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디카교실

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실버넷 만평

전국의 아름다운 길

은퇴 후 자산관리

외국어

기타

확대 l 축소

Social determinants of multimorbidity and multiple functional limitations among the ageing population of England(1/2)

- 우리는 수(壽)와 복(福)을 누릴 수 있다 -

우리에겐 생···(···)라는 글자는 그렇게 생소하지 않다. 이 네 글자의 흐름에서 공통적인 맥을 찾는다면, 이는 에이징(Aging)이다. ()에서 출발한 ‘Aging’은 가령화 과정(加齡化 過程, Adding Age)을 거치기 때문이다. 이 과정은 한 해, 두 해가 지느냐면서 나이를 더하게 된다. 그리고 50세에 도달하면 노화 과정(Aging)에 이르렀다고 한다.

청년기부터 계속해서 음주, 흡연, 과로, 긴장 등으로 병()에 걸렸다면, 또한 가족역(家族疫)까지 겹쳐 있다면, 어떻게 되겠는가? 그래도 낳아 길러주신 부모님, 특히 어머님은 자녀의 수복(壽福)을 기원한다. ()는 장수이다. ()은 건강하고, 안녕하고, 행복으로 이어진다. ()와 복()은 건강에 기초한다.

특히 우리의 몸은 정확한 반응을 보인다. 내가 내 몸을 추스르지 않으면, 그 무언가 고통을 준다. 이 고통은 자각(自覺)증세에 의하여 느끼게 된다. 이때 서둘러 내 몸을 보살펴야 한다. 온몸 전체를 위에서 아래까지’, 발목부터 발가락 마디 마디까지 하나도 빠짐없이 챙겨야 한다. 그러나 타각(他覺) 증세로 알려진 나의 아픔은 이미 늦은 감은 있지만 어떻게 하여야 하나?

이와 같은 물음에 답을 찾기 위하여 2002년부터 2015년까지 10여 년을 연구한 영국의 주요 논문 한 편을 번역 기사로 2회에 나누어 게재한다. 50세 이상 인구의 건강증진에 관한 내용이다. 한 사람이 겪게 되는 두 가지 이상의 완치되지 않는 고질적인 만성 질병 이환율(罹患率)과 육체적, 사회·심리적, 행태·사회적 결정요인 간의 종단적 관계를 탐구한다.

이웃의 부부가 편찮은 노인을 방문하여 담소하고 있다. 사진: regis.com.au. 캡처

노화의 생물학적인 특성을 고려해서 건강에 대한 두 가지 별개의 척도를 세웠다. 이 척도는 한사람이 겪게 되는 두 가지 이상의 완치되지 않는 고질적인 만성 질병 이환율과 복수의 기능 한계자의 관계를 의미한다. 사람들은 음주, 흡연 및 만성 질병 이환자(罹患者)와의 관계에서 나타나는 반응으로써 약을 먹는 것을 찾지 못하였다. 특히 자신의 건강 상태를 적극적으로 완화하려는 활동이 없었다.

환자의 개별 위험에 대한 생의학적 및 사회 인구학적 특성에 조준하면서도, 노령인구의 개별특수성 이외에 늘어나는 건강 문제를 이해할 필요성이 있다. 만성 질병 이환자의 경향성과 개별특수성 이외의 사회환경과 상호관계를 동시에 진행한 연구는 많지 않다. 노년층에서의 만성 질병 이환자는 젊은 층에서 나타나는 것과는 다른 측면이 있다. 예를 들어 심혈관 및 신경학적 패턴의 유병률은 나이에 따라 증가하지만, 정신 건강 장애는 젊은 사람들 사이에서 더 흔하다.

만성 질병 이환(罹患)의 개념은 체내 세포 손상의 축적을 반영하고 있다. 이러한 세포 손상은 전 생애 과정에서 누적된다. 그리고 모든 신체의 체계를 유기적으로 조절하는데 만성적인 장애를 입고 왔었다. 병을 쌓아가는 것은 신체적 체계를 유기적으로 조절할 수 없고, 질병으로부터 회복력이 상실되며, 빠른 노화의 진행을 알려주는 획기적인 사건들의 이정표이다. 만성 질병 이환의 생성과정에서 모든 신체의 체계를 유기적으로 조절하려는데 거북한 암시를 받기 시작한다.

