered affirmatively to the question: “Do you have a problem that prevents you from doing any mobility or flexibility test?”
Low level of physical activity: For this variable, the answer to the question “In the past 12 months have you had regular exercise or rigorous physical activity such as sports, jogging, dancing or heavy workloads three times a week?” was used, and it was considered altered physical activity if the question was answered in the negative.
Exhaustion or poor endurance: The answer to the question, “Were you full of energy?”, was used. The variable was considered altered if the older people responded that they felt they had no energy whatsoever.
Weight loss: We used the answer to the question, “In the last 6 months, have you lost more than 5 kilograms of weight without planning it?”. If the patient answered affirmatively, the variable was considered altered.
Once the five variables were defined, each was codified as 1 if they met the frailty criteria for a particular variable and 0 if the criteria was not met. Those who did not present altered variables on any of the 5 measures analysed (which translates to a total sum of 0 for all variables) were classified as robust, those who presented 1 or 2 altered variables were classified as pre-frail, and those who presented 3 to 5 altered variables were classified as frail. It is worth mentioning that if any values were missing, the individual was not included, as there was a strict method to prevent biases for this categorization. This methodology was also used for the cohort follow-up years in 2005, 2007, and 2009.
For this study, we measured the incidence of some outcomes analysed in other publications. The year 2005 was considered the beginning year of the longitudinal analysis, and the year 2009 was the final follow-up year of the cohort. The specifications for these outcomes are described as follow:
Mortality: This variable was registered in the mortality database of the CRELES study according to the year. Hospital admittance: For this outcome, the questions “Were you admitted to a hospital?” and “For the past 12 months, how many nights did you spend in the hospital?” were used. A variable was created based on the answers, and two categories were established: those who were admitted one or more times and those who were not admitted at all for the years analysed. Functional decline: To create this variable, the older people were asked if he or she had difficulty doing any of the following 6 activities: walking, climbing flight of stairs, bathing, feeding, going to the bathroom, or having faecal or urinary incontinence.
For the walking variable, the questions “Can you walk?’, “Is it difficult for you to walk multiple blocks?” and/or “Is it difficult for you to cross a room walking?” were used.
For the climbing a flight of stairs variable, the question “Is it difficult for you to climb a flight of stairs without pausing or resting?” was used.
Regarding the feeding variable, the question “Do you have difficulties in feeding, cutting food, filling cups, etc.?” Was considered.
The use of the bathroom variable was assessed by the answer to the question “Is it difficult for you to use the bathroom or sitting and standing up from the toilet?”
Finally, for the variables of faecal and urinary incontinence, the following questions were used: “For the past 12 months, have you had any problems on an almost daily basis with involuntary loss of urine?” or “Involuntary loss of faeces?” The aforementioned variables were analysed in a dichotomic form so that a total sum of 0 to 6 points was established (considering 1 as a negative point and 0 as positive point for each factor). Therefore, if there were changes in functionality from 2005 to 2009, these would be reflected by the changes in the total sum of the points for each year; specifically, it was considered a functional deterioration if a person in 2009 had a higher score than that obtained in 2005.
Falls and self-perception of health: Both variables were considered just as they were answered and recorded in the CRELES database, which used the following questions: “Have you fallen any time during the past two years?” (not including falls due to drunkenness) and for the health self-perception ítem “How would you say your health condition is now: excellent, very good, good, regular, bad?” It is worth noting that for this last variable, the answers were considered healthy if the response was “excellent, very good and good” and unhealthy if the response was “regular or bad”. By performing this grouping, the dichotomic nature of the variables was upheld for both cases.
Once the outcome variables were obtained, an analysis was made using a multinomial logistic regression; for the present study, these outcome variables were used as dependent variables and a model was built for each of the five outcomes.
