How do education, occupation and gender contribute to physical frailty in the community-dwelling elderly? AbstractIntroduction: Frailty is a syndrome which is common in geriatric patients. It is connected with the ageing process and results in a huge cost to the NHS. The aim of this paper is to review literature in order to identify how education, occupation and gender contribute to physical frailty in later life.
Method: A systematic search was conducted of studies published after 2000 to find those that linked education, occupation or gender, with physical frailty in the community-dwelling elderly. The following databases were used: PubMed, Medline Ovid, SCOPUS and Cochrane.Discussion: Some of the studies do show that gender, occupation and education have a significant contribution to frailty.
Gender may have a greater impact on pre-frailty than frailty, however not enough studies were conducted that reported pre-frailty. Conclusion: There is a link between occupation or gender and frailty, however further research is required. Gender contributes the most to physical frailty out of the factors studied, however behavioural factors such as smoking and alcohol act as mediators. Lower levels of education, can be shown to lead to a higher prevalence of frailty when compared to university level education.
IntroductionFrailty is a syndrome which is linked to the ageing process. Those who are frail are at greater risk of having ‘dramatic changes in their physical and mental well being after an apparently minor event which challenges their health, such as an infection or new medication’ (British Geriatric Society, 2014). Prevalence of frailty rises with age, one in ten of people who are 65 and over are frail and this increases to one in four for those aged 85 and over (Mortimer and Green, 2015). With a growing and ageing population the impact of frailty on NHS spending will increase and add more strain to an already financially struggling healthcare system. By analysing studies to see how education, occupation and gender contribute to physical frailty in later life, preventative measures may be taken in future. MethodThe definition of frailty used in this report is the frailty phenotype which defines frailty as meeting three out of five of the following criteria: self-reported exhaustion, unintentional weight loss (10 lbs in the past year), weakness (grip strength), slow walking speed and low physical activity (Fried et al., 2001). Pre-frailty is thus described as meeting one or two of these criteria.
This definition is used as it is the most common and it describes frailty as purely a physical condition.A systematic literature search was carried out on the following databases: PubMed, Medline Ovid, SCOPUS and Cochrane. A grey literature search was also performed to find published material that could provide further information. The websites used were: Google Scholar and British Geriatrics Society.
The Boolean search technique was used and the key terms searched for were a combination of the following ‘frail’, ‘socioeconomic factors’, ‘occupation’, ‘gender’ and ‘education’. Table 1 in the appendix shows an example of a search carried out on Medline Ovid. Inclusion Criteria – Studies which were published from 2000 onwards have been used in this review. Only studies that provided data on the community-dwelling elderly aged 60 years old or greater were included.
Studies carried out in all locations were used. However those carried out in countries with a similar sociodemographic to the UK (United Kingdom) were taken into greater consideration. All the studies used the definition of frailty as proposed by Fried et al. There was no lower or upper time limit for any of the studies. Exclusion Criteria – Studies which focused on cognitive and not physical frailty were not included.
Those that included participants with chronic diseases were also excluded. If the study only had one author it was also excluded as the probability of bias was high. Data Extraction – The following data was extracted from each study and tabled: author, publication year, location, sample size, age of population and type of study. The aim was to extract data that met the inclusion and exclusion criteria mentioned above. This data is shown in table 2 in the appendix.DiscussionIn this report, education was considered as the number of years spent in education rather than knowledge one may have come across by other means.
Although there is correlation between level of education and smoking and alcohol intake, only one study was found to take this into account (Soler-Vila et al., 2001). Occupation was split into two types, manual labour and non-manual labour.
Non-manual labour included professional and managerial jobs, income was not looked at in the studies. A cross-sectional study in the urban areas of Brazil was conducted with 1609 subjects aged 60 years or older (Tavares et al., 2017). The results from this study show that the probability of women being more pre-frail than men is 0.
017. As p = <0.05, it means that a significant difference does exist.
However this difference is not significant in frailty. This may because women more likely to live longer than men and therefore have a longer period of time to be classified as pre-frail (Ginter and Simko, 2013). The prevalence of pre-frailty and frailty is higher in women, and of the 219 subjects classed as frail, 74% were female.
Tavares et al concluded that there was no connection between years of education and frailty or pre-frailty. The p=0.978 for 0 years of education and since this is greater than 0.05, there is no significant difference. The study was only conducted in the urban areas of Brazil and had rural areas been included it is possible the results would have skewed in favour of males. This is because there is a likelihood that men in rural areas would be involved in much more physical labour compared to the women (Ramirez and Ruben, 2015) and therefore more likely to be frail.
Although prevalence of frailty has been shown to be the same between rural and urban areas in Latin America, whether or not this affects gender is unknown (Curcio et al., 2014).Similarly, another cross-sectional study was carried out in Rio De Janeiro, with 847 people aged 65 years or older who were selected by inverse randomised sampling (Moreira and Lourenco, 2013). This study did not take pre-frailty into account, but did show that the probability that the difference in gender affects prevalence of frailty is less than 0.001. Therefore the impact of gender on frailty, with women being more frail than men, is significant. Prevalence of frailty in females was also greater, however 66.