연구의 출발점은 여러 신체 시스템에서 역기능의 중심으로 조준된다. 이 기능은 중복장애, 기능 제한 및 질병의 유발이다. 만일 개인에게 영향을 미치는 다양한 사회적 결정요인을 확인할 수 있다면, 일련의 신체 시스템에서 변화를 종합적으로 관찰될 수 있다. 여기서 하나 이상의 건강 조건보다 더 많은 호스트 반응을 아우르는 일정 범위를 어떻게 생성했는지를 긴장, 빈곤 또는 주거 생활의 질이 포함된 사회적 결정요인의 조합 방법을 통하여 보여준다.

이환율(罹患率, morbidity)은 일정 지역 사회에서 혹은 어느 한 조직에서, 동일 연령 집단이 두 가지 이상의 어떤 질병에 앓고 있는 이환율 또는 이환자(罹患者) 수이다. 어떤 질병이란 개별환자의 증세에 차이가 있으며, 유형별 질병에 따라 특수성이 있다. 어느 질병은 잘 낫지도 않고 계속 약을 먹어야 한다. 그러고 완치는 어렵다.

연구의 주된 흐름은 질병의 동반이환[同伴罹患, Comorbid(C)], 만성 질병 이환율[Morbidity(M)], 복수의 만성 질병 이환율[Basic Multimorbidity(B2M)], 세 가지 이상의 만성 질병 이환율[Complex multimorbidity(C3M)], 복수의 만성 질병이 진행 중인 이환율[Multimorbidity condition of Being Multimorbid(MBM)], 복수의 기능 한계자[Multiple Functional Limitations(MFL)], 건강의 사회적 결정요인(Social Determinants of Health) 이다.

위와 같은 여러 갈래의 이환율에 해당하는 질병은 이환(Morbidities), 몸의 체계(Body Systems), 그리고 기능 한계로 나누어 기술한다. 이환율(罹患率)은 이환의 징후가 있는 질병 25가지이다. 고혈압, 협심증, 울혈성 심부전, 심 잡음, 비정상적인 심장 고동, 심장 마비, 당뇨병, 뇌졸중, 폐병, 천식이다. 관절염, 골다공증, , 파킨슨병, 치매, 알츠하이머병, 환각, 불안, 우울증, 정서적 문제이다. 기분 변화, 녹내장, 당뇨병성 안질, 황반 변종, 백내장이다.

치매 걸린 아내와 남편이 대화하고 있다. 사진: aplaceformom.com 캡처

몸의 체계(Body System)는 다음과 같이 8가지로 나누고 있다. 이들 8가지를 소체계(Subsystem)로 나누어 해당 질병명을 나열한다. 눈 질환은 녹내장, 황변변종, 백내장; 순환 장애에는 고혈압, 협심증, 심장 마비, 울혈성 심부전, 심 잡음, 비정상적인 심장 고동, 일상생활의 뇌졸중; 내분비, 영양 및 대사(代謝)는 당뇨병성 안질환과 당뇨병; 근골격계 및 연결에는 골다공증과 관절염; 호흡기는 폐병과 천식; 종양은 암; 신경 장애는 파킨슨병, 알츠하이머병, 환각; 정신적 및 행동적 변인은 불안, 우울증, 정서적 문제, 기분 변화이다.

기능의 한계(Functional Limitations)는 생활의 실제에 있어 외부의 자극에 대한 정신적·신체적 능력이 따르지 못하는 한계이다. 일반적인 이동성은 100 야드 걷기, 2시간 동안 앉아있는 것, 의자에서 일어나기, 여러 계단을 오르기, 한 계단 오르기, 몸을 굽히고, 무릎 오그리고 움츠리는 것, 어깨 위에 팔 얹기, 의자를 당기거나 밀기, 무게 10.3b(4.67kg) 들고 옮기기, 5잎 동전 줍기다

일상생활 활동은 드레싱 (양말과 신발과 신기), 방에서 거닐기, 목욕 또는 샤워, 음식 자르면서 먹기, 침대 안팎에서 움직임, 화장실 변기 뚜껑 여닫기, 지도를 사용하여 주위 접근 방법 알아보기, 뜨거운 식사 준비, 식료품 쇼핑, 전화 걸기, 약 복용, 집이나 정원 주변에서 일하는 것, 돈 관리 및 청구서 지급·경비 확인이다. 징후 혹은 증상에는 0.25마일 (0.4Km) 걷기 어려움, 일반적 통증, 시력 문제, 청력 상실, 평평한 표면에서 균형 유지, 수평면에서 걸어보니 현기증 나기다.