With regard to the independent variables, interest was focused on how the frailty phenotype was associated with each proposed outcome. With these models, in addition to including frailty condition as an independent variable, a wide variety of other variables (age, gender, comorbidities, among others) were included, which were used to adjust the model estimation, and the decision to include or exclude them was made according to the criteria for variable selection.
Once an adequate model was obtained, odd ratios were calculated for the outcome categories considered least healthy (i.e., prefrail and frail). This was because the outcome category considered healthy (robust) was the one used as reference. With these odd ratios, the respective 95% confidence intervals.
Table 1 Costa Rica: Prevalence of health and sociodemographic characteristics of the elderly of analyzed on the year 2005*
| Health and sociodemographic characteristics | Percentual distribution |
|---|---|
| Age | |
| 60 -69 | 53,8 |
| 70 -79 | 31,6 |
| 80+ | 14,7 |
| Sex | |
| Female | 52,5 |
| Male | 47,5 |
| Education | |
| ≤ 6to | 78,5 |
| 7mo-9no | 7,7 |
| ≥ 10mo | 13,8 |
| **Income | |
| ≤ 100 US dollars | 41,9 |
| 100-520 US dollars | 38,1 |
| ≥ 520 US dollars | 20,0 |
| Physical activity | |
| Yes | 31,4 |
| No | 68,6 |
| Live alone | |
| Yes | 9,9 |
| No | 90,1 |
| Health self-perception | |
| Healthy | 52,7 |
| Unhealthy | 47,3 |
Note: The total analyzed population was 2827. *Base year. **1 US dollars approx 480 colones. (2005) Abbreviation: US, United State Dollars.
were estimated, mainly focusing on the relationship between outcome and frailty. To carry out the analysis, all the available cases of the CRELES study database which belong to the cohort that follows in the period 2005-2009 were used. In the year of the initial cohort (2005), there was a total of 2827 patients, and counting for the final year of the cohort with 1863 patients. There are no analytical exclusion criteria because they point in the same direction as those already carried out by the CRELES study, where 525 cases were lost due to mortality and 439 because they could not be contacted to follow them.
Results
The final sample of the base year 2005 was composed of 2827 individuals with ages ranging from 60 to 109 (average of 70 years). 52,5% were women. The sociodemographic and health characteristics of the analysed patients are summarized in tables 1 and 2. 53.8% were under the age of 69 years, 31.6% between the ages of 70 to 79 years and 14.7% presented ages from 80 or above. The education level of the study population was low, for this study 78.5% presented an education level of less than 6 years of schooling. At the economic level, 41.9% presented an income of less than 100 United State Dollars
(USD) and an 20% presented data of more than 520 USD per month. 9.9% of the analysed patients lived by themselves and a 47,3% presented a poor health perception.
Regarding comorbidities seen in the frail patients, 48,4% has hypertension, 20.8% Diabetes, 16,7% pulmonary illness, 9.7% osteoporosis, 14.6% arthritis, 3.8% with cerebrovascular events, 5.9% cancer, 4.6% coronary disease and 39.6% had dyslipidaemia (table 2).
Table 2 Costa Rica: Prevalence of comorbidities of analyzed elderly on the year 2005*
| Variable | Percentage (%) |
|---|---|
| Arthritis | 14,6 |
| Falls | 46,0 |
| Cerebrovascular event | 3,8 |
| Osteoporosis | 9,7 |
| Hypertension | 48,4 |
| Diabetes | 20,8 |
| Cancer | 5,9 |
| Pulmonary Disease | 16,7 |
| Heart attack | 4,6 |
| Cholesterol | 39,6 |
| Smoking | 43,1 |
Note: The total analyzed population was 2827. *Base year.
For the base year 2005, the final analysis shows a prevalence of frailty in the older population of 11%. The general characteristics of the frail patient in Costa Rica were analysed in a previous prevalence study (11). Using a model based on Fried´s with certain modifications, the Costa Rican prevalence of frailty in the CRELES Study for 2005 was 11.6 %, of prefrail 65.5% and 22.9% robust.11
From the analysed outcome variables of this Costa Rican population, frailty was associated with functional decline (OR 4,79, CI 1,89; 12,11), hospital admittance (OR 3,21, CI 1,85; 5,86), and worsening of the self-perception of health (OR 5,17, CI 3,45;7,75).