9% of the subjects were female so this may have affected the results and there would have been more frail women. The results also showed a link between education level, in terms of years, and frailty. Those who had been educated for at least 6 years were less likely to be frail than those who were illiterate. As p = < 0.001 this difference is significant and implies that education can affect physical frailty in later life.
This study did rely on self reported weight loss which is not reliable as people may have provided incorrect information. Only those who had been members of a healthcare plan for over 12 months were included in the study. This therefore does not provide information for people who may not be able to afford a healthcare plan. Both of these studies were conducted in Brazil which has a different sociodemographic to the UK so the results may not be applicable and will need to be repeated in the UK or a country with a similar demographic to be confirmed.A cohort study, with 1857 subjects aged 60 and over, was conducted in Spain (Soler-Vila et al. 2016) which indicated that the effect between occupation and frailty was only significant in women.
The type of occupation was classified into manual and non-manual labour. 15.84% of women in manual labour were frail and only 6.70% of those in non-manual labour were frail.
Whereas in men, the difference was not significant as 3.60% of men in manual work were frail and 4.47% of men in non-manual work were frail. The meditators between occupation type and frailty were factors such as alcohol intake and diet but they only accounted for 15% of the probability of frailty being linked to manual labour. However housewives in this study were classed by their partner’s occupation.
This makes the results unreliable as manual labour would not then affect the women personally. Much like occupation, the level of education and the incidence of frailty had a much more significant difference in women than in men. Of the women whose education level was of primary level or less, 13.
34% were frail and only 4% of those who were educated to university level were frail. In this study there were a number of behavioural factors that were associated with education and frailty. Those with lower education levels were likely to drink more alcohol and spend more time watching tv. However sedentary time spent on screens other than tv, for example like computers, was not taken into account. In South Australia, a longitudinal cohort study of 4060 adults aged 18 years and over was carried out (Grant et al., 2008) to understand population health. A secondary analysis was conducted on this study that only included the 909 participants who were aged 65 years or older (Thompson et al.
, 2017). Using the frailty phenotype, 18% were classified as frail. The study did not show that gender or education had any effect on the number of subjects who were frail or non-frail. 91%of those with bachelor degrees were non-frail and 80% of those with an education level up to secondary were also non-frail. As this study was conducted in Australia, its results could be applied to the UK, however only the secondary analysis focused on frailty and the older population. Education was also not classed at lower levels unlike other studies. The lowest level included illiterate to secondary education, whereas a significant difference was seen in lower years in other studies such as the one Rio De Janeiro (Moreira and Lourenco, 2013).
This is a secondary analysis of a study conducted and therefore the results may be inaccurate due to the interpretation by Thompson et al.A prospective study was conducted in the USA on 40,657 women aged 65 to 79 at baseline (Fugate Woods et al., 2005).
6619 of the participants, 16.3%, were classified as frail at baseline and 11,517 (28.3%) were classed as pre-frail.
Those classed as frail at baseline were more likely to be older than those who were classed as non-frail. 19.7% of women who had the equivalent of a high school education or less were non-frail and 43.5% of women a college degree level education of greater were non-frail. This implies that those with a college degree level education or greater are twice as likely to be non-frail than those who have a high school education or less.
However there was no significant difference in the percentage of women in the different levels of education in pre-frailty and frailty. Conclusion:The aim of the study was to find out to how occupation, education and gender affected physical frailty in the community-dwelling elderly. I found that out of the social factors studied, gender had the greatest contribution to physical frailty in community-dwelling elderly. This is because behaviour factors such as smoking act as mediators between gender and frailty.
The effect of gender may be more significant in pre-frailty than frailty, however only one study took gender and pre-frailty into account. The effect of education on frailty seems to be more important if the level of education received is below primary. More research is required to see if gender, occupation and education have a significant contribution to frailty and pre-frailty in the UK. Longitudinal studies are needed to see how pre-frailty may lead to frailty and whether social factors affect this too. AppendixTable 1, Table showing an example of one literature search carried out on Medline OvidDatabaseKey TermsAndNotNumber of hitsMedline OvidEconomic or education or socioeconomic factors or gender or occupation or job or employ* or school*frail*, community-dwellingassessment400Table 2, Table showing the data extraction from each study usedAuthorsPublication YearCountrySample size at baselineSample age in years at baselineType(s) of factor(s) included in the studyStudy typeTavares et al2017Brazil 1609?60Literacy, monthly income, marital statusCross-sectional, household, observational studyMoreira and Lourenco2013Brazil847?65Gender, age group, race, marital status, education level, functional capacity, fallsCross -sectional descriptive studySoler-vila et al2016Spain1857?60Education, occupation, age, tobacco smoking, alcohol intake, sedentary behaviourCohort studyThompson et al2017Australia909?65Education, occupation, age, tobacco smoking, alcohol intake, marital statusSecondary analysisWoods et al2005USA40,65765-79Education, smoking, alcohol use, living aloneProspective studyReferencesBritish Geriatrics Society.
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