김수일 기자 dsmym@silvernetnews.com


Original Text    

Social determinants of multimorbidity and multiple functional limitations among the ageing population of England, 20022015    

Abstract

This study explores longitudinal relationships between material, psycho-social and behavioural social determinants of health and multimorbidity of people aged 50 years or older in England. We used data from the English Longitudinal Study of Ageing collected biannually between 2002 and 2015. Apart from the basic measure of multimorbidity (two or more diseases within a person) we constructed two distinct measures of health in order to take into account the biology of ageing (complex multimorbidity and multiple functional limitations).

We found that the likelihood of multimorbidity and multiple functional limitations was consistently associated with the levels of household wealth, sense of control over one's life, physical activity and loneliness. Larger health inequalities were observed when health was measured as complex multimorbidity and multiple functional limitations than basic multimorbidity. Compared to the population group with the highest wealth, those with the lowest wealth had 47% higher odds of basic multimorbidity (95% C.I. 1.34-1.61), 73% higher odds of complex multimorbidity (95% C.I. 1.52-1.96) and 90% higher odds of having 10 or more functional limitations (95% C.I. 1.59-2.26). We did not find a dose-response relationship between alcohol consumption, smoking and multimorbidity but rather evidence of people in ill health actively moderating their health behaviour.

We suggest that materialist models of multimorbidity and functional limitation at older age can not, on their own, explain the health inequalities as the behavioural and psycho-social factors play an important role. Policies aiming to reduce the risk of multimorbidity and functional limitation should address the issue at these three levels simultaneously, using the existing national infrastructure of General Practices.    

1.Introduction

Multimorbidity, the co-occurrence of two or more diseases within a person, affects over a quarter of primary care patients older than 18 years of age in England (Cassell et al., 2018). Individuals with multimorbidity have higher rates of GP consultations, prescriptions, and hospitalisations compared to people without multimorbidity (Salisbury, Johnson, Purdy, Valderas, & Montgomery, 2011; Cassell et al., 2018). Multimorbidity also leads to lower health-related quality of life (Bayliss et al., 2012; Peters et al., 2018) and decline in physical functioning (Jindai et al., 2016). Multimorbidity among people older than 65 years in England is set to rise with prevalence projected to increase from 54% in 2015 to 67.8% in 2035 (Kingston et al., 2018). People will live longer lives in worse health and this will increase the utilization of health services and the costs of health care (Cassell et al., 2018; Kingston et al., 2018).

While current studies of multimorbidity focus on the impact of biomedical and socio-demographic characteristics on patients’ individual risk (Northwood, Ploeg, Markle-Reid, & Sherifali, 2018), we also need to understand the extra-individual factors contributing to the increase of multiple health problems in the ageing population. Only a few studies have examined simultaneously longitudinal trends in multimorbidity and their relationship with extra-individual factors such as society and environment (Dhalwani et al., 2017; Jackson, Dobson, Tooth, & Mishra, 2015; Mounce et al., 2018; Schäfer et al., 2012). None of them referred to any theoretical framework that would justify the choice of the contextual characteristics. This leads to the risk of omitting relevant factors which might explain more of the outcome variance and to exaggerating effects of the observed characteristics (Frohlich, Corin, & Potvin, 2001). Choosing an appropriate measure should be backed by theory too. For instance, education and income reflect different mechanisms through which socio-economic status operates (Demakakos, Nazroo, Breeze, & Marmot, 2008). We argue that multimorbidity should be studied with the help of the theories of social determinants of health (SDoH). These refer to the social, cultural, economic, and political conditions that influence the health of individuals and populations (De Maio, Mazzeo, & Ritchie, 2013; Lucyk & McLaren, 2017).

Multimorbidity in older people may have a different profile than that found in younger people. For example the prevalence of cardiovascular and neurological patterns (including dementia and Alzheimer's disease) in England increases with age while mental health disorders are more common among younger people (Public Health England, 2018). Further, we suggest that measuring multiple health problems of older people should be consistent with our knowledge of biological ageing. The concept of multimorbidity should reflect the build-up of damage within cells (Austad, 2009; Barnes, 2015; Kirkwood, 2008) that accumulates during the life course and leads to a chronic dysregulation of multiple body systems (Fabbri et al., 2015; Li et al., 2015). Accumulation of diseases is a milestone for system dysregulation, loss of resilience and accelerated ageing (Fabbri et al., 2015). The role of body systems in development of multimorbidity is beginning to receive some attention (Yarnall et al., 2017).