Frailty was not associated with increased mortality rates (OR 1,9; CI 0.87;4,2) or a higher risk of falls in the Costa Rican population (OR 1,36; CI 0,93;1,95) (Table 3).
Discussion
Although there have been studies that have measured the outcomes related to frailty in the Costa Rican population,10 this is the first study, to the knowledge of the authors, that identify these outcomes in the older Costa Rican population using the phenotype model to define frailty. In this study, frailty was associated with conditions that impact the health care systems for older people, as well as their quality of life, such as functional decline, hospital admittance, and a worsening of the self-perception of health. Regarding the functional decline outcome, after adjusting the variables for age, hypertension, diabetes and lung disease, it was evident, as was mentioned, that there was a risk of such an outcome associated with frailty. This risk is even greater than that detected in other related epidemiological trials.5 This correlation was also observed in pre-frail patients (OR 3,27; CI 1,35;7,79). This result matches the findings of the majority of longitudinal trials conducted around the world.12,13
The physiopathology of frailty at the central nervous, hormonal, and immune system levels directly affects the balance and homeostasis of the muscle and leads to sarcopenia1 that explains the findings of this study regarding functional deterioration and performance.
Table 3 Costa Rica: Results of the models of incidence by type outcome analyzed according to the state of frailty presented, 2005 and 2009
| State of frailty1/ | Functional decline | Hospitalizations | Health perception | Falls | Mortality | |||||
| OR | CI | OR | CI | OR | CI | OR | CI | OR | CI | |
| Prefrail | 3,25 | 1,35; 7,79* | 1,93 | 1,16; 3,18* | 2,17 | 1,63; 2,89* | 1,01 | 0,77; 1,33 | 1,43 | 0,70; 2,92 |
| Frail | 4,79 | 1,89; 12,11* | 3,29 | 1,85; 5,86* | 5,17 | 3,44; 7,75* | 1,36 | 0,94; 1,95 | 1,91 | 0,87; 4,18 |
*p<0,05. CI= Confidence Interval at 95%; OR= Odds Ratio. 1/ Category of reference: “Robust”. Note: A model is made for each outcome by adjusting different variables.
this relationship.3,7,13-17,18-21 Reasons that explain this finding are similar to what was mentioned above. The phenotypic model used in this study has had great support by other authors in different publications. However, its structure from sample variables from a study not originally designed for this purpose may present certain difficulties in a reliable outcome. Therefore, it can overestimate or underestimate the actual condition of frailty. The same can be said of the follow-up, since it could not corroborate to 100% the deaths, hospitalizations or other adverse results, and that could be due to accidents, other illnesses, or other reasons and not necessarily frailty.
Despite the study limitations mentioned above, the results generated do represent the Costa Rican population. Furthermore, the criteria used for this study were more similar to Fried’s original criteria when compared to previous studies at the national and international levels.3
The impact of the frailty syndrome and the significant outcomes for the Costa Rican older population was demonstrated, providing an overall picture of this important condition in this area of the American continent. This allows us to assess the needs for future investigations in this area and to take preventive actions such as caloric and protein support, exercise intervention (resistance and aerobic), reduced polypharmacy, and treat Vitamin D deficiency,1,2involving specific groups of older people to avoid the frailty síndrome and its consequences, which can affect health care systems and the quality of life for older individuals.
Frailty was associated with adverse outcomes such as a higher risks of hospital admittances, functional deterioration, and worse self-perception of health in the Costa Rican population. This risk is present even in pre-frail patients, which then represents an opportunity for an early intervention to avoid these outcomes. Frailty was not associated with higher mortality rates or with a higher number of falls.