Our study seeks to address these gaps in understanding multimorbidity of ageing people. Along the basic definition of multimorbidity (Van den Akker, Buntinx, Metsemakers, Roos, & Knottnerus, 1998) we propose two measures of health which in our view better reflect the biological process of ageing: complex multimorbidity (Harrison, Britt & Henderson, 2014) and multiple functional limitations. These outcomes should not be omitted when studying multimorbidity as they have implications for quality of life, need for health care and residential care, and premature mortality of old people (Jindai, Nielson, Vorderstrasse, & Quiñones, 2016; Zulman, Pal & Wagner, 2015). Our approach is also novel in that it brings together new measures of multimorbidity and functional limitation with social theory of SDoH in a longitudinal design. The aim of our study is to explore the association of material, psycho-social and behavioural determinants to the probability of developing basic multimorbidity, complex multimorbidity and multiple functional limitations in the ageing population of England over a 14 year period.    

1.1. Theoretical framework

Our starting point is the centrality of dysfunction in several body systems that is conducive to multiple impairment, limitation and disease. We postulate that if we can identify diverse social determinants that simultaneously affect an individual, changes could be observed across a number of body systems that will be involved in generating compound health outcomes. Here we follow the Generalized Health Impact model by White, O’Campo, Moineddin, and Matheson (2013) that showed how a combination of social determinants (stress, poverty or quality of housing) generated a range of host responses encompassing more than one health condition. This model informed our approach to the choice of social determinants and for measuring multimorbidity and multiple functional limitations.

1.2. Measuring multimorbidity and multiple functional limitations

Multimorbidity has been measured by a range of methods. In primary care settings, indices based on diagnostic or pharmaceutical data have been used such as Charlson Index, Adjusted Clinical Groups System or Cumulative Illness Index Rating Scale (Diederichs, Berger, & Bartels, 2011). Multimorbidity estimates in general populations are based on a simple unweighted enumeration of the number of diseases. The most common definition is “the co-occurrence of two or more diseases within a person” (Van den Akker et al., 1998) but different cut-off points have been used too (Marengoni et al., 2011). In our study this measure will be called basic multimorbidity in order to distinguish it from two other measures. The limitation of the concept of basic multimorbidity is that it leads to very high estimates among old people (55% to 98% between studies) which may be less informative than other definitions (Marengoni et al., 2011). Neither does it differentiate between the co-occurrence developing within one body system and two or more systems. Multimorbidity may have a larger impact on overall health if it arises out of disparate conditions (such as physical and mental health) rather than closely related comorbidities (Piette & Kerr, 2006; Yarnall et al., 2017).

The construct of complex multimorbidity addresses these issues. It has been defined as “the co-occurrence of three or more chronic conditions affecting three or more different body systems within one person without an index chronic condition” (Harrison, Britt, Miller, & Henderson, 2014, p. 8). Individuals with chronic conditions in 3 + body systems may require more complex care, as chronic conditions in different body systems are likely to compete for treatment, while conditions within the same system are more likely to be complementary (Piette & Kerr, 2006). With regard to the theories of ageing based on dysregulation of body systems described earlier, we argue that complex multimorbidity might be a more appropriate measure for ageing people than basic multimorbidity.

Finally, the measure of multiple functional limitations provides an idea of the impact of multimorbidity on the ageing population. Functional limitations are defined as restrictions in performing vital situation-free physical actions needed in everyday life (Verbrugge & Jette, 1994). This aspect is important as some morbidities (e.g. high blood pressure) may have less of an impact on the quality of life than others (e.g. arthritis). The measure of multiple functional limitations reflects the knowledge that the proportion of old people with physical impairments and limitations in multiple body systems increases with age (Burden of Disease Network Project, 2004; Jindai et al., 2016). Most studies have explored prevalence and effects of single impairments or functional limitations but we know less about the relationships between combined burden of impairments and functional limitations and social determinants (Burden of Disease Network Project, 2004).    

1.3. Social determinants

The theoretical approach to health inequalities and the role of SDoH in the UK was shaped by the publication of the Black Report in 1980 that concluded that material conditions were the major determinant of health and premature mortality (Black, 1992). This led to discussions between proponents of the materialist explanations and those who claimed that health inequalities are result of culturally mediated choices and behaviours (Bartley, 2004; Cockerham, 2007). The debate has been enriched by a third perspective, the role of psycho-social factors highlighted in the Whitehall II Study (Marmot et al., 1991). The current approach is to understand these hypotheses as complimentary rather than mutually exclusive and to assess their effects in one model with three groups of determinants (Robertson, Benzeval, Whitley, & Popham, 2015; Van Oort, Van Lenthe, & Mackenbach, 2005).

Material determinants refer to the distribution of income and wealth in society and to resources that allow people to secure goods and services needed for a healthy life, e.g. housing, healthcare (Bartley, 2004; Cockerham, 2007). Attained education can be interpreted as another type of material resource as it mediates health risks on the pathway between childhood conditions and occupational level, income and accumulated wealth in later life (Northwood, Ploeg, Markle-Reid, & Sherifali, 2018). Studies of the ageing population in England and the UK found disparities by socio-economic status (SES) for a range of health outcomes (Nazroo, Zaninotto, and Gjonca, 2008). The few longitudinal studies of multimorbidity showed associations with low education, low household income, difficulties managing on income and total household wealth (Jackson et al., 2015; Mounce et al., 2018; Schäfer et al., 2012). Lower level of education, manual occupation and poor social network predicted higher number of functional limitations in the Swedish population older than 60 years of age (Calderón-Larrañaga et al., 2018). Subjective social status (SSS) has been referred to as a subjective measure of SES as it reflects individual's perceived standing in a social hierarchy and hence can be included in the group of material determinants (Singh-Manoux, Marmot, & Adler, 2005). SSS also reflects perceptions of stress and the sense of social inequality (Charonis et al., 2017). To our knowledge there are no studies of how SSS is related to multimorbidity and only one study examined its association with functional decline (Chen, Covinsky, Cenzer, Adler, & Williams, 2012).

Psycho-social determinants, such as leisure and social activities and social networks and contacts, are increasingly more relevant to older people's idea of healthy ageing (Bowling, 2008; Cosco, Prina, & Perales, 2013). Social networks affect health via pathways such as provision of social support, social influence, social engagement and attachment, and access to resources and goods (Berkman & et al., 2010). Living as a couple, in a family, having a large social network and having a sense of control over one's life were all protective factors reducing the risk of multimorbidity (Marengoni et al., 2011; Melis, Marengoni, Angleman, & Fratiglioni, 2014). Older multimorbid people with a supportive social network have longer survival time compared to those without social support (Olaya et al., 2017). Loneliness has been found positively associated with multimorbidity in England, although the relationship was stronger for people younger than 44 than for people older than 65 (Stickley & Koyanagi, 2018). The stress-buffering hypothesis suggests that social relationships can provide resources that buffer the effect of stress on health (Gellert et al., 2018; Uchino, 2009). The direct effects' model says that social networks can facilitate positive health behaviours and access to health care by providing resources such as material assistance or transportation (Olaya et al., 2017).

Behavioural determinants describe different types of consumption and leisure activities that directly affect health and are, to some extent, subject to individual choice and decision-making (Bartley, 2004). Multimorbidity is associated with levels of physical activity, fruit and alcohol consumption, smoking tobacco and Body Mass Index (Dhalwani et al., 2017). However, while sociology of health has began to describe an interplay between human agency and social structure (Cockerham, 2007), health behaviours are still treated in isolation from other social determinants (Moor, Spallek, & Richter, 2016).

Each type of social determinant can work through any or several of the body systems (Blane, Kelly-Irving, D’Errico, Bartley, & Montgomery, 2013). For instance, occupation can affect respiratory, endocrine or cardiovascular system through toxins at work (Agency for Toxic Substances & Disease Registry, 2018) or nervous system and immune system through stress (Marmot et al., 1991). Smoking tobacco can affect nervous, respiratory, cardiovascular or digestive systems through both inhaled carcinogens and lower self-esteem (Bartley, 2004). These examples illustrate our assumption that the combined long-term impact of material, psycho-social and behavioural determinants should be sufficiently wide to be observable across a range of body systems through our measures of complex multimorbidity and multiple functional limitations.    

2.Material and methods

2.1. Data

The English Longitudinal Study of Ageing (ELSA) is a multidisciplinary panel study of a representative sample of men and women aged 50 years and over living in England. ELSA explores the dynamics between ageing and demographic, socio-economic, psychological and health factors. The study began in 2002 with 12,099 participants and the sample is re-examined every two years. It was replenished at waves 3, 4, 6 and 7 with new participants to maintain the size and representativeness of the study (Steptoe et al., 2013). We used data from the core members. ELSA defines core members as individuals who met the following survey criteria: they fit the age eligibility criteria (aged 50 years or older), took part in the original HSE survey that served as the basis for ELSA, and participated in wave 1 of ELSA or joined later as part of the refreshment samples. The cohabiting partners or household members are not included. Data on psycho-social characteristics come from self-completion interviews and all other data from personal interviews.    

2.2. Dependent variables: measures of health

We used data on 25 physical and mental health conditions that were consistently recorded at each wave (). Respondents were asked whether they still had any of the medically diagnosed conditions or whether they had a new condition. This was coded as a binary variable with the value of 1 meaning the presence of a condition and 0 for its absence. The data were grouped into three categories: individual morbidities, groups representing body systems and functional limitations, a decision based on Verbrugge and Jette's Disablement Process Framework (1994). We decided to enlarge their category ‘functional limitations’ by including instances of impairment (dysfunction and abnormalities in body systems) and disability (difficulty with daily activities).

    

Table 1. Health data used in the analysis.    

1000. Morbidities(25)

1101. High blood pressure, 1102 Angina, 1103 Congested heart failure, 1104 Heart murmur, 1105 Abnormal heart rhythm, 1106 Heart attack, 1107 Diabetes, 1108 Stroke, 1109 Lung disease, 1110 Asthma

1211 Arthritis, 1213 Cancer, 1214 Parkinson's disease, 1215 Dementia, 1216 Alzheimer's disease, 1217 Hallucinations, 1218 Anxiety, 1219 Depression, 1220 Emotional problems

1321 Mood swings, 1322 Glaucoma, 1323 Diabetic eye disease, 1324 Macular degeneration, 1325 Cataracts

2000. Body systems    

2100. Eye disorders(3): 2111. Glaucoma, 2112. Macular degeneration, 2114. Cataracts  

2200. Circulatory disorders(7): 2221. High blood pressure, 2222. Angina, 2223. Heart attack, 2224. Congestive heart failure, 2225. Heart murmur, 2226. Abnormal heart rhythm, 2227. Stroke     

2300. Endocrine, nutritional and metabolic(2): 2331 Diabetic eye disease, 2332. Diabetes

2400. Musculoskeletal and connective system(2): 2441. Osteoporosis, 2442. Arthritis

2500. Respiratory (2): 2551. Lung disease, 2552. Asthma

2600. Neoplasms(1): 2661. Cancers

2700. Nervous disorders(3): 2771. Parkinson's disease, 2772. Alzheimer's disease, 2773. Hallucinations

2800. Mental and behavioural(4): 2881. Anxiety, 2882. Depression, 2883. Emotional problems, 2884. Mood swings

3000. Functional limitations

3100. General mobility(10): 3101 Walking 100 yards, 3102 Sitting for 2hrs, 3103 Getting up from chair, 3104 Climbing several flights of stairs, 3105 Climbing one flight of stairs, 3106 Stooping, kneeling or crouching, 3107 Reaching arms above shoulders, 3108 Pulling or pushing a chair, 3109 Lifting/carrying weights over 10 pounds, 3110 Picking up a 5p coin

3200. Activities of daily living(13) : 3211 Dressing, including putting on shoes and socks, 3212 Walking across a room,  3213 Bathing or showering, 3214 Eating, such as cutting up your food, 3215 Getting in or out of bed, 3216 Using the toilet, including getting up or down, 3217 Using a map to figure out how to get around, 3218 Preparing a hot meal, 3219 Shopping for groceries, 3220 Making telephone calls, 3221 Taking medications, 3222 Doing work around the house or garden, 3223 Managing money (paying bills, track of expenses)    

3300. Symptoms(6):  3324 Difficulty walking 0.25 mile, 3325 Pain in general, 3326 Problems with eyesight, 3327 Problems with hearing, 3328 Balance on level surface, 3329 Dizzy walking on level surface.


Source from: https://doi.org/10.1016/j.ssmph.2019.100413


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